Recent domestic and international socio-political crises have reinvigorated debate about whether colleges should issue statements on issues outside of their academic mission, with more colleges than ever pledging neutrality. Although philosophical arguments for and against the practice are in abundance, no empirical research has looked at how position-taking affects faculty and their behaviors. This research addresses this gap through a survey of tenure-track faculty (N=250) at U.S. R1 universities. Findings indicate a majority of faculty are aware of at least some statements, but a non-negligible number are unaware (22%). Most favor neutrality and have disagreed with some of the positions. Nearly one third report self-censoring around statements, and about half are influenced in professionally engagement with a topic based on their university’s position. Formal sanctions were rare, but faculty expressed concerns about marginalization. Differences in responses based on race and tenured status were found. Non-white and untenured faculty more likely to be constrained in expressing their viewpoints, and more likely to be discouraged from participating in professional activities on a topic if their views were misaligned. Most faculty perceived ideological homogeneity among their academic community. The results point to some chilling effect of statements, worth considering neutrality to counteract.

Building on the non-Markovian gambler’s ruin model with endogenous bias [1], this work integrates ergodicity economics (EE) to shift focus from ensemble averages (e.g., expected utilities) to time-average growth rates, which better capture individual experiences in non-ergodic systems [2; 3; 4]. The extended model computes ergodic growth rates g = (1/τ ) ln(Bτ /B0) for surviving paths, where τ is the stopping time. Simulations (now with 10,000 paths and fixed seed for reproducibility) show that endogenous bias β boosts g (from 0.041 to 0.062 for β = 0 to 0.2), reducing ruin rates but masking fragility through homogenized paths (reduced variance) and persistent tail risks, with bottom 5% growth rates saturating at ∼ −0.012 and conditional value at risk (CVaR at 5%) worsening from -0.019 to -0.025. Gini coefficients rise 18% with β, quantifying increased inequality. In multi-player settings, coarser resolution yields marginally lower g, suggesting an “ergodicity premium” where finer observation enables growth-optimal stopping. Continuous SDE approximations align closely (e.g., g ≈ 0.059 for β = 0.2). This bridges stochastic fragility with EE, highlighting how feedback amplifies inequality and crashes in markets. Results underscore EE’s value: optimism drives growth but fails to restore ergodicity, fattening tails [5; 6].

We extend the gambler's ruin problem by allowing the player's decision to continue to bias the transition probabilities of a symmetric random walk. The model incorporates memory-dependent persistence and optional stopping under concave utility. We derive ruin probabilities, expected utilities, and information premia via recursive transitions and Monte Carlo simulations. Results show heightened fragility: endogenous bias boosts gain-seeking and reduces ruin rates but amplifies tail risks and negative premia by up to 30% for moderate bias. Information premia, derived for multi-player settings with asymmetric observation, yield values of -1 to -4 units, reflecting resolution's impact in biased environments. This connects stochastic processes and behavioral economics, highlighting feedback-driven risks.

An inferential model (IM) is a model describing the construction of provably reliable, data-driven uncertainty quantification and inference about relevant unknowns. IMs and Fisher's fiducial argument have similar objectives, but a fundamental distinction between the two is that the former doesn't require that uncertainty quantification be probabilistic, offering greater flexibility and allowing for a proof of its reliability. Important recent developments have been made thanks in part to newfound connections with the imprecise probability literature, in particular, possibility theory. The brand of possibilistic IMs studied here are straightforward to construct, have very strong frequentist-like reliability properties, and offer fully conditional, Bayesian-like (imprecise) probabilistic reasoning. This paper reviews these key recent developments, describing the new theory, methods, and computational tools. A generalization of the basic possibilistic IM is also presented, making new and unexpected connections with ideas in modern statistics and machine learning, e.g., bootstrap and conformal prediction.

Egyptologist Jan Assmann pioneered the concept of mnemohistory to demonstrate how collective remembrance of past events plays a critical role in preserving or transforming a shared notion of national identity.

In this timely book, Adam Ellwanger adapts the notion of mnemohistory to expose the New York Times’ 1619 Project as a propaganda effort aimed destroying America in order to found a new nation defined by far-left “social justice” ideologies.

As a series of essays, the New York Times’ 1619 Project argued that the true founding of the United States was not in 1776 with the signing of the Declaration of Independence, but rather in 1619, when the first ship of captured Africans arrived on the shore of British North America. Further, the authors asserted that the nation that was later founded by the colonists was designed to advance the interests of white supremacy, and that whatever legitimacy there is to America’s commitment to equality, freedom, and liberty comes from the efforts of black Americans.

Through a close rhetorical analysis of the 1619 Project as mnemohistorical propaganda, Ellwanger shows how narrative inversions and strategic manipulation of memory work as techniques for fomenting a mass political movement aimed at destroying American society. Ultimately, his analysis shows that the 1619 narrative – which explicitly aims to improve the lot of African-Americans – actually locks black identity in the past, preventing the cultural transformation that the project promises.

We investigate socioeconomic variation among Whites in the United States. Using data from the American Community Survey, we explore educational attainment, wages, poverty, affluence, and household income by ancestry groups among non-Hispanic, single-race Whites. The results indicate that persons who identify as White report a wide variety of ethnic ancestries. Some ethnic groups have relatively high socioeconomic attainments while others have low outcomes, including a few ethnic groups with poverty and high-school dropout rates that are similar to Blacks. Some of the socioeconomic variation between White ethnic groups is explained by demographic control variables such as age, nativity, region, and disability, but notable socioeconomic differentials frequently persist. Indeed, some of the between-ethnic differentials are as large as the differentials between Whites overall and Blacks. A great deal of socioeconomic variation within specific ethnic groups is furthermore often evident. This substantial heterogeneity in socioeconomic characteristics is inconsistent with the critical demography paradigm which tends to portray Whites as a homogenous and uniformly advantaged group. Our findings suggest that the demographic heterogeneity of Whites—which includes substantial variation in both ethnic identity and socioeconomic characteristics—is a more realistic assessment in the contemporary era of rising class inequality.

The Gulf coastal plain of Texas preserves a nearly uninterrupted Cretaceous marine record from Aptian to Maastrichtian (Stephenson 1914, 1941). Deposits are often rich in vertebrate remains, particularly actinopterygian fish, selachians, and mosasaurid reptiles (Case and Cappetta 1997; Thurmond 1969). Herein, we describe a jaw section from a large mosasaurid collected from Chambers Creek in Navarro County, Texas, USA.

US presidential elections are of interest and sometimes controversial. There is a need to provide context over time, both for total vote and party vote. Our idea is to standardize the vote by the estimated population, the Voter Participation Rate. The benefit of our method is that election cycles are more comparable over time.

Commentary on Sir Roger Penrose’s paper on the gravitational collapse of stars. His paper was heralded by the Nobel Committee for Physics, who awarded Dr. Penrose a half-share of the 2020 Nobel Prize in Physics.

From just about any point of view, the 2020 US Presidential Election was unusual: a high number of votes recorded, increased mail-in/absentee voting, voting rules changed, etc. Now that the 2024 vote totals are in, we can examine the 2020 voting in relation to the 2016 and 2024 vote counts. The voter participation rate, VPR, the percentage of the population voting, was calculated for the years 2016, 2020, and 2024 by party, Democrat, D, and Republican, R, for each state. Next, the 2020 VPR by party and state was compared to the average VPR for the same party and state by subtracting the averages for the years 2016 and 2024 from the 2020 figure. One would expect that the relative increase, jump or decrease, averaged over the states would be zero, i.e., on average, the VPR for Ds and Rs relative to the 2016 and 2014 elections would be the same. In fact, the average jump in voting (in voter participation rate) was larger for Democrats than for Republicans, 3.09 vs 1.29, a 1.80 advantage for Democrats. A statistical test indicated the difference was beyond chance as an explanation.

There is a vast amount of research on the relationships between psychological variation and political opinion. One finding that has received attention in recent years is the relationship between mental health and left-wing opinions. A number of studies have found that positive aspects of mental health (e.g., life satisfaction) and negative (e.g., depression, anxiety) relate to left-wing ideology, such that left-wing ideology relates to worse mental health and lower positivity. We sought to test to which extent these relationships hold across a wide variety of measures of mental health and political ideology. We studied a sample of fairly representative American adults on Prolific (n = 978) who filled out 76 mental health-related questions, and 41 political ideology related questions. Overall, we found that every aspect of mental health correlated negatively with overall leftism (r’s 18 diagnoses = -0.20, last 2 weeks symptoms = -0.14, MMPI-subset = -0.17, life satisfaction = -0.15, all p’s < .001). Regression models showed that diagnoses were the primary predictor of leftism, as the other measures of psychopathology did not predict leftism once diagnoses were included in the same model. Controlling for age, sex, and race reduced the strength of association by about 25%. Alternatively, one might consider that leftism causes psychopathology. Controlling for self-reported measures and demographics, the diagnosis score predicted leftism (beta = 0.10, p < .001). Measurement invariance testing found only very small amounts of bias and in different directions across items. Thus, the relationship cannot be explained as resulting from biased measurement. Finally, we replicated associations between unnatural hair color, number of piercings, and tattoos, leftism, and mental health diagnoses (r’s about 0.20).

Patents and journal papers play important roles in technological progress. Both provide archival records of innovation; both disseminate new knowledge. Beyond this, however, patents and journal papers also provide valuable credentials for innovators. To examine this in greater detail, we introduce a simple working model of systematic knowledge, and argue that patents and journal papers both make original contributions to the edifice of such knowledge. Although a patent certifies an inventor’s original contribution to knowledge, however, it is not an overarching credential, whereas a refereed journal paper is both an original contribution to knowledge and a demonstration of an investigator’s ancillary skills. Thus patents, although impressive on their own, reach their full value as credentials within a portfolio only when accompanied by other credentials that certify the ancillary skills demonstrated by journal paper authors. We conclude that the patents of inventors who have demonstrated such skills are the equal of journal papers in their value as credentials when an investigator’s portfolio is evaluated holistically rather than element-by-element. Several examples drawn from academia illuminate this interpretation. Along the way, the discussion briefly turns to a number of related aspects, including a comparison of the qualities of peer review versus patent examination, the usefulness of patents’ forward-citation counts as indicators of value, and the contributions of patent attorneys.

Widely established evidence shows that anatomical and physiological sex differences favor males over females in nearly every athletic event, yet a small number of researchers have put forth widely publicized claims of an inherent female advantage in many athletic events. The present study assessed the beliefs of a representative sample of 300 U.S. adults for each of four athletic events (1500-m run, 100-m dash, long jump, and 800-m swim). Participants were asked, “In [this event], the world record for professional women is closest to the world record for males of what age?” The correct answer for each event is age 14, yet, for each event, at least 64% of participants indicated that the women’s record was closest to the record for males of age 18 or older (median = age 20). This stark and pervasive misconception indicates that many people in the U.S. and perhaps elsewhere sharply underestimate the contribution of biology to the male-female performance gap in athletics. By extension, this finding suggests that many people underrate the impact of allowing biological males to compete in female-only sports.

This study explores how situational cues, attitudes (i.e., neosexism), and context (i.e., banking vs. daycare) influence perceptions of sexism, interaction favorability, and subsequent decision making in ambiguous interactions. Results indicate sex composition significantly affects sexism perceptions, especially in male-to-female interactions within a traditionally-male context (i.e., banking). Neosexism introduced a bias toward higher sexism ratings and showed more pronounced effects in the opposite-sex banking scenario but not the daycare scenario. This study extends our understanding of individual and situational factors that affect sexism attributions in ambiguous situations and offers implications for decision making.

I provide commentary on the submitted paper, Do Gradebooks Lean Left?

Harmful language guides are becoming an increasingly common tool used by academic institutions, businesses, and professional organizations such as the American Psychological Association to reduce harmful effects of language, particularly for people from marginalized groups (APA, 2023). These guides provide a list of harmful words or phrases (e.g., pipeline, master bedroom) and alternative phrase (e.g., pathway, primary bedroom) to use in their place. Although these guides are well-intentioned, there is great disagreement as to whether harmful language causes adverse outcomes (Lilienfeld, 2017; Lilienfeld, 2020; Williams, 2020a Williams, 2020b). Thus, the purpose of the current pre-registered studies was to determine (1) how people view harmful language and (2) whether exposure to harmful languages causes adverse outcomes. In Study 1, 616 participants rated 175 harmful or alternative words or phrases. Results indicated that harmful language was viewed less favorably than alternative language; however, the vast majority of harmful language was rated on the favorable side of the scale. In Study 2, 334 participants were randomly assigned to read a short story that included 25 harmful words or phrases or the same story with harmful language replaced by alternative language. After exposure to the experimental or control condition, participants completed surveys measuring their anxiety, affect, and feelings of belonging. Results indicated that there were no differences on any psychological outcomes as a result of being exposed to harmful language. The findings from both studies call into question the concept of harmful language and the utility of harmful language guides.

I am testing to see if I can edit my submission after submitting it to the journal.

Abstract Reviews of treatment outcome studies of medically transitioned young people have raised concerns about the methodological problems of these studies and the treatments they support. These concerns include small sample size, lack of control groups, sample selection bias, lack of long-term follow-up data, and heavy attrition rates at follow up. Despite these limitations, major mental health associations including the American Psychological and American Psychiatric Associations continue to support medical transitioning of gender dysphoric youth. This article raises questions for psychologists (and mental health professionals in general) about the theory, research, and practice behind this diagnosis and such life-changing interventions. For these reasons, it’s important to press for higher quality research, specifically mental health research and re-imagine Gender Affirming Care of trans-identified youth as an actual psychotherapeutic endeavor. This proposed approach involves an empirically supported and broadly-based process of informed consent, and maintains an affirming, client-centered stance that envisions multiple treatment options and pathways to gender dysphoria.

Background: Recent scholarship has identified that factual errors have been common in introductory psychology textbooks. These errors tend to be in the direction of making psychological research appear more consistent than it is, as well as promoting viewpoints consistent with politically progressive ideologies. Some famous experiments in psychology have also seen serious questions raised about their validity.

Objective: Given that these conversations have gone on for about a decade, it is worth considering whether identification of these issues resulted in improved coverage in introductory textbooks.

Method: Textbooks were sampled at two time points…16 textbooks were sampled in 2018, and 18 in 2023.

Results: Results indicated that errors in textbooks have remained common even after this issue had been clearly identified in the published literature.

Conclusions: Misreporting of basic scientific information remains common in introductory textbooks, despite improvements in some areas.

Teaching Implications: Textbook authors should be alert to potential misinformation, particularly related to controversial topics. Introductory psychology teachers may need to be aware than not all information presented in textbooks is true.

Inferential models (IMs) offer prior-free, Bayesian-like posterior degrees of belief designed for statistical inference, which feature a frequentist-like calibration property that ensures reliability of said inferences. The catch is that IMs' degrees of belief are possibilistic rather than probabilistic and, since the familiar Monte Carlo methods approximate probabilistic quantities, there are computational challenges associated with putting this framework into practice. The present paper addresses these challenges by developing a new Monte Carlo method designed specifically to approximate the IM's possibilistic output. The proposal is based on a characterization of the possibilistic IM's credal set, which identifies the "best probabilistic approximation" of the IM as a mixture distribution that can be readily approximated and sampled from. These samples can then be transformed into an approximation of the possibilistic IM. Numerical results are presented highlighting the proposed approximation's accuracy and computational efficiency.

This paper examines Evidence-Based Decision Making (EBDM) within the context of ecological rationality. It contrasts classical rationality, which prioritizes comprehensive and logical evidence utilization, with ecological rationality, which emphasizes practical decision making (DM) under real-world constraints. Our examination underscores the importance of adaptive heuristics, professional judgment, and the integration of experience and expertise in forming intuitive responses. It also examines the limitations of framing intuitive versus analytical thinking as a strict dichotomy and advocates for a balanced approach that considers context and practical constraints. Finally, the paper addresses the potential impacts of motivated reasoning and bias in decision-making. Concluding with practical recommendations, it guides practitioners in applying EBDM in an ecologically rational way, stressing the need to balance an emphasis on classical rationality with professional judgment, expertise, and the specificities of each decision context.

We analyzed a socio-politically diverse sample (N = 1412) of adults who reported experiencing or having experienced same-sex attractions to compare the degree of depression and flourishing between three statuses of sexual orientation change efforts (SOCE): No SOCE (n = 329), Ongoing SOCE (n = 326), and Ended SOCE (n = 757). ANCOVA results controlling for age indicated that the participants with Ongoing SOCE reported greater depression and less flourishing than participants in either the group with No SOCE or the group who had Ended SOCE, who had similar health outcomes, with small effect sizes. A chi-square analysis with a medium effect size indicated that the majority of participants (48.6%) in the Ongoing SOCE group did not identify as LGBQ+ while 62.9% of participants in the Ended SOCE group identified as LGBQ+. Overall, 16% (173/1083) of participants exposed to SOCE reported having developed sufficient other-sex sexual attraction to enjoy other-sex sexual behavior, though 11.7% (127/1083) indicated the ability to enjoy heterosexual sex pre-SOCE. Further, 5.8% (19/329) of participants not exposed to SOCE reported experiencing developing sufficient other-sex sexual attraction to enjoy other-sex sexual behavior. Duration of SOCE was not associated with health outcomes and the number of years elapsed following SOCE was not correlated with health outcomes after controlling for age. We conclude by discussing important limitations and cautions for interpreting our findings, potential clinical implications, the benefits of adversarial collaboration, and recommendations for future research.

Contrary to a popular impression in some leading circles, an argument is offered questioning whether Islam is in fact a “religion of peace,” as it promotes itself to outsiders.  Evidence mustered features Islam’s own occasional revelatory declarations about its true nature, including some culled from prominent secondary data, and behavioral indications of actual beliefs, values, and motivations.  If the analysis is correct, failure to discern the reality could prove mortally dysfunctional for Western societies.  Finally, concerning the interface of method and content (comparable to the epistemology–ontology distinction in philosophy), although coming from a social science measurement perspective, this analytic essay endeavors to serve the most fundamental metaphysical purpose of all: excavation of the truth, even if that truth is unpleasant or unpopular.  

We discuss ten TRACE graphics generated using functions from four CRAN R-packages [genridge, lmridge, ridge or RXshrink] that implement diverse forms of Ridge Regression. The “10-Factor” data of Gorman and Toman (1966) are used to illustrate alternative “shrinkage” paths. “Efficient” shrinkage TRACEs, Obenchain (2022), are shown for not only all 10 X-predictors but also for the “Best Subset” of 7 X-predictors proposed by Gorman and Toman.

Review of Martin et al. (2024)

An ideology currently permeating many U.S. institutions is “Critical Race Theory” (CRT). A practical derivative of CRT is “diversity, equity, and inclusion” (DEI). Broadly defined, CRT/DEI are a set of beliefs espousing that U.S. and Western societies are based on “white supremacy” and that social inequalities are due to racially-designed barriers to oppress non-Whites. These views are often accompanied by a deemphasis on classically liberal values such as free speech and due process. Advocates of CRT/DEI demand that society become more egalitarian, just, and inclusive. However, many ideas and tenets of CRT/DEI are debatable and are contested by critics due to the ideas’ questionable empirical support. The purpose of this study was to examine select personality and attitudinal variables that may predict endorsement of CRT/DEI ideas that we call “radical progressive ideology” (RPI). Based on a sample of university students, three variables emerged significantly correlating with RPI: left-wing authoritarianism, anti-White attitudes, and anti-U.S. attitudes. Findings suggest that those embracing RPI may hold prejudicial views of Whites and of the U.S. as a country and may be desirous of punishing those not sharing their radical progressive ideology. Additional implications are discussed.

Abstract: Although patents and journal papers both contribute to “the edifice of knowledge,” the full value of a patent as an intellectual credential is realized when, and only when, the patent is supplemented by other credentials that certify an inventor’s ancillary skills. Such ancillary skills are those ideally taught in graduate school, and include conducting investigations according to accepted methodology, carrying written arguments logically from premises to conclusions, and relating new contributions to the existing body of knowledge.

In 2020, men’s professional tennis players began facing a 25 second limit between the end of a point and the start of a new one. This rule change constrained players’ behavior, as previously they were allowed unlimited time between points, and is exactly the kind of idiosyncratic shock the Efficient Market Hypothesis predicts could lead to inefficiencies in tennis betting markets. We use easily obtained data from about 11000 men’s professional tennis matches and a straightforward algorithm to show it was possible to experience a 30-percent increase on returns to betting in the time period following the implementation of the rule change. However, as is consistent with the idea of efficient markets, the opportunity to exploit inefficiencies closed, as betting markets wised up to the implications of the rule change. We show markets were slow to understand that returns to younger and taller players increased with the serve clock.

Classical statistical methods have theoretical justification when the sample size is predetermined. In applications, however, it's often the case that sample sizes aren't predetermined; instead, they're often data-dependent. Since those methods designed for static sample sizes aren't reliable when sample sizes are dynamic, there's been recent interest in e-processes and corresponding tests and confidence sets that are anytime valid in the sense that their justification holds up for arbitrary dynamic data-collection plans. But if the investigator has relevant-yet-incomplete prior information about the quantity of interest, then there's an opportunity for efficiency gain, but existing approaches can't accommodate this. The present paper offer a new, regularized e-process framework that features a knowledge-based, imprecise-probabilistic regularization with improved efficiency. A generalized version of Ville's inequality is established, ensuring that inference based on the regularized e-process remains anytime valid in a novel, knowledge-dependent sense. In addition, the proposed regularized e-processes facilitate possibility-theoretic uncertainty quantification with strong frequentist-like calibration properties and other desirable Bayesian-like features: satisfies the likelihood principle, avoids sure-loss, and offers formal decision-making with reliability guarantees.

Background: Voluntary therapeutic interventions to reduce unwanted same-sex sexuality are collectively known as sexual orientation change efforts (SOCE). Currently almost all evidence addressing the contested question whether SOCE is effective or safe consists of anecdotes or very small sample qualitative studies of persons who currently identify as sexual minority and thus by definition failed to change. We conducted this study to examine the efficacy and risk outcomes for a group of SOCE participants unbiased by current sexual orientation.
Methods: We examined a convenience sample of 125 men who had undergone SOCE for homosexual-to-heterosexual change in sexual attraction, identity and behavior, and for positive and negative changes in psychosocial problem domains (depression, suicidality, self-harm, self-esteem, social function, and alcohol or substance abuse). Mean change was assessed by parametric (t-test) and nonparametric (Wilcoxon sign rank test) significance tests.
Results: Exposure to SOCE was associated with significant declines in same-sex attraction (from 5.7 to 4.1 on the Kinsey scale, p <.000), identification (4.8 to 3.6, p < .000), and sexual activity (2.4 to 1.5 on a 4-point scale of frequency, p < .000). From 45% to 69% of SOCE participants achieved at least partial remission of unwanted same-sex sexuality; full remission was achieved by 14% for sexual attraction and identification, and 26% for sexual behavior. Rates were higher among married men, but 4-10% of participants experienced increased same-sex orientation after SOCE. From 0.8% to 4.8% of participants reported marked or severe negative psychosocial change following SOCE, but 12.1% to 61.3% reported marked or severe positive psychosocial change. Net change was significantly positive for all problem domains.

Conclusion: SOCE was perceived as an effective and safe therapeutic practice by this sample of participants. We close by offering a unifying understanding of discrepant findings within this literature and caution against broad generalizations of our results.

Even if a research claim in a scientific study is accepted as true, it may, in fact, be false. There are many contradictory claims in the published science literature, and some end up being taken as true and canonized. These situations can be considered research puzzles. Which of the two opposing claims is correct? Maybe neither is correct. How these puzzles come about and what might be done to see how puzzle parts fit together is an important current question. In this research protocol, we give parts/steps whereby false claims can be identified and hopefully resolved. Claim A or B could be true, or both could be true but under different circumstances.

After positively discussing Professor Lawford-Smith's central argument, I provide some of my own comments on the term 'gender identity', contrasting it with 'sexual orientation'. I argue that, unlike 'sexual orientation', both components of the phrase 'gender identity' have significant ambiguities that make clear empirical research into the nature of 'gender identity' a non-starter. I briefly suggest some ways to resolve the problem.

None

After briefly discussing Lawford-Smith's arguments, I point to seemingly fatal ambiguities in both parts of the term 'gender identity', arguing that, in marked contrast to the 'sexual orientation' concept, the 'gender identity' concept is too confused to be used in a scientific context.

N/A

Review of Lawford-Smith (2024) by J. Michael Bailey

This paper begins with a case-study and then makes a broader argument about how the proper exercise of the intellectual virtues is undermined by failures of character and institutional incentives. Our topic is the rise and spread of trigger warnings as a pedagogical tool. In part I, we define them and explain how they spread. In part II, we review the justifications for trigger warnings. In part III, we review the empirical evidence and show how it undermines these justifications. In part IV, we make a broader argument that draws on Aristotle and MacIntyre. Given that there never was any good evidence that trigger warnings work, why are they so ubiquitous? We argue that their adoption and use is best explained by a lack of prudence which is explained by two other failures. On the one hand, the unwillingness to speak out is due to a failure of character. Pedagogues are not unable to read the evidence, they are unwilling to speak out when doing is costly and requires courage. On the other hand, educational institutions do not favour virtue because professional success is often at odds with the excellence that is internal to teaching. 

Abstract: Two forms of Lotka’s Law appear in the literature. The first, which is the traditional form, holds that the number of authors who publish exactly N first-author papers in primary, technical journals is proportional to 1/N2. The second holds that the number of authors who publish at least N first-author papers is proportional to 1/N, based on pseudo-integration of the first. We show that the two forms are not equivalent, despite assertions claiming that they are. We also show that the median contribution of authors who publish in primary journals is one paper, but that the theoretical average number of papers-per-author is undefined.

The Southern California Children’s Health Study (CHS) was started in 1992 and continues to this time. Currently, it contains three cohorts recruited across 16 southern Californian communities and included in a follow study in adulthood:

• fourth graders (aged ~9-10 years) in 1992-1993 (cohort “C”)
• fourth graders (aged ~9-10 years) in 1995-1996 (cohort “D”)
• kindergarteners and first graders (aged ~5-7 years) in 2002-2003 (cohort “E”)

Beginning in their respective baseline year and continuing until high school graduation, annual or biannual questionnaires collected a range of information, including demographic characteristics, housing characteristics, prevalence and incidence of bronchitic symptoms, report of doctor-diagnosed asthma, family history of asthma, history of smoking in the household, and residential history.

Large Language Models, LLMs, and Gen AI Technology are being used to improve the efficiency of research. LLMs are trained on vast amounts of data, including research papers, so they can provide a window into a research topic. Here a User queries ChatGPT, a LLM, on the nature of a Data Dredge.

Ideologically incongruent authoritarians – liberal right-wing authoritarians and their counterpart conservative left-wing authoritarians – represent an important yet understudied group. What underlies the incongruence displayed by incongruent authoritarians? We present four conceptual frameworks for understanding this question: Psychological Ambivalence, Rigidity of the Right, Religion-Specific Authoritarianism, and Ecological Threat. We examined each of these frameworks using data from 14 studies and over 9,000 participants. Findings offer modest support for all four frameworks, but no framework on its own comprehensively accounts for incongruent authoritarianism. What is clear, however, is that ideologically incongruent authoritarians in the U.S. comprise a meaningful category with predictable differences from both their fellow non-authoritarian ideologues and their counterpart congruent authoritarians. As such, this work advances our current understanding of authoritarianism, provides unique insight into the psychology of incongruent authoritarians, and contributes to the ongoing asymmetry debate in political ideology.

What is the size of the analysis search space of randomly selected studies cited in Expert testimony to courts about air quality−adverse health effects?

Some of the most controversial information in psychology involves genetic or evolutionary explanations for sex differences in educational-vocational outcomes (Clark et al., 2024a). We investigated whether men and women react differently to controversial information about sex differences and whether their reaction depends on who provides the information. In the experiment, college students (n=396) and U.S. middle-aged adults (n=154) reviewed a handout, purportedly provided by either a male or a female professor. The handout stated that (1) women in STEM are no longer discriminated against in hiring and publishing and (2) sex differences in educational-vocational outcomes are better explained by evolved differences between men and women in various personal attributes. We found that college women were less receptive to the information than college men were and wanted to censor it more than men did; also, in both the college student and community adult samples, women were less receptive and more censorious when the messenger was a male professor than when the messenger was a female professor. In both samples, participants who leaned to the left politically and who held stronger belief that words can cause harm reacted with more censoriousness. Our findings imply that the identity of a person presenting controversial scientific information and the receiver’s pre-existing identity and beliefs have the potential to influence how that information will be received.

Here, I present a review of “The Value Gap: How gender, generation, personality, and politics shape the values of American university students.”

Inferential models (IMs) offer provably reliable, data-driven, possibilistic statistical inference. But despite IMs' theoretical and foundational advantages, efficient computation is often a challenge. This paper presents a simple and powerful numerical strategy for approximating the IM's possibility contour, or at least its alpha-cut for a specified alpha. Our proposal starts with the specification a parametric family that, in a certain sense, approximately covers the credal set associated with the IM's possibility measure. Then the parameters of that parametric family are tuned in such a way that the family's 100(1-alpha)% credible set roughly matches the IM contour's alpha-cut. This is reminiscent of the variational approximations now widely used in Bayesian statistics, hence the name variational-like IM approximation.

The false confidence theorem establishes that, for any data-driven, precise-probabilistic method for uncertainty quantification, there exists (both trivial and non-trivial) false hypotheses to which the method tends to assign high confidence. This raises concerns about the reliability of these widely-used methods, and shines promising light on the consonant belief function-based methods that are provably immune to false confidence. But an existence result alone leaves much to be desired. Towards an answer to the title question, I show that, roughly, complements of convex hypotheses are afflicted by false confidence.

The inferential model (IM) framework offers alternatives to the familiar probabilistic (e.g., Bayesian and fiducial) uncertainty quantification in statistical inference. Allowing uncertainty quantification to be imprecise makes exact validity/reliability possible. But is imprecision and exact validity compatible with attainment of statistical efficiency? This paper gives an affirmative answer to this question via a new possibilistic Bernstein--von Mises theorem that parallels a fundamental result in Bayesian inference. Among other things, our result demonstrates that the IM solution is asymptotically efficient in the sense that, asymptotically, its credal set is the smallest that contains the Gaussian distribution with variance equal to the Cramer--Rao lower bound.

This essay argues that although patents and journal papers have equal status as contributions to “the edifice of knowledge,” the full value of a patent as a credential is realized only when the patent is supplemented by other credentials that certify an inventor’s ancillary skills. Such ancillary skills are those ideally taught in graduate school, and include conducting investigations according to accepted methodology, carrying written arguments logically from premises to conclusions, and relating new contributions to the existing body of knowledge.

Abstract: This essay argues that although patents and journal papers have equal status as contributions to “the edifice of knowledge,” the full value of a patent as a credential is realized only when the patent is supplemented by other credentials that certify an inventor’s ancillary skills. Such ancillary skills are those ideally taught in graduate school, and include conducting investigations according to accepted methodology, carrying written arguments logically from premises to conclusions, and relating new contributions to the existing body of knowledge.

This paper aims to answer why elite universities tend to score worse on free speech. Using ranking scores that claim to assess universities’ amenability to free speech, it is shown that in the United States, the correlation between university prestige and free speech can be accounted for by the behaviour of handful of institutions, plus variables that pertain to the prevalent academic culture as well as funding sources. Universities with a greater predominance of social science as well as less recipient of federal funding are less supportive of free speech. It is concluded that both academic culture and greater freedom from government do much to explain why elite American universities tend to be less supportive of free speech.

Ethnic differences in self-assessed intelligence (SAI) were examined in two studies. In Study 1 no differences in SAI were found. In Study 2 significant differences in SAI were found with Blacks rating themselves higher in intelligence than Whites, Asians, and Hispanics. Whites rated themselves higher than Hispanics. When scores on a measure of cognitive ability were taken into account it was found that Whites underestimated and Blacks overestimated their ability in comparison to each other and Asians and Hispanics. The results contradict previous findings of higher White SAI and are more consistent with ethnic differences in self-esteem. Future research could examine the possible role of self-esteem in ethnic differences in SAI and further disaggregate ethnic group categorization.

Men outnumber women in many high-status, high-tech fields, e.g., Science, Technology, Engineering, and Mathematics (STEM) and medical professorships. It is often assumed that men and women are equal in all relevant aspects of ability and interest, so blame has been placed on implicit (subconscious) bias for these observed differences. Implicit bias is measured using the gender Implicit Association Test, gIAT. Since measured gIAT is reportedly high, it has been assumed that implicit bias is an important factor in gender difference. We plan to evaluate this current paradigm.

1. How reliable is the gIAT?
2. Does the gIAT correlate well with explicit measures of gender difference and real-world gender actions?
3. How much gender difference variance is accounted for by the gIAT?

This study explores the cryptic Voynich Manuscript, by looking for subtle signs of scribal intent hidden in overlooked features of the “Voynichese” script. The findings indicate that distributions of tokens within paragraphs vary significantly based on positions defined not only by elements intrinsic to the script such as paragraph and line boundaries but also by extrinsic elements, namely the hand-drawn illustrations of plants.

A common goal in statistics and machine learning is estimation of unknowns. Point estimates alone are of little value without an accompanying measure of uncertainty, but traditional uncertainty quantification methods, such as confidence sets and p-values, often require strong distributional or structural assumptions that may not be justified in modern problems. The present paper considers a very common case in machine learning, where the quantity of interest is the minimizer of a given risk (expected loss) function. For such cases, we propose a generalized universal procedure for inference on risk minimizers that features a finite-sample, frequentist validity property under mild distributional assumptions. One version of the proposed procedure is shown to be anytime-valid in the sense that it maintains validity properties regardless of the stopping rule used for the data collection process. We show how this anytime-validity property offers protection against certain factors contributing to the replication crisis in science.

The first author of this article is the mother of an ROGD (Rapid Onset Gender Dysphoria) youth, who is writing under a pseudonym, and the second author is an expert on transgenderism, homosexuality, and behavioral genetics, having written major works on the subject, such as The Man Who Would Be Queen (Bailey, 2003). This is important to mention because we presume that the first author has intimate knowledge of the nature of the current wave of transgenderism, whereas the second author has in-depth scientific knowledge of the characteristics of transgenderism in the past. Aside from establishing the credentials of both authors, which appear to constitute an optimal combination for addressing the alternative hypotheses proposed, we mention this to frame the basic problem posed by this paper. That question is whether the transgenderism of the current wave of gender dysphoria is identical to what has been traditionally known as such in the past (Hypothesis 1) or whether it is a novel phenomenon that the present paper designates as ROGD (Hypothesis 2).

Hypothesis 1 presupposes that the recent increase in gender dysphoria is more apparent than real, and produced by a greater societal openness to atypical sexuality that has disinhibited the reporting of sexual orientations that were previously suppressed by widespread social disapproval. Hypothesis 2 instead proposes that the recent increase is instead quite real, rather than attributable to reporting biases, but that the current wave of gender dysphoria is quite different from conventional transgenderism, and stems from an entirely different set of aetiologies.

In describing Hypothesis 2, the authors consider the role of psychopathology, an often ignored predictor, in developing ROGD. The study's results strongly suggest that a sizeable proportion of individuals experienced a psychopathological condition before the onset of ROGD. In particular, anxiety and depression were quite prevalent in the sample. According to the authors, mental health issues crystallized at 10.5 years of age, preceding ROGD by more than three years. The authors also considered the contribution of social influence, concluding that having a transgendered friend increased the likelihood of social transition, which is adopting formal measures to live as the opposite gender. These results, in conjunction with the reported cooccurrence of ROGD in close friends or acquaintances of the individual experiencing ROGD, suggest emotional contagion as part of an underlying affective disorder could operate as a risk factor for the consolidation of ROGD. Hence, the article provides a novel and unique understanding of social influences and mental health issues leading to the persistence of the condition. The article's results offer future avenues concerning the role of premorbid individual attributes, including mental health issues and the nature of social interactions increasing the risk of ROGD.

The data is almost completely descriptive rather than inferentially based on theory-driven tests derived from the stated alternative hypotheses. Nevertheless, the descriptive data does seem to support Hypothesis 2 in that the profile presented for ROGD does not look very much like that of conventional gender dysphoria, especially in the seemingly large role of social media as well as the developmentally late and sudden onset of the condition. However, only suspected ROGD cases were surveyed so there is no comparison group available, such as a sampling of individuals that had been chronically gender dysphoric from an early age or individuals that were not gender dysphoric at all. To be fair, these limitations are acknowledged in the Discussion as inherent in the way the data were collected. On the other hand, it would have been helpful for the authors to report any comparable data that might be available from non-ROGD cases, although formal significance testing might not be valid due to sampling differences between these data and previous findings. Table 9 comes close to what might be helpful, but only goes as far back as 2020. We are sure Professor Bailey should have some comparable statistics from his previous studies.

A number of recent thinkers on technology have described the ways in which technological change often proceeds apace, in spite of the varied efforts that human beings have made to check its advance. This essay explores how these circumstances have undermined human agency in the political arena. Through a close analysis of the work of major critics of technology such as Langdon Winner, Jacques Ellul, Theodore Kaczynski, Lewis Mumford, and others, I demonstrate how technological change has reached a point where every major contingent of the liberal democratic political order has been neutralized in its ability to direct the course of future advances. After describing how the masses, the elites, and the experts have each been rendered powerless, I explore the implications of this situation for the practice of politics in human society.

Primary-source documents archived by the Smithsonian’s National Museum of American History and the U.S. Patent Office refute the widely accepted legend that actress Hedy Lamarr and musician George Antheil invented frequency hopping spread spectrum (FHSS) communication. Particular attention is called to the prosecution history of the seventh claim of their original patent application, which claim could well serve as the definition of FHSS. Claim 7 was properly denied by the patent office based on prior art. The six allowed claims of US patent 2,292,387 describe Lamarr and Antheil’s actual invention – a player-piano-like synchronization mechanism, not FHSS itself

In spite of referring to the human tendency to "breath together" or share the same spirit, the word "conspire" has developed a negative connotation in contemporary society, specifically as it pertains to theorizing about conspiracies as a result of the human proclivity to recognize patterns recognition and coalesce common themes amongst those with shared perceptions into something resembling a unified narrative. This proclivity has only become more pronounced with the dawn of the Internet age, and as a result, the tendency to assume the actuality of certain conspiracies and insulate ourselves from viable, competing ideas has led to a series of microenvironments not dissimilar from those that allow for the proliferation of cancerous cells in the human body. In this article, I draw out the analogy between cancer and conspiracy theorizing in order to present readers with a clearer picture of the deleterious effects of the contemporary phenomena such as unbridled political polarization and the effect of sociopolitical news coverage presented by low-correlation outlets, otherwise known as "echo chambers."

That science and other domains are now largely data-driven means virtually unlimited opportunities for statisticians. With great power comes responsibility, so it's imperative that statisticians ensure that the methods being developing to solve these problems are reliable. But reliable in what sense? This question is problematic because different notions of reliability correspond to distinct statistical schools of thought, each with their own philosophy and methodology, often giving different answers in applications. To achieve the goal of reliably solving modern problems, I argue that a balance in the behavioral--statistical priorities is needed. Towards this, I make use of Fisher's "underworld of probability" to motivate a new property called invulnerability that, roughly, requires the statistician to avoid the risk of losing money in a long-run sense. Then I go on to make connections between invulnerability and the more familiar behaviorally- and statistically-motivated notions, namely coherence and (frequentist-style) validity.

Comments on Winegard et al.'s article on bias among liberals and the role of the value of equalitarianism.

During the past decade there has been a dramatic increase in adolescents and young adults (AYAs) complaining of gender dysphoria. One influential if controversial explanation is that the increase reflects a socially contagious syndrome among emotionally vulnerable youth: rapid-onset gender dysphoria (ROGD). We report results from a survey of parents who contacted the website ParentsofROGDKids.com because they believed their AYA children had ROGD. Results focused on parent reports on 1,655 AYA children whose gender dysphoria began between ages 11 and 21 years, inclusive. These youths were disproportionately (75%) natal female. Natal males had later onset (by 1.9 years) than females, and they were much less likely to have taken steps towards social gender transition (65.7% for females versus 28.6% for males). Pre-existing mental health issues were common, and youths with these issues were more likely than those without them to have socially and medically transitioned. Parents reported that they had often felt pressured by clinicians to affirm their AYA child’s new gender and support their transition. According to the parents, AYA children’s mental health deteriorated considerably after social transition. We discuss potential biases of survey responses from this sample and conclude that there is presently no reason to believe that reports of parents who support gender transition are more accurate than those who oppose transition. To resolve controversies regarding ROGD, it is desirable that future research include data provided by both pro-transition and anti-transition parents, as well as their gender dysphoric AYA children.

Author's Notes: This is a slightly revised version of a paper that was accepted then retracted at Archives of Sexual Behavior. It was retracted in response to an academic outrage mob offended at its findings, and the journal presented a dubious technicality for retracting it that, as far as we know, has never been applied to any other paper there. JOIBS’ policy is to retract papers only when they meet Committee on Publication Ethics for retraction, which involve data fraud or massive data error. This paper has not even been accused of either data fraud or data error, and JOIBS is delighted to have published it here.

The persistence of race-based income disparities in the United States, particularly between Black and White populations, has been a significant focus of academic research. This study aims to investigate the extent to which differences in average intelligence test performance can account for these income gaps, rather than labour market discrimination or other factors. Utilising five large, nationally representative US datasets, we employ structural equation modelling to adjust our intelligence measures for random measurement error, addressing underestimation of group differences in average intelligence. Our findings reveal that, with the exception of the NLSY97 dataset, performance on intelligence tests mediates the entire income disparity between Black and White individuals when personal income is considered. However, racial gaps in household income persist, indicating that race may influence spousal income and marital choices. This study highlights the importance of accounting for intelligence differences in understanding race-based income disparities.

Past research suggests that national stereotypes are largely inaccurate, unlike other demographic stereotypes. However, past research relied mainly on self-reported personality as the truth set (criterion data), which is problematic due to the reference group effect. Indeed, when an objective measure of conscientiousness was constructed using the measures such as the accuracy of public clocks, this correlated well with national stereotypes (r’s .60 to .70). No prior measurements of national stereotypes of intelligence had been reported in the literature. We surveyed a nationally representative sample of 478 Americans on Prolific to assess the stereotypes about national intelligence, and related these to estimates of national intelligence. We found that stereotypes about national IQs correlated highly with national IQ data (r = .88).

At the individual level in bivariate analysis, leftism (r = -.21, p < .001), neoconservatism (r = -0.24, p < .001), being Black (d = -0.54, p < .001), and being female (d = -0.44, p < .001) predicted lower accuracy. Crystalized ability was not a robust predictor of accuracy (r = .011, p = .86), though the quality of the measurement of crystalized ability was questionable.

Correlates of the ratings of specific countries were also assessed. Leftists tended to rate the intelligence of Americans, Russians and Israelis lower than right wingers, while they assessed the intelligence of Cuba, Uganda, and Kenya to be higher in comparison to right wingers. Blacks rated the intelligence of African countries higher (d = 1.00, p < .001) than Whites, and leftism was associated with giving higher ratings to African countries (r = .26, p < .001). Some of the analysis in this paper were preregistered.

As Basu (1977) writes, "Eliminating nuisance parameters from a model is universally recognized as a major problem of statistics," but after more than 50 years since Basu wrote these words, the two mainstream schools of thought in statistics have yet to solve the problem. Fortunately, the two mainstream frameworks aren't the only options. This series of papers rigorously develops a new and very general inferential model (IM) framework for imprecise-probabilistic statistical inference that is provably valid and efficient, while simultaneously accommodating incomplete or partial prior information about the relevant unknowns when it's available. The present paper, Part III in the series, tackles the marginal inference problem. Part II showed that, for parametric models, the likelihood function naturally plays a central role and, here, when nuisance parameters are present, the same principles suggest that the profile likelihood is the key player. When the likelihood factors nicely, so that the interest and nuisance parameters are perfectly separated, the valid and efficient profile-based marginal IM solution is immediate. But even when the likelihood doesn't factor nicely, the same profile-based solution remains valid and leads to efficiency gains. This is demonstrated in several examples, including the famous Behrens--Fisher and gamma mean problems, where I claim the proposed IM solution is the best solution available. Remarkably, the same profiling-based construction offers validity guarantees in the prediction and non-parametric inference problems. Finally, I show how a broader view of this new IM construction can handle non-parametric inference on risk minimizers and makes a connection between non-parametric IMs and conformal prediction.

Distinguishing two classes of candidate models is a fundamental and practically important problem in statistical inference. Error rate control is crucial to the logic but, in complex nonparametric settings, such guarantees can be difficult to achieve, especially when the stopping rule that determines the data collection process is not available. In this paper we develop a novel e-process construction that leverages the so-called predictive recursion (PR) algorithm designed to rapidly and recursively fit nonparametric mixture models. The resulting PRe-process affords anytime valid inference uniformly over stopping rules and is shown to be efficient in the sense that it achieves the maximal growth rate under the alternative relative to the mixture model being fit by PR. In the special case of testing for a log-concave density, the PRe-process test is computationally simpler and faster, more stable, and no less efficient compared to a recently proposed anytime valid test.

Previous research indicates that in college samples there is a positive correlation between psychosocial development and economic conservatism. We tested the generality of this relationship with a nationally representative sample of respondents from the United States. The result was instead consistent with an alternative hypothesis that psychosocial development is related to political extremism. To our knowledge, this is the first report of an association between psychosocial development and political orientation.

Many social science researchers are liberals and progressives. Many published research studies also happen to support liberal and progressive narratives. This is even true for published research articles which might be fairly interpreted as insulting of conservatives such as referring to them as racist or unintelligent. Is this a coincidence? In a series of impressive studies, Winegard et al. (2023) demonstrate that political bias influences liberals’ perceptions and that, in the quest for finding equality, liberals assign greater moral worth to minority groups than majority. These findings have important implications for recent revisionist history approaches within education, and potential misinformation spread among youth in schools.

According to the journal scope statement of the Journal of Experimental Psychology: Human Perception and Performance (JEP:HPP), researchers are now required to report demographics and justify their sample compositions. However, we feel that the requirement is indefensible on both conceptual and ethical grounds. Conceptually, the requirement wrongly emphasizes generalizing findings rather than generalizing theories without recognizing the crucial role auxiliary assumptions play in the generalization process. Moreover, it distracts researchers with a focus on theoretically irrelevant measures, fails to distinguish between including demographics as moderators in analyses versus as mere classification percentages, encourages researchers to commit the fallacy of using interindividual summary statistics to draw conclusions at the intraindividual level, and potentially reduces sampling precision. Ethically, the requirement places poor or minority researchers at a disadvantage and has the potential to create unnecessary anxiety for participants. It also pushes European and British researchers to violate the General Data Protection Regulation that operates in Europe and the UK, thereby placing those researchers in an untenable situation.

Many social science researchers are liberals and progressives. Many published research studies also happen to support liberal and progressive narratives. This is even true for published research articles which might be fairly interpreted as insulting of conservatives such as referring to them as racist or unintelligent. Is this a coincidence? In a series of impressive studies, Winegard et al. (2023) demonstrate that political bias influences liberals’ perceptions and that, in the quest for finding equality, liberals assign greater moral worth to minority groups than majority. These findings have important implications for recent revisionist history approaches within education, and potential misinformation spread among youth in schools.

Recent scholarship has challenged the long-held assumption in the social sciences that Conservatives are more biased than Liberals, yet little work deliberately explores domains of liberal bias. Here, we demonstrate that Liberals (some might call them Progressives) are particularly prone to bias about victims’ groups (e.g. women, Black people) and identify a set of beliefs that consistently predict this bias, termed Equalitarianism. Equalitarianism, we believe, stems from an aversion to inequality and a desire to protect relatively low status groups, and includes three interrelated beliefs: (1) demographic groups do not differ biologically; (2) prejudice is ubiquitous and explains existing group disparities; (3) society can, and should, make all groups equal in society. This leads to bias against information that portrays a perceived privileged group more favorably than a perceived victims’ group. Eight studies and twelve mini meta-analyses (n=3,274) support this theory. Liberalism was associated with perceiving certain groups as victims (Studies 1a-1b). In Studies 2-7 and meta-analyses, Liberals evaluated the same study as less credible when the results portrayed a privileged group (men and White people) more favorably than a victims’ group (women and Black people) than vice versa. Ruling out alternative explanations of normative reasoning, significant order effects in within-subjects designs in Study 6 and Study 7 (preregistered) suggest that Liberals believe they should not evaluate identical information differently depending on which group is portrayed more favorably, yet do so. In all studies, higher equalitarianism mediated the relationship between liberalism and lower credibility ratings when privileged groups were portrayed more favorably. Although not predicted a priori, meta-analyses also revealed Moderates to be the most balanced in their judgments. These findings do not indicate whether this bias is morally justifiable, only that it exists.

Previous research indicates that in college samples there is a positive correlation between psychosocial development and economic conservatism. We tested the generality of this relationship with a nationally representative sample of respondents from the United States. The result was instead consistent with an alternative hypothesis that psychosocial development is related to political extremism. To our knowledge, this is the first report of an association between psychosocial development and political orientation.

Recent scholarship has challenged the long-held assumption in the social sciences that Conservatives are more biased than Liberals, yet little work deliberately explores domains of liberal bias. Here, we demonstrate that Liberals (some might call them Progressives) are particularly prone to bias about victims’ groups (e.g. women, Black people) and identify a set of beliefs that consistently predict this bias, termed Equalitarianism. Equalitarianism, we believe, stems from an aversion to inequality and a desire to protect relatively low status groups, and includes three interrelated beliefs: (1) demographic groups do not differ biologically; (2) prejudice is ubiquitous and explains existing group disparities; (3) society can, and should, make all groups equal in society. This leads to bias against information that portrays a perceived privileged group more favorably than a perceived victims’ group. Eight studies and twelve mini meta-analyses (n=3,274) support this theory. Liberalism was associated with perceiving certain groups as victims (Studies 1a-1b). In Studies 2-7 and meta-analyses, Liberals evaluated the same study as less credible when the results portrayed a privileged group (men and White people) more favorably than a victims’ group (women and Black people) than vice versa. Ruling out alternative explanations of normative reasoning, significant order effects in within-subjects designs in Study 6 and Study 7 (preregistered) suggest that Liberals believe they should not evaluate identical information differently depending on which group is portrayed more favorably, yet do so. In all studies, higher equalitarianism mediated the relationship between liberalism and lower credibility ratings when privileged groups were portrayed more favorably. Although not predicted a priori, meta-analyses also revealed Moderates to be the most balanced in their judgments. These findings do not indicate whether this bias is morally justifiable, only that it exists.

In the last few years, many countries have introduced (or are proposing to introduce) legislation on ‘conversion therapy’, prohibiting attempts to change or suppress sexual orientation and/or gender identity. This legislation covers ‘aversion therapy’, a form of torture that has already been criminalized in most progressive countries, and also ‘talk therapy’, involving things like counselling, psychoanalysis, and prayer. Focusing on this latter category of practices, I explain what is at stake in the fact that sexual orientation and gender identity have been paired for the purposes of this legislation. I use a particular law reform institute’s approach to this legislation as a case study, and review their literature review in mind to discovering whether they provided sufficient empirical justification for including gender identity in their conversion therapy legislation. I conclude that they did not, and suggest that the pairing of sexual orientation and gender identity may be purely political.

Motivated by the need for the development of safe and reliable methods for uncertainty quantification in machine learning, I propose and develop ideas for a model-free statistical framework for imprecise probabilistic prediction inference. This framework facilitates uncertainty quantification in the form of prediction sets that offer finite sample control of type 1 errors, a property shared with conformal prediction sets, but this new approach also offers more versatile tools for imprecise probabilistic reasoning. Furthermore, I propose and consider the theoretical and empirical properties of a precise probabilistic approximation to the model-free imprecise framework. Approximating a belief/plausibility measure pair by an [optimal in some sense] probability measure in the credal set is a critical resolution needed for the broader adoption of imprecise probabilistic approaches to inference in statistical and machine learning communities. It is largely undetermined in the statistical and machine learning literatures, more generally, how to properly quantify uncertainty in that there is no generally accepted standard of accountability of stated uncertainties. The research I present in this manuscript is aimed at motivating a framework for statistical inference with reliability and accountability as the guiding principles.

The Implicit Association Test, IAT, came into being in the late 1990s, Greenwald et al. 1998. It is a visual and speed reaction test where the subject associates words and pictures. It purports to measure unconscious bias of some sort, e.g. race, gender, obesity, social status, etc. IAT has become one of the most popular subjects of psychology research. A Google Scholar search on 19June2023 found over 40,000 papers with the words “Implicit Association Test” in the paper. Several researchers have called into question the entire IAT scientific enterprise. There are two points of view. On one side, in favor of the IAT, are the originators of the test, Drs. Anthony Greenwald and Mahzarin Banaji, Greenwald et al. 2009. On the other side, calling the test into question, are Drs Philip E. Tetlock, Gregory Mitchell, Oswald et al. 2013, and Ulrich Schimmack 2019.

The cause of autism, termed by Eugen Bleuler in 1911, has not been elucidated to this day. A scientific and logical approach can be utilized to eliminate potential causes and consider ideas not thought of before. Therefore, it is proposed that literary creativity is in fact the causative agent of autism and that a biological cause of autism will never be found. This notion is substantiated through its very unscientific early history and verified with observations made by both Leo Kanner and Hans Asperger. Without a mechanism of pathology, it is suggested that perception plays a major role in both the dissemination and the cure of autism. The Chi-square test of independence of statistics was used for hypothesis testing to show the low probability that pathology is likely to be found in the word “egocentricity,” a term described to be one of the earliest precursors of autism. Therefore, due to the history of autism, autism is best defined as only a redefinition of the word “egocentricity.”

During the past decade there has been a dramatic increase in adolescents and young adults (AYA) complaining of gender dysphoria. One influential if controversial explanation is that the increase reflects a socially contagious syndrome: Rapid Onset Gender Dysphoria (ROGD). We report results from a survey of parents who contacted the website ParentsofROGDKids.com because they believed their AYA children had ROGD. Results focused on 1,655 AYA children whose gender dysphoria reportedly began between ages 11 and 21 years, inclusive. These youths were disproportionately (75%) natal female. Natal males had later onset (by 1.9 years) than females, and they were much less likely to have taken steps towards social gender transition (65.7% for females versus 28.6% for males). Pre-existing mental health issues were common, and youths with these issues were more likely than those without them to have socially and medically transitioned. Parents reported that they had often felt pressured by clinicians to affirm their AYA child’s new gender and support their transition. According to the parents, AYA children’s mental health deteriorated considerably after social transition. We discuss potential biases of survey responses from this sample and conclude that there is presently no reason to believe that reports of parents who support gender transition are more accurate than those who oppose transition. To resolve controversies regarding ROGD, it is desirable that future research include data provided by both pro- and anti-transition parents, as well as their gender dysphoric AYA children.

Authors' Note: This is a slightly revised version of a paper that was accepted then retracted at Archives of Sexual Behavior. It was retracted in response to an academic outrage mob offended at its findings, and the journal presented a dubious technicality for retracting it that, as far as we know, has never been applied to any other paper there. JOIBS’ policy is to retract papers only when they meet Committee on Publication Ethics for retraction, which involve data fraud or massive data error. This paper has not even been accused of either data fraud or data error, and JOIBS is delighted to have published it here.

This brief paper comments on the Bernstein et al. studies on "Tribalism in American Politics." Although the studies have some significant limitations, they provide a tantalizing window into the possibility that the state of affairs with respect to partisan bias may not be as sanguine as a recent meta-analysis suggests - that liberals and conservatives are equally biased against the other. On the contrary, until relatively recently, when researchers are now starting to challenge the received wisdom on the subject, the social and political psychology project has painted a relatively negative psychological portrait of conservatives and a positive one of liberals. By now, these portraits are well familiar to most psychologists and even much of the general public. Conservatives are more authoritarian, less intelligent, and more closed minded, among other things. Liberals are more enlightened, more flexible, and more open minded. But is this narrative correct? Could it be that liberals are more biased and less open-minded than conservatives, as the Bernstein et al. findings suggest, at least under some (perhaps even many) circumstances? The answer to this question, with its likely complexities, awaits further research.

The contribution of Bernstein et al reports a series of studies demonstrating partisan bias: the tendency to evaluate otherwise identical information more favorably when it supports one’s political beliefs or allegiances than when it challenges those beliefs or allegiances. The write-up is clear and concise, and the studies are interesting with a number of nice empirical touches, but the novelty and quality of the data need to be considered, especially the difficulty of ruling out rational counter explanations for data ostensibly showing motivated partisan bias.

Rausch et al. describe an empirical effort to test a number of hypotheses put forward by Lukianoff and Haidt (2018) regarding the values of contemporary (Gen Z) vs. previous generations of undergraduates. My review of their work focuses primarily on the methodologies Rausch et al. employ in executing their research. Strengths of their study include: the empirical replication and extension of prior claims; a priori specification of hypotheses and the methodology to test them; and the insight that prior claims may confound gender and generational status. Weaknesses include: the use of an untested (and unknown) scale to measure their dependent variable; the unfortunate and erroneous classification of majors into “hard” science vs. social science categories; a confound between graduate student status and generational status; and the number and interdependence of the statistical tests they use to test their hypotheses. All of the methodological weaknesses I identify (with the exception of their measurement of academic values) could be strengthened through changes to their procedures and analyses. Overall, their insight that Lukianoff and Haidt’s attribution of value differences to generational status may be confounded with gender is worthy of further research.

Statisticians are largely focused on developing methods that perform well in a frequentist sense---even the Bayesians. But the widely-publicized replication crisis suggests that these performance guarantees alone are not enough to instill confidence in scientific discoveries. In addition to reliably detecting hypotheses that are (in)compatible with data, investigators require methods that can probe for hypotheses that are actually supported by the data. In this paper, we demonstrate that valid inferential models (IMs) achieve both performance and probativeness properties and we offer a powerful new result that ensures the IM's probing is reliable. We also compare and contrast the IM's dual performance and probativeness abilities with that of Deborah Mayo's severe testing framework.

There is a troubling trend in contemporary writing pedagogy to construe classical approaches to writing instruction "as fixed, static entities . . . produced by asymmetrical power relations that . . . reinforce oppressive or stereotypical attitudes and ideologies" (Mutnick and Lamos 25). In place of the classical tradition, progressive educators, following the lead of Paulo Freire, have championed student-centered approaches to education, in effect developing students in the service of themselves as opposed to in the service of knowledge as is characteristic of classical approaches. In this article I argue against the pedagogical monism that characterizes contemporary educational models by positing an integrated model of writing instruction that builds contemporary, theory-driven frameworks on top of historically valid and progressively developed principles, using the languages of modernistic and classical architecture as a mnemonic. Using the "Palladian Arch" as a guiding image, I then close my article by describing how vertically-aligned, foundational approaches such as process pedagogy and genre and rhetorical studies relate to the horizontally-aligned, theoretical approaches that ultimately lead to the apotheosis of each student's intellectual persona.

The nature of "silence" is something of a recurring theme of contemplative philosophies far and wide, but more often than not silence is relegated to being little more than a mere concept or worse, a completely social phenomenon that chalks the matter up as some negation of humanity's "linguistic" way of being. Silence, it would seem, is "nothing" of the sort, but the only way to determine whether or not that is the case would be to contemplate exactly how silence ought to be considered, if, in fact, silence can be considered at all. In this article, I wrestle with the ontological reality of silence using Heidegger's treatment of the Nothing as a waymark, ultimately revealing the interrelatedness of presence and silence as conditions, and opening up possible avenues for new discussions related to meditative and contemplative practice.

Basu's via media is what he referred to as the middle road between the Bayesian and frequentist poles. He seemed skeptical that a suitable via media could be found, but I disagree. My basic claim is that the likelihood alone can't reliably support probabilistic inference, and I justify this by considering a technical trap that Basu stepped in concerning interpretation of the likelihood. While reliable probabilistic inference is out of reach, it turns out that reliable possibilistic inference is not. I lay out my proposed possibility-theoretic solution to Basu's via media and I investigate how the flexibility afforded by my imprecise-probabilistic solution can be leveraged to achieve the likelihood principle (or something close to it).

The first of a pair of linked papers (the other being "A One Parameter Earned Income Tax") which between them describe— in theory at least— a way to bring about a fair and efficient redistribution of income from capital to labor on a scale sufficient to fully compensate for the losses to labor caused by current trade and immigration policies on the one hand, and by automation in the form of the never-ending introduction of new labor-saving technologies on the other.

NA

Fisher's fiducial argument is widely viewed as a failed version of Neyman's theory of confidence limits. But Fisher's goal---Bayesian-like probabilistic uncertainty quantification without priors---was more ambitious than Neyman's, and it's not out of reach. I've recently shown that reliable, prior-free probabilistic uncertainty quantification must be grounded in the theory of imprecise probability, and I've put forward a possibility-theoretic solution that achieves it. This has been met with resistance, however, in part due to statisticians' singular focus on confidence limits. Indeed, if imprecision isn't needed to perform confidence-limit-related tasks, then what's the point? In this paper, for a class of practically useful models, I explain specifically why the fiducial argument gives valid confidence limits, i.e., it's the "best probabilistic approximation" of the possibilistic solution I recently advanced. This sheds new light on what the fiducial argument is doing and on what's lost in terms of reliability when imprecision is ignored and the fiducial argument is pushed for more than just confidence limits.

Greg Lukianoff and Jonathan Haidt, in their book, The Coddling of the American Mind (2018), portrayed current undergraduate American college students (most of whom are in the generation Gen Z: 1995 - 2013) as valuing emotional well-being and the advancement of social justice goals above traditional academic values such as academic freedom and the pursuit of truth. We investigated whether this value discrepancy exists among 574 American university students by exploring the prioritization of five different academic values (academic freedom, advancing knowledge, academic rigor, social justice, and emotional well-being). We also explored how gender, generation, personality, major, and conservatism predict each academic value. Generational differences were present, with Gen Z students emphasizing emotional well-being and de-emphasising academic rigor. Males scored higher on measures of academic freedom and advancing knowledge, while lower on social justice and emotional well-being compared to females. Political conservatism was the strongest predictor for social justice scores, with increased liberal attitudes predicting higher scores on social justice. Emotional stability positively predicted advancing knowledge, while negatively predicting emotional well-being. Agreeableness positively predicted emotional well-being, while negatively predicting advancing knowledge. We ultimately argue that gender is a crucial, underestimated explanatory factor of the value orientations of American college students.

The parameters of a machine learning model are typically learned by minimizing a loss function on a set of training data. However, this can come with the risk of over training; in order for the model to generalize well, it is of great importance that we are able to find the optimal parameter for the model on the entire population—not only on the given training sample. In this paper, we construct valid confidence sets for this optimal parameter of a machine learning model, which can be generated using only the training data without any knowledge of the population. We then show that studying the distribution of this confidence set allows us to assign a notion of confidence to arbitrary regions of the parameter space, and we demonstrate that this distribution can be well-approximated using bootstrapping techniques.

Political tribalism has increased dramatically in recent years. We explored partisan double-standards of Democratic and Republican voters across both hypothetical and real-world scenarios. In Study 1, participants rated the perceived legitimacy of election outcomes in response to hypothetical and ambiguous results from the 2020 presidential election. In Study 2 Part 1, college students and Amazon Turk volunteers rated their support of real-world presidential policies and actions. All policies/actions were attributed to Trump or Obama though they actually occurred under both presidents. In Study 2 Part 2, participants rated how bigoted various statements were; we manipulated who the utterances were attributed to (Trump v. Bill Clinton or Trump v. Martin Luther King [MLK]). Generally, Republican ratings were more favorable when statements were attributed to Trump vs. Democratic leaders while the opposite is true of Democrats. Crucially, these biases exist when evaluating identical information. Republicans and Democrats had a very small and very large tendency, respectively, to view statements as more bigoted under Trump vs. MLK. To the degree that this study can answer the question about which side is more guilty of double-standards, our results provide tentative evidence that this occurs under Democrats more than Republicans, though this overall difference may obscure important moderators. Our data provide evidence for tribal loyalty which may have significant social and political ramifications.

Review of Grawitch et al. (2022)

Political tribalism has increased dramatically in recent years. We explored partisan double-standards of Democratic and Republican voters across both hypothetical and real-world scenarios. In Study 1, participants rated the perceived legitimacy of election outcomes in response to hypothetical and ambiguous results from the 2020 presidential election. In Study 2 Part 1, college students and Amazon Turk volunteers rated their support of real-world presidential policies and actions. All policies/actions were attributed to Trump or Obama though they actually occurred under both presidents. In Study 2 Part 2, participants rated how bigoted various statements were; we manipulated who the utterances were attributed to (Trump v. Bill Clinton or Trump v. Martin Luther King [MLK]). Generally, Republican ratings were more favorable when statements were attributed to Trump vs. Democratic leaders while the opposite is true of Democrats. Crucially, these biases exist when evaluating identical information. Republicans and Democrats had a very small and very large tendency, respectively, to view statements as more bigoted under Trump vs. MLK. To the degree that this study can answer the question about which side is more guilty of double-standards, our results provide tentative evidence that this occurs under Democrats more than Republicans, though this overall difference may obscure important moderators. Our data provide evidence for tribal loyalty which may have significant social and political ramifications.

Building on error management theory and heuristic decision making, we conducted three studies manipulating the sex of the sender and receiver of messages and asked observers to rate the sender’s sexism (Studies 1-3), pleasantness, and professionalism (Studies 2-3). We also examined concern for political correctness (CPC) and social justice attitudes (Study 1), ambivalence toward men (Study 2), and neosexism (Study 3) as moderators of respondent ratings. Across all studies, we found that when the receiver was female, the sender was rated as significantly more sexist, especially when the sender was male. Although CPC, social justice, and ambivalence toward men failed to interact with scenario conditions, neosexism levels resulted in stronger sexism ratings in the male sender-female receiver condition.

Word embeddings are a fundamental tool in natural language processing. Currently, word embedding methods are evaluated on the basis of empirical performance on benchmark data sets, and there is a lack of rigorous understanding of their theoretical properties. This paper studies word embeddings from a statistical theoretical perspective, which is essential for formal inference and uncertainty quantification. We propose a copula-based statistical model for text data and show that under this model, the now-classical Word2Vec method can be interpreted as a statistical estimation method for estimating the theoretical pointwise mutual information (PMI). Next, by building on the work of Levy & Goldberg (2014), we develop a missing value-based estimator as a statistically tractable and interpretable alternative to the Word2Vec approach. The estimation error of this estimator is comparable to Word2Vec and improves upon the truncation-based method proposed by Levy & Goldberg (2014). The proposed estimator also performs comparably to Word2Vec in a benchmark sentiment analysis task on the IMDb Movie Reviews data set.

The Record of the Ontario College of Psychologists vs. Dr. Jordan Peterson is examined and issues related to the practice in the profession of psychology are explored, with special consideration of relevant legislation, disciplinary processes and standards of conduct. Case examples from the public register are presented.

This manuscript is a non-technical response to False dichotomy alert: Improving subjective-probability estimates vs. raising awareness of systemic risk”, by Tetlock, Lu & Mellers (2022).

A fundamental aspect of statistics is the integration of data from different sources. Classically, Fisher and others were focused on how to integrate homogeneous (or only mildly heterogeneous) sets of data. More recently, as data are becoming more accessible, the question of if data sets from different sources should be integrated is becoming more relevant. The current literature treats this as a question with only two answers: integrate or don't. Here we take a different approach, motivated by information-sharing principles coming from the shrinkage estimation literature. In particular, we deviate from the do/don't perspective and propose a dial parameter that controls the extent to which two data sources are integrated. How far this dial parameter should be turned is shown to depend, for example, on the informativeness of the different data sources as measured by Fisher information. In the context of generalized linear models, this more nuanced data integration framework leads to relatively simple parameter estimates and valid tests/confidence intervals. Moreover, we demonstrate both theoretically and empirically that setting the dial parameter according to our recommendation leads to more efficient estimation compared to other binary data integration schemes.

Transfer learning uses a data model, trained to make predictions or inferences on data from one population, to make reliable predictions or inferences on data from another population. Most existing transfer learning approaches are based on fine-tuning pre-trained neural network models, and fail to provide crucial uncertainty quantification. We develop a statistical framework for model predictions based on transfer learning, called RECaST. The primary mechanism is a Cauchy random effect that recalibrates a source model to a target population; we mathematically and empirically demonstrate the validity of our RECaST approach for transfer learning between linear models, in the sense that prediction sets will achieve their nominal stated coverage, and we numerically illustrate the method’s robustness to asymptotic approximations for nonlinear models. Whereas many existing techniques are built on particular source models, RECaST is agnostic to the choice of source model. For example, our RECaST transfer learning approach can be applied to a continuous or discrete data model with linear or logistic regression, deep neural network architectures, etc. Furthermore, RECaST provides uncertainty quantification for predictions, which is mostly absent in the literature. We examine our method’s performance in a simulation study and in an application to real hospital data.

As part of his Industrial/Organizational (I/O) Psychology course, the author engaged students in developing a survey about perceptions of hostile work environments and academic freedom. Students were interested in the extent to which identity and beliefs might predict perceptions and judgments. Survey respondents expressed their perceptions and judgments regarding 20 ecologically valid scenarios. The sample of 120 respondents was broadly representative of the Berea College campus community. Stepwise multiple regressions within a path analytic framework helped develop and refine a general predictive model. Gender and sexual orientation, and their interaction, predicted political identity. Political identity, an activist orientation, and explicit support for hostile environment protection were positively related and predicted over half the variance in respondents’ perception of environmental hostility. These ratings strongly predicted their subsequent judgments of academic freedom protection. Once respondents categorized a situation as being “a hostile environment,” they concluded it would not be protected by academic freedom. A respondent’s explicit academic freedom support added little to the prediction of one’s expressed willingness to protect academic freedom. Although academic freedom may be acknowledged as being important, in practice, the perception of environmental hostility diminishes support for academic freedom. These results have many educational and organizational implications.

Bayesian inference requires specification of a single, precise prior distribution, whereas frequentist inference only accommodates a vacuous prior. Since virtually every real-world application falls somewhere in between these two extremes, a new approach is needed. This series of papers develops a new framework that provides valid and efficient statistical inference, prediction, etc., while accommodating partial prior information and imprecisely-specified models more generally. This paper fleshes out a general inferential model construction that not only yields tests, confidence intervals, etc.~with desirable error rate control guarantees, but also facilitates valid probabilistic reasoning with de~Finetti-style no-sure-loss guarantees. The key technical novelty here is a so-called outer consonant approximation of a general imprecise probability which returns a data- and partial prior-dependent possibility measure to be used for inference and prediction. Despite some potentially unfamiliar imprecise-probabilistic concepts in the development, the result is an intuitive, likelihood-driven framework that will, as expected, agree with the familiar Bayesian and frequentist solutions in the respective extreme cases. More importantly, the proposed framework accommodates partial prior information where available and, therefore, leads to new solutions that were previously out of reach for both Bayesians and frequentists. Details are presented here for a wide range of examples, with more practical details to come in later installments.

Reanalysis of several meta-analysis papers dealing with the effects of lockdowns

Many Systematic Review and Meta-analysis, SRMA, studies based on observational studies are appearing in the literature. There is a need to be able to evaluate the reliability of a SRMA. Starting with the risk ratios and confidence limits, we compute a p-value for each study and examine the distribution of the p-values using a p-value plot. In this paper we examine four SRMAs dealing with the effects of COVID lockdowns on humans.

The probabilistic approach to mutations was initiated in the 1970s by Warren Ewens and others in population biology. It led to the famous Ewens' sampling formula, giving parameterised families of discrete probability distributions on (multiset) partitions, for which the length of a partition turned out to be a sufficient statistic. The current paper takes a fresh (mathematical) look at this area and generalises it from partitions (as certain multisets) to six basic datatypes that are used in mathematics and computer science, such as lists, multisets, subsets, set partitions (covers), multiset partitions, and numbers. These datatypes are organised in a triangular prism diagram, with basic transformations between them. This generalisation builds on (combinatorial) relationships between these datatypes and develops probabilistic mutation operation for all of them. They lead to mutation distributions on each of the different datatypes, via iterated mutations. Moreover, each of the transformations in (a restricted version of) the prism turns out to be a sufficient statistic. The paper thus provides a `multi-datatype' generalisation of the groundbraking work of Ewens on mutations.

Multistate Markov models are a canonical parametric approach for data mod- eling of observed or latent stochastic processes supported on a finite state space. Continuous-time Markov processes describe data that are observed irregularly over time, as is often the case in longitudinal medical and biological data sets, for exam- ple. Assuming that a continuous-time Markov process is time-homogeneous, a closed- form likelihood function can be derived from the Kolmogorov forward equations – a system of differential equations with a well-known matrix-exponential solution. Un- fortunately, however, the forward equations do not admit an analytical solution for continuous-time, time-inhomogeneous Markov processes, and so researchers and prac- titioners often make the simplifying assumption that the process is piecewise time- homogeneous. In this paper, we provide intuitions and illustrations of the potential biases for parameter estimation that may ensue in the more realistic scenario that the piecewise-homogeneous assumption is violated, and we advocate for a solution for likelihood computation in a truly time-inhomogeneous fashion. Particular focus is afforded to the context of multistate Markov models that allow for state label mis- classifications, which applies more broadly to hidden Markov models (HMMs), and Bayesian computations bypass the necessity for computationally demanding numeri- cal gradient approximations for obtaining maximum likelihood estimates (MLEs).

In this paper, I establish the chronology of the longest journey of Jonathan Richardson, the eldest son and collaborator of the English painter and art theorist of the same name, and I examine his itinerary in view of travel restrictions caused by the outbreak of the plague in Southern France in 1720.

Greg Lukianoff and Jonathan Haidt, in their book, The Coddling of the American Mind (2018), portrayed current undergraduate American college students (most of whom are in the generation Gen Z: 1995 - 2013) as valuing emotional well-being and the advancement of social justice goals above traditional academic values such as academic freedom and the pursuit of truth. We investigated whether this value discrepancy exists among 574 American university students by exploring the prioritization of five different academic values (academic freedom, advancing knowledge, academic rigor, social justice, and emotional well-being). We also explored how gender, generation, personality, major, and conservatism predict each academic value. Generational differences were present, with Gen Z students emphasizing emotional well-being and de-emphasising academic rigor. Males scored higher on measures of academic freedom and advancing knowledge, while lower on social justice and emotional well-being compared to females. Political conservatism was the strongest predictor for social justice scores, with increased liberal attitudes predicting higher scores on social justice. Emotional stability positively predicted advancing knowledge, while negatively predicting emotional well-being. Agreeableness positively predicted emotional well-being, while negatively predicting advancing knowledge. We ultimately argue that gender is a crucial, underestimated explanatory factor of the value orientations of American college students.

An overview of the core syntax of Martin-L"of type theory (MLTT) and its semantics in homotopy type theory (HoTT).

Note: This is a chapter in the author's forthcoming book {\em Probability, Intuition, and Common Sense}.

Non-linear logistic, thermal and particle diffusion wave equations describe the harmonic distribution of all Standard Model particles (SM) with mass in relative terms. The combined association of Thermal Diffusion Waves (Thermons) and Particle Diffusion Waves (PDW) is a new hypothesis, validated by experimentally-determined masses of SM particles. The Thermon-PDW mechanism requires a continuous supply of thermal energy, which is obligatory for the existence of particle mass. As well as particle mass, Thermon-PDW interaction dynamics, under steady state, non-linear equilibrium conditions, describe the concomitant appearance of universe time and gravity. The consequences for the universe’s inception and operation is correlated with known universe observations, requiring modifications to the present Model of Cosmology.

By focusing on intrinsic properties of the objects under study---notions of identity (what the objects are), reference (what the concepts refer to), and extension (which objects satisfy a given condition)---traditional logical frameworks have a limited ability to express the many non-mathematical attributes of Logic and Probability. Once instantiated as mathematical objects within a formalism that emphasizes intrinsic properties of the objects rather than extrinsic meaning of the system as a whole, logical and probabilistic concepts become captured by their mathematical representations. Whatever lies beyond the math is lost.

Mathematical representations of logical concepts attain meaning not through what they {\em are} (i.e., frequencies, sizes, prices) but in how they {\em are used} (e.g., for prediction, inference, betting). Axiomatic systems developed within the orthodox quantitative frameworks of probability (see Chapter 4) are useful for applications in which a number (frequency, size, price, or other) adequately represents the primary object of interest.
But sets and numbers can only represent properties that sets and numbers themselves possess, and both logical propositions and probabilities have attributes beyond what these formalizations can encode.

A suitable logic for Intuition and Common Sense, as we seek here, cannot be so constrained that it distorts or eliminates fundamental patterns of intuitive reasoning. Inductive inferences, qualitative probabilistic inferences, context dependence, and generic subjective judgments are inherent to the Intuition yet incompatible with classical logic and orthodox probability theory. That which simply `makes sense' is the gold standard of Intuition, but wholly unacceptable in the traditional formal paradigm. The goal of this chapter is to initiate a theory that does justice to the Intuition and Common Sense, as a sound basis for discerning when something makes sense.

Note: This is a chapter in the author's forthcoming book Probability, Intuition, and Common Sense.

After more than three hundred years of study, the predominant theories of probability have settled on a few core notions.
Formally, probabilities are represented as numbers. Pre-formally, they're interpreted as frequencies, degrees of belief, intermediate truth values, propensities, weights, sizes.
While no single axiomatization is universally accepted, the predominant paradigm consists of theories in which probabilities are represented numerically (usually as values between 0 and 1) and whose primary relationships to one another are determined by the way in which they combine via addition.

In this chapter we catalog a few of these theories, with the goal of understanding how each one agrees with pre-formal (i.e., non-mathematical) intuition about probability. Our objective is a pre-formal meaning explanation for the axioms of each formal system, as opposed to an interpretation of what the specific probabilities are. In addition to shedding new light on formal concepts that are likely already familiar to some readers, this discussion sets the stage for the vastly different formalism of probability introduced in Chapter 5 and developed throughout the rest of the book.

Note: This is a chapter in the author's forthcoming book {\em Probability, Intuition, and Common Sense}.

Schein (1973) is a highly cited article in research on sex and gender biases. The original article concluded that people are biased against women regarding requisite management characteristics. However, the present paper replicates Schein (1973) and demonstrates that the findings were a result of an imbalanced ratio of items which exhibited mean differences between men and women targets. In addition, the use of intraclass correlations creates an illusion of large differences or similarities between targets when the actual mean rating differences are practically trivial and statistically nonsignificant. A bias against women, against men, and no bias are obtained by altering the number of male and female items, or by applying the intraclass correlation to more appropriate data. The implications of the results for the measurement of sex and gender biases are discussed. Broader concerns are raised about ideological biases which allow for conclusions and theories to propagate without empirical support.

Drawing on the existing integrative evidence (e.g., reviews, meta-analyses, theory papers) since 2016, the current review synthesizes the remote work literature and identifies conclusions that can be drawn based on the current evidence. The review spans three remote work clusters: telecommuting, computer-mediated work, and virtual teams. Four major conclusions were identified: (1) remote work tends to be cost-effective for the organization, but the cost-effectiveness can vary based on context; (2) remote work comes with trade-offs for most employees, and not all employees will thrive in remote work settings; (3) degree of virtuality is likely an important moderator, regardless of the outcomes of interest; and (4) there is a lot we do not know about what leads to effective remote work or how to ensure a sufficient likelihood of effectiveness. Sources of current unknowns in the literature are reviewed (e.g., understudied constructs, the absolute importance of key factors), and recommendations for future research and practice are provided.

Inference on the minimum clinically important difference, or MCID, is an important practical problem in medicine. The basic idea is that a treatment being statistically significant may not lead to an improvement in the patients' well-being. The MCID is defined as a threshold such that, if a diagnostic measure exceeds this threshold, then the patients are more likely to notice an improvement. Typical formulations use an underspecified model, which makes a genuine Bayesian solution out of reach. Here, for a challenging personalized MCID problem, where the practically-significant threshold depends on patients' profiles, we develop a novel generalized posterior distribution, based on a working binary quantile regression model, that can be used for estimation and inference. The advantage of this formulation is two-fold: we can theoretically control the bias of the misspecified model and it has a latent variable representation which we can leverage for efficient Gibbs sampling. To ensure that the generalized Bayes inferences achieve a level of frequentist reliability, we propose a variation on the so-called generalized posterior calibration algorithm to suitably tune the spread of our proposed posterior.

Two music critics differ on how to critique Beyonce's album Renaissance. The first critic feels that the album should be critiqued honestly. He feels that the second critic is being dishonest in his evaluation of the album because he doesn't want to appear insensitive. The first critic fails to recognize a conundrum of postmodern criticism based upon identity politics, Post-structuralist criticism or what I label as Representalism judges art not on the conventions of its genre but on how well the artist represents his/her identity group. Therefore, if a critic gives a negative review, the negative review is reflective upon the identity group the artist represents.

Political tribalism has increased dramatically in recent years. We explored partisan double-standards of Democratic and Republican voters across both hypothetical and real-world scenarios. In Study 1, participants rated the perceived legitimacy of election outcomes in response to hypothetical and ambiguous results from the 2020 presidential election. In Study 2 Part 1(and an associated online pilot study), college students rated their support of real-world presidential policies and actions. All policies/actions were attributed to Trump or Obama though they actually occurred under both presidents. In Study2 Part 2, participants rated how bigoted various statements were; we manipulated who the utterances were attributed to (Trump v. Bill Clinton or Trump v. Martin Luther King [MLK]). Generally, Republican ratings are more favorable when attributed toTrump vs. Democratic leaders while the opposite is true of Democrats. Crucially, these biases exist in the context of evaluating identical information. Republicans and Democrats had a very small and very large tendency, respectively, to view statements as more bigoted underTrump vs. MLK. To the degree that this study can answer the question about which side is more guilty of double-standards, our results provide tentative evidence that this occurs under Democrats more than Republicans, though this overall difference may obscure the importance of content as a moderator. Our data suggest people display tribal loyalty which may have significant social and political ramifications.

Two music critics differ on how to critique Beyonce's album Renaissance. The first critic feels that the album should be critiqued honestly. He feels that the second critic is being dishonest in his evaluation of the album because he doesn't want to appear insensitive. The first critic fails to recognize a conundrum of postmodern criticism based upon identity politics, Post-structuralist criticism or what I label as Representalism judges art not on the conventions of its genre but on how well the artist represents his/her identity group. Therefore, if a critic gives a negative review, the negative review is reflective upon the identity group the artist represents.

Many recent studies observe that the religious genre of apocalypse has been adapted to secular purposes. In observing examples of secular apocalypses, researchers typically locate them in contexts that are nevertheless concerned with spiritual and mystical matters. In contrast, this essay demonstrates that secular apocalyptic rhetoric is also utilized in the official discourse of scientific experts and government representatives. Through a close analysis of official documents on the topics of anthropogenic climate change and the COVID-19 pandemic (two urgent issues of public policy that are framed as agents of global cataclysm), I demonstrate the ways that experts appropriate religious forms of rhetoric to persuade audiences to accept the measures proposed by authorities. In the process, the essay identifies the rhetorical features of the secular apocalyptic subgenre in contradistinction to its religious predecessor. Ultimately, this study exposes the ways that secular officials – whose power is justified on the grounds of a purportedly objective, rational, empiricism that is opposed to mysticism – make use of persuasive strategies drawn from traditions that do not conform to the standards of scientific epistemology.

Following the work of Kenneth Burke, most theorists understand modern identity as something that is formed through the processes of identification, recognition, and association. Further, recent work on personal transformation suggests that new identities are legitimized through personal, confessional testimony about the self. In contrast, this essay focuses on “voluntary disappearances” as one example of a type of personal transformation that operates through dissociative practices. Voluntary disappearance is the term given to situations where a person wishes to completely cut ties with all aspects of the present life and achieves this by moving to a new place and assuming a new identity. Through a rhetorical analysis of various books on the topic of how to enact such a disappearance, this study demonstrates two central insights: a) that some modes of ethos formation are achieved by dissociation rather than association, and b) that dissociative personal transformations are achieved through disidentification, non-recognition, concealment and deception.

Recent scholarship has challenged the long-held assumption in the social sciences that Conservatives are more biased than Liberals, yet little work deliberately explores domains of liberal bias. Here, we demonstrate that Liberals (some might call them Progressives) are particularly prone to bias about victims’ groups (e.g. women, Black people) and identify a set of beliefs that consistently predict this bias, termed Equalitarianism. Equalitarianism, we believe, stems from an aversion to inequality and a desire to protect relatively low status groups, and includes three interrelated beliefs: (1) demographic groups do not differ biologically; (2) prejudice is ubiquitous and explains existing group disparities; (3) society can, and should, make all groups equal in society. This leads to bias against information that portrays a perceived privileged group more favorably than a perceived victims’ group. Eight studies and twelve mini meta-analyses (n=3,274) support this theory. Liberalism was associated with perceiving certain groups as victims (Studies 1a-1b). In Studies 2-7 and meta-analyses, Liberals evaluated the same study as less credible when the results portrayed a privileged group (men and White people) more favorably than a victims’ group (women and Black people) than vice versa. Ruling out alternative explanations of normative reasoning, significant order effects in within-subjects designs in Study 6 and Study 7 (preregistered) suggest that Liberals believe they should not evaluate identical information differently depending on which group is portrayed more favorably, yet do so. In all studies, higher equalitarianism mediated the relationship between liberalism and lower credibility ratings when privileged groups were portrayed more favorably. Although not predicted a priori, meta-analyses also revealed Moderates to be the most balanced in their judgments. These findings do not indicate whether this bias is morally justifiable, only that it exists.

Greg Lukianoff and Jonathan Haidt, in their book, The Coddling of the American Mind (2018), portrayed current undergraduate American college students (most of whom are in the generation Generation Z: 1995 - 2013) as valuing emotional well-being and the advancement of social justice goals above traditional academic values such as academic freedom and the pursuit of truth. We investigated whether this value discrepancy exists among 574 American university students by exploring the prioritization of five different academic values (academic freedom, advancing knowledge, academic rigor, social justice, and emotional well-being). We also explored how gender, generation, personality, major, and conservatism predict each academic value. Generational differences were present, with Generation Z students emphasizing emotional well-being and de-emphasising academic rigor compared to older generations. Males scored higher on measures of academic freedom and advancing knowledge, while lower on social justice and emotional well-being compared to females. Political conservatism was the strongest predictor for social justice scores, with increased liberal attitudes predicting higher scores on social justice. Emotional stability positively predicted advancing knowledge, while negatively predicting emotional well-being. Agreeableness positively predicted emotional well-being, while negatively predicting advancing knowledge. We ultimately argue that gender is a crucial, underestimated explanatory factor of the value orientations of American college students.

While considerable quantitative research demonstrates ideological liberalism among American professors, only qualitative work examines whether this affects undergraduate education. Using the Higher Education Research Institute (HERI) dataset surveying students in their first and fourth years in college (n=7,207), we use OLS regressions to test whether students’ political beliefs are associated with reported college grades and perceived collegiate experiences. We find that while standardized test scores are the best predictors of grade point average, ideology also has impacts. Even with controls for SES, demographics, and SAT scores, liberal students report higher college grades and closer relationships with faculty, particularly at elite institutions, with findings driven by social issues like abortion. Nevertheless, conservative students consistently show higher levels of satisfaction with college courses and experiences, and higher high school grades. We discuss implications, and possible limitations.

ABSTRACT Objectives: This review highlights some important problematic issues in hematology laboratory and hematopathology diagnostic process, which are well known to professionals in these areas but are largely ignored for a variety of reasons.

Methods: Microscopic evaluation of peripheral blood smears at Quest Diagnostics; Ameripath Nort East (ANE; part of Quest Diagnostics); Umass Memorial Medical Center (UMMC), Tufts Medical Center (TMC), Beth Israel Deaconess Medical Center (BIDMC). Microscopic evaluation of aspirate smears, bone marrow biopsies and clots, cytospins of lymph nodes and body fluids (including CSF, peritoneal and pleural fluids) and Flow Cytometry testing within laboratories listed above.

Conclusions: While operation of clinical hematology laboratories and laboratory components of hematopathology services is extensively regulated, there is insufficient attention to the quality of materials arriving for diagnostic work up. For example, there is no College of American Pathologist Checklist item dealing with issues of how laboratory establishes stability criteria to assure that a process generating stability criteria addresses reasons for blood cells’ alteration and disruption during transportation. Also, there is little attention to how hematopathology services identify and report suboptimal or inadequate materials in relation to a clinical history prompting evaluations (bone marrow biopsy, aspirate, and flow cytometry). The specific issues relevant to the above and some possible steps for remediation are discussed below in more detail.

Historically, a lack of cross-disciplinary communication has led to the development of statistical methods for detecting exoplanets by astronomers, independent of the contemporary statistical literature. The aim of our paper is to investigate the proper- ties of such methods. Many of these methods (both transit- and radial velocity-based) have not been discussed by statisticians despite their use in thousands of astronomical papers. Transit methods aim to detect a planet by determining whether observations of a star contain a periodic component. These methods tend to be overly rudimentary for starlight data and lack robustness to model misspecification. Conversely, radial velocity methods aim to detect planets by estimating the Doppler shift induced by an orbiting companion on the spectrum of a star. Many such methods are unable to detect Doppler shifts on the order of magnitude consistent with Earth-sized planets around Sun-like stars. Modern radial velocity approaches attempt to address this de- ficiency by adapting tools from contemporary statistical research in functional data analysis, but more work is needed to develop the statistical theory supporting the use of these models, to expand these models for multiplanet systems, and to develop methods for detecting ever smaller Doppler shifts in the presence of stellar activity.

There is no formal difference between particles and black holes. This formal similarity lies in the intersection of gravity and quantum theory; quantum gravity. Motivated by this similarity, 'wave-black hole duality' is proposed, which requires having a proper energy-momentum tensor of spacetime itself. Such a tensor is then found as a consequence of 'principle of minimum gravitational potential'; a principle that corrects the Schwarzschild metric and predicts extra periods in orbits of the planets. In search of the equation that governs changes of observables of spacetime, a novel Hamiltonian dynamics of a Pseudo-Riemannian manifold based on a vector Hamiltonian is adumbrated. The new Hamiltonian dynamics is then seen to be characterized by a new 'tensor bracket' which enables one to finally find the analogue of Heisenberg equation for a 'tensor observable' of spacetime.

This paper considers statistical inference in contexts where only incomplete prior information is available. We develop a practical construction of a suitably valid inferential model (IM) that (a) takes the form of a possibility measure, and (b) depends mainly on the likelihood and partial prior. We also propose a general computational algorithm through which the proposed IM can be evaluated in applications.

A short review of the psychological and financial harms perpetrated by so-called corporate psychopaths, including prevalence in corporate settings, impact, and personality traits associated with white-collar crime.

A key task in the emerging field of materials informatics is to use machine learning to predict a material's properties and functions. A fast and accurate predictive model allows researchers to more efficiently identify or construct a material with desirable properties. As in many fields, deep learning is one of the state-of-the art approaches, but fully training a deep learning model is not always feasible in materials informatics due to limitations on data availability, computational resources, and time. Accordingly, there is a critical need in the application of deep learning to materials informatics problems to develop efficient {\em transfer learning} algorithms. The Bayesian framework is natural for transfer learning because the model trained from the source data can be encoded in the prior distribution for the target task of interest. However, the Bayesian perspective on transfer learning is relatively unaccounted for in the literature, and is complicated for deep learning because the parameter space is large and the interpretations of individual parameters are unclear. Therefore, rather than subjective prior distributions for individual parameters, we propose a new Bayesian transfer learning approach based on the penalized complexity prior on the Kullback–Leibler divergence between the predictive models of the source and target tasks. We show via simulations that the proposed method outperforms other transfer learning methods across a variety of settings. The new method is then applied to a predictive materials science problem where we show improved precision for estimating the band gap of a material based on its structural properties.

Many claims made by researchers often fail to replicate when tested rigorously. In short, these claims can be wrong. There is a need for students (and citizens) to understand how incorrect claims can come about in research by using questionable research practices. Ask a lot of questions of a data set and make a claim if any p-value is less than 0.05 is p-hacking. HARKing is making up a claim/narrative after looking at the data set. Statistical experts know about p-hacking and HARKing, but they appear to be largely silent. Some researchers know too but they ignore the problem. We present a hands-on demo about rolling ten-sided dice multiple times to show how incorrect claims come about. Several individuals executed simulations of p-values with ten-sided dice and show how easily a small p-value can come about by chance for a modest number of questions (rolls of the dice). Notably, small p-values found by one individual were not replicated by other individuals. These simple simulations allow students (and citizens) to better judge the reliability of a science claim when multiple questions are asked of a data set.

Because discrete data with values restricted to integer sets like (1,2,3,4,5) cannot be normally distributed, students often believe that standard means-based methods like $t$-tests cannot be used with such data. This note revisits the use of means-based and rank-based methods for scores from Likert data in 2 and $k>2$ independent samples. The results show that both type methods have good statistical properties such as Type I and II errors, but means are easier to understand and lead to simple confidence intervals with proper coverage. Thus, there should be no statistical justification for avoiding these methods, although one may object on philosophical grounds to the conversion of ordinal Likert responses to integers, and these objections are also briefly addressed.

The key pathogenetic mechanisms of COVID-19 involve tissue damage, inflammation, and thrombosis. Based on pathogenetic mechanisms of this disease, our initial literature search, prior to the first wave of COVID-19, identified well-known, inexpensive, and widely available medications (indomethacin, famotidine and azithromycin), which could target multiple components of pathophysiologic process triggered by SARS-CoV-2. We describe three adult patients managed via telemedicine, discuss details of treatment, including medication dosages, and propose ambulatory implementation of this approach for preventing progression of the disease to a stage requiring hospitalization. We discuss some key goals related to prompt delivery of treatment to adult patients facilitated by telemedicine, aimed at reducing risks of spread of infection, and accelerating treatment initiation.

Inferential models (IMs) are data-dependent, probability-like structures designed to quantify uncertainty about unknowns. As the name suggests, the focus has been on uncertainty quantification for inference, and on establishing a validity property that ensures the IM is reliable in a specific sense. The present paper develops an IM framework for decision problems and, in particular, investigates the decision-theoretic implications of the aforementioned validity property. I show that a valid IM's assessment of an action's quality, defined by a Choquet integral, will not be too optimistic compared to that of an oracle. This ensures that a valid IM tends not to favor actions that the oracle doesn't also favor, hence a valid IM is reliable for decision-making too. In a certain special class of structured statistical models, further connections can be made between the valid IM's favored actions and those favored by other more familiar frameworks, from which certain optimality conclusions can be drawn. An important step in these decision-theoretic developments is a characterization of the valid IM's credal set in terms of confidence distributions, which may be of independent interest.

Existing frameworks for probabilistic inference assume the quantity of interest is the parameter of a posited statistical model. In machine learning applications, however, often there is no statistical model/parameter; the quantity of interest is a statistical functional, a feature of the underlying distribution. Model-based methods can only handle such problems indirectly, via marginalization from a model parameter to the real quantity of interest. Here we develop a generalized inferential model (IM) framework for direct probabilistic uncertainty quantification on the quantity of interest. In particular, we construct a data-dependent, bootstrap-based possibility measure for uncertainty quantification and inference. We then prove that this new approach provides approximately valid inference in the sense that the plausibility values assigned to hypotheses about the unknowns are asymptotically well-calibrated in a frequentist sense. Among other things, this implies that confidence regions for the underlying functional derived from our proposed IM are approximately valid. The method is shown to perform well in key examples, including quantile regression, and in a personalized medicine application.

Prediction, where observed data is used to quantify uncertainty about a future observation, is a fundamental problem in statistics. Prediction sets with coverage probability guarantees are a common solution, but these do not provide probabilistic uncertainty quantification in the sense of assigning beliefs to relevant assertions about the future observable. Alternatively, we recommend the use of a probabilistic predictor, a data-dependent (imprecise) probability distribution for the to-be-predicted observation given the observed data. It is essential that the probabilistic predictor be reliable or valid, and here we offer a notion of validity and explore its behavioral and statistical implications. In particular, we show that valid probabilistic predictors avoid sure loss and lead to prediction procedures with desirable frequentist error rate control properties. We also provide a general inferential model construction that yields a provably valid probabilistic predictor, and we illustrate this construction in regression and classification applications.

A simple macro-enabled spreadsheet to compute the edge of a variety of baccarat wagers and side bets.

A second spreadsheet to compute the edge for the UR Way Egailte side bet.

In this fairly short paper, we present a computational physics model written in the Python programming language that applies Newtonian mechanical principles to predicting the motion of celestial bodies, which served as our entry in the McGill Physics Hackathon (2021). We also demonstrate the predictive validity of our model by invoking a phenomenological approach, where we compare the predictions of our model to empirical observations regarding the motion of celestial bodies.

Modern machine learning algorithms are capable of providing remarkably accurate point-predictions; however, questions remain about their statistical reliability. Unlike conventional machine learning methods, conformal prediction algorithms return confidence sets (i.e., set-valued predictions) that correspond to a given significance level. Moreover, these confidence sets are valid in the sense that they guarantee finite sample control over type 1 error probabilities, allowing the practitioner to choose an acceptable error rate. In our paper, we propose inductive conformal prediction (ICP) algorithms for the tasks of text infilling and part-of-speech (POS) prediction for natural language data. We construct new conformal prediction-enhanced bidirectional encoder representations from transformers (BERT) and bidirectional long short-term memory (BiLSTM) algorithms for POS tagging and a new conformal prediction-enhanced BERT algorithm for text infilling. We analyze the performance of the algorithms in simulations using the Brown Corpus, which contains over 57,000 sentences. Our results demonstrate that the ICP algorithms are able to produce valid set-valued predictions that are small enough to be applicable in real-world applications. We also provide a real data example for how our proposed set-valued predictions can improve machine generated audio transcriptions.

Probability weighting describes a systematic discrepancy between subjective probabilities and the objective probabilities of a given generative process. Since its introduction as part of prospect theory, probability weighting has been considered an irrational cognitive bias --- an error of judgment committed by the decision maker. Recently, theories seeking to provide normative accounts for probability weighting have emerged. These attempt to explain why decision makers should express these discrepancies. One such theory, which we term the mechanistic model, proposes that probability weighting can be explained simply as a decision maker having greater uncertainty about the world than the experimenter. Such uncertainty arises naturally in decisions from experience, where the decision maker is expected to experience rare outcomes less often than the objective probabilities in the experimenter's code prescribe. Arcording to this theory, uncertainty decreases with increased experience, introducing a time-dependency whereby the discrepancy between subjective and objective probabilities narrows with time. This approach contrasts with prospect theory, a descriptive theory, which provides a functional form for describing the probability weighting, and treats the phenomena as a static trait of the decision maker. The two theoretical approaches thus make different experimental predictions. Here we present a Bayesian cognitive model that formalizes the two competing models, incorporating both models into one hierarchical latent mixture model. We explore how the different theories behave when faced with a simple experimental paradigm of decision making from experience. We simulate choice data from synthetic agents behaving in accordance with each theory, showing how this leads to discrepant experimental predictions that diverge as the agents acquire more experience. We show that the same modeling framework when applied to the synthetic choice data, can accurately recover the correct model and that it can recover ground truth parameters, wherever they occur. This work can be taken as preregistration of the formal hypotheses of the two models for a simple experimental paradigm and provides the code necessary for future experimental implementation, model comparison, and parameter inference. Together, this demonstrates the tractability of empirically discriminating between normative and descriptive theories of probability weighting.

This article attempts to explain the "refugees welcome" phenomenon from the evolutionary biology perspective. If there exists a gene variant responsible for sexual violence (in males) then this same variant has to be responsible for feminism (in females), by encoding only one simple emotion (disgust for sexual intercourse) from which all social complications follow naturally (including the "refugees welcome" phenomenon). It is shown that the gene is not required to encode any complex behaviour.

A crucial challenge for solving problems in conflict research is in leveraging the semi-supervised nature of the data that arise. Observed response data such as counts of battle deaths over time indicate latent processes of interest such as intensity and duration of conflicts, but defining and labeling instances of these unobserved processes requires nuance and imprecision. The availability of such labels, however, would make it possible to study the effect of intervention-related predictors --- such as ceasefires --- directly on conflict dynamics (e.g., latent intensity) rather than through an intermediate proxy like observed counts of battle deaths. Motivated by this problem and the new availability of the ETH-PRIO Civil Conflict Ceasefires data set, we propose a Bayesian autoregressive (AR) hidden Markov model (HMM) framework as a sufficiently flexible machine learning approach for semi-supervised regime labeling with uncertainty quantification. We motivate our approach by illustrating the way it can be used to study the role that ceasefires play in shaping conflict dynamics. This ceasefires data set is the first systematic and globally comprehensive data on ceasefires, and our work is the first to analyze this new data and to explore the effect of ceasefires on conflict dynamics in a comprehensive and cross-country manner.

This study suggests that the fairest and most effective means of achieving and sustaining gender parity in corporate leadership roles is to convince corporations to randomly select their leaders from a pool of qualified candidates. Currently, most women rationally prefer traditional gendered career paths because they do not expect the in-groups that hire corporate leaders to select them, even if qualified. In a regime where all qualified candidates have an equal chance of selection, however, more women would prefer career paths preparing them for corporate leadership roles. Because that outcome cannot be demonstrated experimentally, it is shown theoretically by use of a simple relative cost-benefit model and analogically through historical and contemporary real-world applications of the random selection process. The model and analogies all show that random selection can increase the number of qualified female corporate leaders. The paper concludes that random selection could also ensure rational parity for members of all groups, ethnic, racial, trans, and so forth, unduly underrepresented in corporate leadership positions.

In Paris in 1737, the public sale of the countess of Verrue's art collection was documented in manuscript form. That document survives today only in twelve handwritten copies and a 19th century publication. In this work, I examine their properties, establish their interrelationships and sketch their common genealogy.

In mathematical finance, Levy processes are widely used for their ability to model both continuous variation and abrupt, discontinuous jumps. These jumps are practically relevant, so reliable inference on the feature that controls jump frequencies and magnitudes, namely, the Levy density, is of critical importance. A specific obstacle to carrying out model-based (e.g., Bayesian) inference in such problems is that, for general Levy processes, the likelihood is intractable. To overcome this obstacle, here we adopt a Gibbs posterior framework that updates a prior distribution using a suitable loss function instead of a likelihood. We establish asymptotic posterior concentration rates for the proposed Gibbs posterior. In particular, in the most interesting and practically relevant case, we give conditions under which the Gibbs posterior concentrates at (nearly) the minimax optimal rate, adaptive to the unknown smoothness of the true Levy density.

It is widely understood that in order to achieve the 2030 agenda we need to implement evidence-based policymaking, but recently It has started to become clear that it is also key to design coherent policies, and for this, we need to incorporate a complexity approach to take into account SDGs interactions. We construct an interaction (Mutual Information) network using data from SDGs progress in most important metropolitan areas in México. We perform a node ranking analysis and compare the results with a theoretical network. We show that empirical and theoretical networks have a different focus, showing that SDG needs at least different policies for urban and rural areas. We also analyzed the effect of individual (ignoring interactions) SDG achievement using a Bayesian Network. We show that in general monolithic SDG policies translate to a higher probability of low progress. We also made some comments related to the current monolithic general focusing on Poverty fighting by the Mexican government in contrast with what available data suggest would be better: Decent Work and Economic Growth but with a Responsible Consumption and Production. Then investing in education and scientific research in order to advance in Industry, Innovation, and Infrastructure, which requires a strong law and justice procuration system that enables peace, justice, and strong institutions.

Due to the existence of traditional breeding methods (open pollination, mutation breeding, atomic farming) alongside recent technologies (CRISPR/Cas), the line is blurred between genetically modified organisms that require no regulation (as they have been produced by traditional methods), and GMOs (genetically modified organisms as defined by the European Union), which are created by genetic engineering. Although different breeding methods may lead to genetically identical organisms, recent progress in epigenetics and transgenerational epigenetics raises the question of whether plants possess a kind of consciousness that allows them to remember the method by which they have been created. Therefore, we cannot exclude the possibility that genetically identical organisms produced by different breeding methods exhibit different -omics (proteomics, metabolics, RNomics, epigenomics). In this paper, we summarize the recent literature on epigenetics, discuss the transgenerational epigenetic effects of different breeding methods, and emphasize the importance of strictly evaluating possible risks by assessing the final product.

This paper provides an interpretation and analysis of both the Bitcoin black paper, a manuscript authored by Nassim Nicholas Taleb, and the counterarguments provided by McCormack and Alden in their 2021 podcast episode What Bitcoin Did, for the lay reader with some additional commentary.

Vaccine mandates throughout US colleges and workplaces are based on outdated beliefs that the current vaccines will: 1) reduce community spread of the coronavirus, 2) reduce one’s chances of being infected with coronavirus, and 3) reduce likelihood of severe COVID symptoms and death if infected with the coronavirus. However, vaccines do not appear to reduce community spread. They likely offer little, if any, lasting protection against infection, hospitalization and death, despite current misleading media and health department reports. The published clinical trials and real-world studies that support the current vaccines have issues and limitations concerning their efficacy and safety. Emerging evidence suggests vaccination may paradoxically increase the rate of spread and risk of infection, symptom severity and death both during vaccination and also after full vaccine protection has worn off. Given the much lower risks of COVID in younger adults relative to older adults and reduced efficacy of vaccines over time and against new variants, continuing the vaccine mandates may take or impair more lives than save from COVID in these groups. The protective benefits of vaccination do not outweigh the non-trivial risk of death and (small but still alarmingly higher than normal) risk of life-altering injury, especially in younger age groups and in individuals with few occupational and health COVID risk factors. There is a growing global evidence base for alternative COVID prophylactics and therapeutics, as well as new vaccines that could potentially prove safer than those currently on the market. Vaccine mandates should be lifted, and public health policies should be adjusted to better promote personalized medicine, informed consent, and individual choice regarding COVID risk management. Links to online petitions to lift the COVID-19 vaccine college and workplace mandates and vaccine mandates for NYC educators are provided.

Survey with analysis of receptiveness to crowd sourcing and ancillary technologies in the online classroom.

We introduce the PAPER (Preferential Attachment Plus Erdos--Renyi) model for random networks, where we let a random network G be the union of a preferential attachment (PA) tree T and additional Erdos--Renyi (ER) random edges. The PA tree component captures the fact that real world networks often have an underlying growth/recruitment process where vertices and edges are added sequentially, while the ER component can be regarded as random noise. Given only a single snapshot of the final network G, we study the problem of constructing confidence sets for the early history, in particular the root node, of the unobserved growth process; the root node can be patient zero in a disease infection network or the source of fake news in a social media network. We propose an inference algorithm based on Gibbs sampling that scales to networks with millions of nodes and provide theoretical analysis showing that the expected size of the confidence set is small so long as the noise level of the ER edges is not too large. We also propose variations of the model in which multiple growth processes occur simultaneously, reflecting the growth of multiple communities, and we use these models to provide a new approach to community detection.

In this paper, we extend the epsilon admissible subsets (EAS) model selection approach, from its original construction in the high-dimensional linear regression setting, to an EAS framework for performing group variable selection in the high-dimensional multivariate regression setting. Assuming a matrix-Normal linear model we show that the EAS strategy is asymptotically consistent if there exists a sparse, true data generating set of predictors. Nonetheless, our EAS strategy is designed to estimate a posterior-like, generalized fiducial distribution over a parsimonious class of models in the setting of correlated predictors and/or in the absence of a sparsity assumption. The effectiveness of our approach, to this end, is demonstrated empirically in simulation studies, and is compared to other state-of-the-art model/variable selection procedures.

Abstract From 1970 research evidence has accumulated that the Mediterranean diet promotes health and longevity. Its main components include local (wild) green vegetables, Citrus fruits, and olive oil (extra virgin). Since the 1990s, experimental research on phytochemicals to explain why plant food is healthy and promotes longevity has grown exponentially. Nowadays, molecular biology provides deep explanations for many experimentally found health-promoting properties of plant species and their phytochemicals. The specialized approach is OK because it is the way research progresses. Mainly, nutritional researchers concentrate on a particular group of compounds such as flavonoids, phenolic compounds, carboxylic acids, fatty acids, etc. Science outside the research on nutrition deals with the same chemical compounds but which nutritional researchers generally do not follow. Plant biologists have found that all photosynthesizing plants share many compounds and ions. They are vital to plants. Some of the compounds and ions are also vital to humans. Plant biologists make a distinction between minerals, primary metabolites, and secondary metabolites. This distinction applies partly to humans. Plant minerals and primary metabolites often are essential to humans. Plant secondary metabolites are often not vital to humans, but experimental research has shown that they promote health and longevity. Eating local wild edible plants (WEP) also promotes sustainability. Wild edible plants are an ecosystem service. I have found 52 compounds and ions that all green edible plants share, promoting human health, well-being, and longevity. I present the evidence in this paper.

In prediction problems, it is common to model the data-generating process and then use a model-based procedure, such as a Bayesian predictive distribution, to quantify uncertainty about the next observation. However, if the posited model is misspecified, then its predictions may not be calibrated---that is, the predictive distribution's quantiles may not be nominal frequentist prediction upper limits, even asymptotically. Rather than abandoning the comfort of a model-based formulation for a more complicated non-model-based approach, here we propose a strategy in which the data itself helps determine if the assumed model-based solution should be adjusted to account for model misspecification. This is achieved through a generalized Bayes formulation where a learning rate parameter is tuned, via the proposed generalized predictive calibration (GPrC) algorithm, to make the predictive distribution calibrated, even under model misspecification. Extensive numerical experiments are presented, under a variety of settings, demonstrating the proposed GPrC algorithm's validity, efficiency, and robustness.

Between Bayesian and frequentist inference, it's commonly believed that the former is for cases where one has a prior and the latter is for cases where one has no prior. But the prior/no-prior classification isn't exhaustive, and most real-world applications fit somewhere in between these two extremes. That neither of the two dominant schools of thought are suited for these applications creates confusion and slows progress. A key observation here is that ``no prior information'' actually means no prior distribution can be ruled out, so the classically-frequentist context is best characterized as every prior. From this perspective, it's now clear that there's an entire spectrum of contexts depending on what, if any, partial prior information is available, with Bayesian (one prior) and frequentist (every prior) on opposite extremes. This paper ties the two frameworks together by formally treating those cases where only partial prior information is available using the theory of imprecise probability. The end result is a unified framework of (imprecise-probabilistic) statistical inference with a new validity condition that implies both frequentist-style error rate control for derived procedures and Bayesian-style coherence properties, relative to the given partial prior information. This new theory contains both the Bayesian and frequentist frameworks as special cases, since they're both valid in this new sense relative to their respective partial priors. Different constructions of these valid inferential models are considered, and compared based on their efficiency.

This research note describes the novel application of Zero-Knowledge Proofs to conduct data analysis. A zero-knowledge proof is useful to prove you can generate a particular result without revealing certain parts of the process. Using such a proof, in the setting of a crowdsourced dataset testing Seth Roberts’ Appetite Theory, we were able to conduct an independent data analysis of unshared data. The analysis queried the data set to generate both a numerical result and a computational proof which illuminated the dataset without revealing it. We suggest that this protocol could be useful for solving the “other” file drawer problem, where researchers naturally seek to horde and protect their private data until they have extracted maximal value from it. And we further suggest and outline an application in citizen science.

The article accompanies the first ever minted "alpha glyph", Alpha Glyph 1 - Independent Analysis of Seth Roberts Appetite Theory Study (Crane and Martin, 2021). https://zora.co/0xa18edFea684792B10223d68e2920467b7Dd6FE8b/2837. Alpha Glyph 1 was created from our independent analysis of Matt Stephenson’s first open source, scientific randomized control trial of Seth Roberts’s Appetite Theory.

Proceeds from the initial auction of Alpha Glyph 1 will be put toward funding both the replication and the eventual publication of the results of Stephenson’s study. Following Stephenson's incentive protocol, 50% of the proceeds from this auction go to those key contributors on whose work this builds.

Three case studies in the challenges and failures of probabilistic reasoning and common sense in the U.S. response to COVID-19.

Published in COVID-19: A Complex Systems Approach (Edited by Alfredo Morales, Joseph Norman and Mads Bahrami, Published by STEM Academic Press).

An exciting new algorithmic breakthrough has been advanced for how to carry out inferences in a Dempster-Shafer (DS) formulation of a categorical data generating model. The developed sampling mechanism, which draws on theory for directed graphs, is a clever and remarkable achievement, as this has been an open problem for many decades. In this discussion, I comment on important contributions, central questions, and prevailing matters of the article.

Due to their long-standing reputation as excellent off-the-shelf predictors, random forests continue remain a go-to model of choice for applied statisticians and data scientists. Despite their widespread use, however, until recently, little was known about their inner-workings and about which aspects of the procedure were driving their success. Very recently, two competing hypotheses have emerged -- one based on interpolation and the other based on regularization. This work argues in favor of the latter by utilizing the regularization framework to reexamine the decades-old question of whether individual trees in an ensemble ought to be pruned. Despite the fact that default constructions of random forests use near full depth trees in most popular software packages, here we provide strong evidence that tree depth should be seen as a natural form of regularization across the entire procedure. In particular, our work suggests that random forests with shallow trees are advantageous when the signal-to-noise ratio in the data is low. In building up this argument, we also critique the newly popular notion of "double descent" in random forests by drawing parallels to U-statistics and arguing that the noticeable jumps in random forest accuracy are the result of simple averaging rather than interpolation.

In this article, we prove the limit formula \lim_{x \to \infty} \frac{|M(x)|}{\pi(x)} = \lim_{x \to \infty} \frac{h}{log(x)} = 0, h = a constant for Mertens' function M(x) using arithmetic and analytic arguments based on theorems for the prime counting function pi(x) and the series \sum \frac{\mu(k)}{k}. The formula is evaluated using limit theorems to give: an alternative proof of \lim_{x \to \infty} \frac{|M(x)|}{x} = 0, a new disproof of Mertens' conjecture, proof of the Odlyzko--te Riele conjecture and a disproof of the Riemann hypothesis based on Littlewood's equivalence theorem.

Glyphs are a proposed system that comprises non-fungible digital tokens for first manuscripts. Author-produced first manuscripts have been uniquely valuable since the dawn of the printing press \citep{plant1934copyright} and digitally scarce manuscript NFTs can similarly be used to reward thinkers, innovators, and creators. We believe that increased rewards for the time-consuming creation and early discovery of original work is essential in the larger project of improving online knowledge creation and dissemination. Of particular interest is a seemingly global decline in innovation which may be partially alleviated by behavioral and market mechanisms which can reward citizen science, ``tinkering'', and perhaps even basic research.

Most scientific publications follow the familiar recipe of (i) obtain data, (ii) fit a model, and (iii) comment on the scientific relevance of the effects of particular covariates in that model. This approach, however, ignores the fact that there may exist a multitude of similarly-accurate models in which the implied effects of individual covariates may be vastly different. This problem of finding an entire collection of plausible models has also received relatively little attention in the statistics community, with nearly all of the proposed methodologies being narrowly tailored to a particular model class and/or requiring an exhaustive search over all possible models, making them largely infeasible in the current big data era. This work develops the idea of forward stability and proposes a novel, computationally-efficient approach to finding collections of accurate models we refer to as model path selection (MPS). MPS builds up a plausible model collection via a forward selection approach and is entirely agnostic to the model class and loss function employed. The resulting model collection can be displayed in a simple and intuitive graphical fashion, easily allowing practitioners to visualize whether some covariates can be swapped for others with minimal loss.

Financial crime, including fraud, money laundering, theft, bribery, and corruption is prevalent globally, affecting individuals and organizations. Costs of such crimes are very significant, and the psychological impact on victims of financial crime is increasingly well documented. Financial harm is a general term that refers to the impact of the illegal and improper use of an individual's resources through deception or pressure, and these impacts can refer to both psychological harm (including stress-related physical symptoms) as well as impacts on financial health. To date, no validated instrument exists that measures financial harm across broad population groups. This paper outlines the development of the Financial Harm Inventory (FHI), and reports the results of pilot data. The factor structure, reliability, item response analysis, and validity of the FHI are presented and discussed.

One of the most troubling developments of 2021 has been the number of fertile-aged women who have been led to believe that vaccines against SARS-CoV-2 (COVID-19) could cause infertility via cross-reactivity of immune response (specifically cross-reactivity of developed antibodies to Syncytin-1). The evidence does not support this claim.

The aim of this paper is to consider the possibility of understanding gravity through the use of information. In this case information is stored in space-time as finite elements. Next, it is shown that the definition of gravity through the use of finite elements may be combined with a deterministic interpretation of quantum mechanics (bohmian mechanics)

Between the two dominant schools of thought in statistics, namely, Bayesian and classical/frequentist, a main difference is that the former is grounded in the mathematically rigorous theory of probability while the latter is not. In this paper, I show that the latter is grounded in a different but equally mathematically rigorous theory of imprecise probability. Specifically, I show that for every suitable testing or confidence procedure with error rate control guarantees, there exists a consonant plausibility function whose derived testing or confidence procedure is no less efficient. Beyond its foundational implications, this characterization has at least two important practical consequences: first, it simplifies the interpretation of p-values and confidence regions, thus creating opportunities for improved education and scientific communication; second, the constructive proof of the main results leads to a strategy for new and improved methods in challenging inference problems.

For high-dimensional inference problems, statisticians have a number of competing interests. On the one hand, procedures should provide accurate estimation, reliable structure learning, and valid uncertainty quantification. On the other hand, procedures should be computationally efficient and able to scale to very high dimensions. In this note, I show that a very simple data-dependent measure can achieve all of these desirable properties simultaneously, along with some robustness to the error distribution, in sparse sequence models.

While taxation affects everybody who lives in a modern society, existing systems suffer from inconsistencies, operational imperfections, and high running costs. Amounts of tax paid by taxpayers are highly independent of the benefits received in return, which provides great freedom for policymaking. Despite that, no taxation model has emerged yet that would be able to efficiently address the problems mentioned above. In this paper, I show that there is an extremely simple taxation model that could overcome these issues. However, it requires all local (national) currency holdings to be fully recorded live at all times.

An Open Letter to the Communications of the ACM December 29, 2020

Initial Letter with signatures will be sent to CACM.

If you are an established researcher, educator, or professional in computing or an adjacent field and would like to add your name to the signatories of this open letter, please fill out the Google form or email karyeh@cs.bgu.ac.il and lreyzin@uic.edu to be added. Note that signatories will be vetted before being added.

One formulation of forensic identification of source problems is to determine the source of trace evidence, for instance, glass fragments found on a suspect for a crime. The current state of the science is to compute a Bayes factor (BF) comparing the marginal distribution of measurements of trace evidence under two competing propositions for whether or not the unknown source evidence originated from a specific source. The obvious problem with such an approach is the ability to tailor the prior distributions (placed on the features/parameters of the statistical model for the measurements of trace evidence) in favor of the defense or prosecution, which is further complicated by the fact that the typical number of measurements of trace evidence is typically sufficiently small that prior choice/specification has a strong influence on the value of the BF. To remedy this problem of prior specification and choice, we develop an alternative to the BF, within the framework of generalized fiducial inference (GFI), that we term a {\em generalized fiducial factor} (GFF). Furthermore, we demonstrate empirically, on the synthetic and real Netherlands Forensic Institute (NFI) casework data, deficiencies in the BF and classical/frequentist likelihood ratio (LR) approaches.

Si fa una distinzione tra probabilità accademiche, che non hanno alcun aggancio alla realtà e pertanto nessun significato reale, e probabilità reali, che attengono ad un significato reale come odds the il soggetto asserente si vincola ad accettare per una scommessa contro la sua affermazione. Con ciò, si discute come la crisi di replicabilità può essere risolta facilmente imponendo che le probabilità pubblicate nella letteratura scientifica siano reali, invece che accademiche. Ad oggi, tutte le probabilità e le statistiche da esse derivate che appaiono nelle pubblicazioni, come i p-values, i fattori Bayesiani, gli intervalli di confidenza, etc., sono il risultato di probabilità accademiche, che non sono utili per fare asserzioni dotate di senso riguardo il mondo reale.

We propose a market-based scoring (MBS) method for evaluating the performance of probabilistic forecasts. We demonstrate our approach on the 2020 U.S.~elections for President, Senate and House of Representatives by evaluating the forecasts posted on the FiveThirtyEight website based on their performance against the prediction markets at PredictIt.

Our analysis finds that PredictIt and FiveThirtyEight perform comparably based on traditional metrics such as calibration and accuracy. For market-based scoring, we find that if we ignore PredictIt's fees and commissions, then FiveThirtyEight forecasts beat the markets overall; but if we factor in fees and commissions, the markets beat FiveThirtyEight. We discuss implications of this analysis for forecasting future election cycles and for betting market design and operations.

In addition to the analysis presented here, a running tally of results from the above analysis was updated and reported throughout the 2020 campaign at https://pivs538.herokuapp.com/.

Mixture models are commonly used when data show signs of heterogeneity and, often, it is important to estimate the distribution of the latent variable responsible for that heterogeneity. This is a common problem for data taking values in a Euclidean space, but the work on mixing distribution estimation based on directional data taking values on the unit sphere is limited. In this paper, we propose using the predictive recursion (PR) algorithm to solve for a mixture on a sphere. One key feature of PR is its computational efficiency. Moreover, compared to likelihood-based methods that only support finite mixing distribution estimates, PR is able to estimate a smooth mixing density. PR's asymptotic consistency in spherical mixture models is established, and simulation results showcase its benefits compared to existing likelihood-based methods. We also show two real-data examples to illustrate how PR can be used for goodness-of-fit testing and clustering.

The roots of (i) expected value optimization, (ii) client-centric planning, and (iii) growth optimal selection twist-and-turn around one another over a centuries-long research history. This paper traces each strand chronologically. It also highlights their interconnections. Its goal is to connect what practitioners already know with Ergodicity Economics, because it is the next wave of product development and client advice.

We cannot individually experience ensemble averages, thus using Expected Value as the default investment decision criterion has created a large catalog of empirical puzzles, paradoxes, and anomalies. Financial Economics uses mathematics as a language to define the structure of investment problems. Ergodicity Economics (EE) restores mathematics as a method of skepticism to question the structure of economic and investment problems. This change has created a growing catalog of solutions to the traditional list of empirical puzzles, paradoxes, and anomalies.

The formalization of embedding randomness in Time by Ole Peters in 2011, as an alternative to embedding randomness in the Ensemble, is a critical development for the financial industry. At a personal level, it clarified the conceptual meaning of a path through three start-ups over three decades. This path went from expected value portfolio optimization, to client-centric retirement planning, and now growth optimal product selection.

Following EE’s example, the rapidly growing number of research papers becomes more manageable when one starts with the foundational papers. Thus, this narrated conceptual chronology starts each conceptual entry with the earliest research papers available, and lists the matching entries in ascending chronological order.

This narrated conceptual chronology proved to be a helpful exercise to connect the dots between Financial Economics and Ergodicity Economics. It also proved to be a productive platform for the development of additional content: Identifying and rank-ordering the empirical puzzles, paradoxes, and anomalies of Financial Economics that matter the most for practitioners, and that could benefit from solutions based on Ergodicity Economics.

An Excel spreadsheet programmed in VBA is presented that performs certain combinatorial calculations to determine the house or player advantage on a number of common baccarat wagers. The spreadsheet allows the user to input any number of decks and a custom number of cards of each rank. The spreadsheet allows for real-time tracking of the house or player advantage. Additionally, this spread sheet allows for risk analysis on completed shoes by manually inputting the cards played and wagers. Various statistical tests are conducted and flags are presented to indicate possible cheating or advantage play.

Economic growth is measured as the rate of relative change in gross domestic product (GDP) per capita. Yet, when incomes follow random multiplicative growth, the ensemble-average (GDP per capita) growth rate is higher than the time-average growth rate achieved by each individual in the long run. This mathematical fact is the starting point of ergodicity economics. Using the atypically high ensemble-average growth rate as the principal growth measure creates an incomplete picture. Policymaking would be better informed by reporting both ensemble-average and time-average growth rates. We analyse rigorously these growth rates and describe their evolution in the United States and France over the last fifty years. The difference between the two growth rates gives rise to a natural measure of income inequality, equal to the mean logarithmic deviation. Despite being estimated as the average of individual income growth rates, the time-average growth rate is independent of income mobility.

As the size, complexity, and availability of data continues to grow, scientists are increasingly relying upon black-box learning algorithms that can often provide accurate predictions with minimal a priori model specifications. Tools like random forest have an established track record of off-the-shelf success and offer various ad hoc strategies for analyzing the underlying relationships among variables. Motivated by recent insights into random forest behavior, we introduce the idea of augmented bagging (AugBagg), a procedure that operates in an identical fashion to classical bagging and random forests, but which acts on a larger, augmented space containing additional randomly generated noise features. Surprisingly and counterintuitively, we demonstrate that this simple act of including extra noise variables in the model can lead to dramatic improvements in out-of-sample predictive accuracy. As a result, common notions of variable importance based on improved model accuracy may be fatally flawed, as even purely random noise can routinely register as statistically significant. Numerous demonstrations on both real and synthetic data are provided along with a proposed solution.

Introduction Telephone consultations (TC) and video consultations (VC) between physicians and patients are increasingly utilised in replacement of conventional face to face (F2F) consultations. The coronavirus pandemic has created an increase in remote consultations (RC) in healthcare due to imposed isolation to help reduce the spread of the virus. We aim to explore patients’ experience and satisfaction of TC and VC (televidimedicine) in the delivery of healthcare at Wimbledon clinics London.

Methods We analysed patient experience of televidimedicine (TVM) on Elective Orthopaedic patients at Wimbledon clinics. After the lockdown occurred, participating patients had remote consultations over a 2 month period during the COVID-19 pandemic. Telephone, Webex ™, Zoom ™, MS Teams ™ and Skype ™ were utilised. They were emailed questionnaires to give their feedback.

Results Of the 334 questionnaires sent, 152 were suitable. 84 patients had TC and 68 patients had VC. There was a similar distribution in gender and similar mean ages (TC: 59.3 [range 22-88]; VC: 58 [range 15-85]). 46% of patients found TVM acceptable (VC: 48.5%; TC: 44%) with 57% of new patients (VC: 59%; TC; 55%) and 40% of post-operative patients (VC: 44%; TC: 37%) found TVM acceptable. Overall, patients found VC (48.5%) more acceptable than TC (44%). 98% of patients found TVM worthwhile (VC: 100%; TC: 96%). 94% of new patients and 79% of post-operative patients found TVM was worthwhile or very worthwhile. Overall, patients found VC (94%) more worthwhile or very worthwhile than TC (80%).

Conclusion It is well documented that remote healthcare is beneficial and telemedicine is well established for clinical practice and even for surgical procedures. We show that TVM in orthopaedic outpatients at Wimbledon clinics is suitable as a replacement for F2F consultations in the new social landscape created by COVID-19. Both consultation types were acceptable. The utilisation of Zoom, MS Teams, Skype and Webex has become household names during coronavirus pandemic. Further analysis needs to be conducted in order to explore patient safety, the impact on resources and clinician experience of TVM in order to appreciate what is increasingly the new normal.

The most common bets in 19th-century casinos were even-money bets on red or black in Roulette or Trente et Quarante. Many casino gamblers allowed themselves to be persuaded that they could make money for sure in these games by following betting systems such as the d'Alembert. What made these systems so seductive? Part of the answer is that some of the systems, including the d'Alembert, can give bettors a very high probability of winning a small or moderate amount. But there is also a more subtle aspect of the seduction. When the systems do win, their return on investment --- the gain relative to the amount of money the bettor has to take out of their pocket and put on the table to cover their bets --- can be astonishingly high. Systems such as le tiers et le tout, which offer a large gain when they do win rather than a high probability of winning, also typically have a high upside return on investment. In order to understand these high returns on investment, we need to recognize that the denominator --- the amount invested --- is random, as it depends on how successive bets come out.

In this article, we compare some systems on their return on investment and their success in hiding their pitfalls. Systems that provide a moderate gain with a very high probability seem to accomplish this by stopping when they are ahead and more generally by betting less when they are ahead or at least have just won, while betting more when they are behind or have just lost. For historical reasons, we call this martingaling. Among martingales, the d'Alembert seems especially good at making an impressive return on investment quickly, encouraging gamblers' hope that they can use it so gingerly as to avoid the possible large losses, and this may explain why its popularity was so durable.

We also discuss the lessons that this aspect of gambling can have for evaluating success in business and finance and for evaluating the results of statistical testing.

Gradient boosting (GB) is a popular methodology used to solve prediction problems through minimization of a differentiable loss function, L. GB is especially performant in low and medium dimension problems. This paper presents a simple adjustment to GB motivated in part by artificial neural networks. Specifically, our adjustment inserts a square or rectangular matrix multiplication between the output of a GB model and the loss, L. This allows the output of a GB model to have increased dimension prior to being fed into the loss and is thus "wider" than standard GB implementations. We provide performance comparisons on several publicly available datasets. When using the same tuning methodology and same maximum boosting rounds, Wide Boosting outperforms standard GB in every dataset we try.

Many studies of wealth inequality make the ergodic hypothesis that rescaled wealth converges rapidly to a stationary distribution. Changes in distribution are expressed through changes in model parameters, reflecting shocks in economic conditions, with rapid equilibration thereafter. Here we test the ergodic hypothesis in an established model of wealth in a growing and reallocating economy. We fit model parameters to historical data from the United States. In recent decades, we find negative reallocation, from poorer to richer, for which no stationary distribution exists. When we find positive reallocation, convergence to the stationary distribution is slow. Our analysis does not support using the ergodic hypothesis in this model for these data. It suggests that inequality evolves because the distribution is inherently unstable on relevant timescales, regardless of shocks. Studies of other models and data, in which the ergodic hypothesis is made, would benefit from similar tests.

The inferential model (IM) framework produces data-dependent, non-additive degrees of belief about the unknown parameter that are provably valid. The validity property guarantees, among other things, that inference procedures derived from the IM control frequentist error rates at the nominal level. A technical complication is that IMs are built on a relatively unfamiliar theory of random sets. Here we develop an alternative---and practically equivalent---formulation, based on a theory of possibility measures, which is simpler in many respects. This new perspective also sheds light on the relationship between IMs and Fisher's fiducial inference, as well as on the construction of optimal IMs.

C. Cunen, N. Hjort, and T. Schweder published a comment on our paper, Satellite conjunction analysis and the false confidence theorem.  Here is our response to their comment.

Whether the goal is to estimate the number of people that live in a congressional district, to estimate the number of individuals that have died in an armed conflict, or to disambiguate individual authors using bibliographic data, all these applications have a common theme --- integrating information from multiple sources. Before such questions can be answered, databases must be cleaned and integrated in a systematic and accurate way, commonly known as structured entity resolution (record linkage or de-duplication). In this article, we review motivational applications and seminal papers that have led to the growth of this area. We review modern probabilistic and Bayesian methods in statistics, computer science, machine learning, database management, economics, political science, and other disciplines that are used throughout industry and academia in applications such as human rights, official statistics, medicine, citation networks, among others. Finally, we discuss current research topics of practical importance.

This paper presents a model for the consumption of a cultural good where consumers can either purchase or pirate the good (or not consume it). Because of the specificity of the cultural good, active consumers (users), buyers and pirates, derive a network utility that depends on the numbers of users of the goods with which they can share their experience of the cultural good. It is shown that the monopoly firm selling the cultural good may obtain a higher profit when piracy is possible than when it is not. Consequently, it is presented that increasing the cost of piracy has a non monotonic effect on a firm's profit and welfare.

The maximization of entropy S within a closed system is accepted as an inevitability (as the second law of thermodynamics) by statistical inference alone.
The Maximum Entropy Production Principle (MEPP) states that such a system will maximize its entropy as fast as possible.
There is still no consensus on the general validity of this MEPP, even though it shows remarkable explanatory power (both qualitatively and quantitatively), and has been empirically demonstrated for many domains.
In this theoretical paper I provide a generalization of state-spaces, to fundamentally show that the MEPP actually follows from the same statistical inference, as that of the 2nd law of thermodynamics.
For this generalization I introduce the concepts of the poly-dimensional statespace and microstate-density.
These concepts also allows for the abstraction of 'Self Organizing Criticality' to a bifurcating local difference in this density.
Ultimately, the inevitability of the maximization of entropy production has significant implications for the models we use in developing and monitoring socio-economic and financial policies, explaining organic life at any scale, and in curbing the growth of our technological progress, to name a few areas.

Predicting the response at an unobserved location is a fundamental problem in spatial statistics. Given the difficulty in modeling spatial dependence, especially in non-stationary cases, model-based prediction intervals are at risk of misspecification bias that can negatively affect their validity. Here we present a new approach for model-free nonparametric spatial prediction based on the conformal prediction machinery. Our key observation is that spatial data can be treated as exactly or approximately exchangeable in a wide range of settings. In particular, under an infill asymptotic regime, we prove that the response values are, in a certain sense, locally approximately exchangeable for a broad class of spatial processes, and we develop a local spatial conformal prediction algorithm that yields valid prediction intervals without strong model assumptions like stationarity. Numerical examples with both real and simulated data confirm that the proposed conformal prediction intervals are valid and generally more efficient than existing model-based procedures for large datasets across a range of non-stationary and non-Gaussian settings.

Comment on the proposal to rename the R.A. Fisher Lecture.

Peters [2011a] defined an optimal leverage which maximizes the time-average growth rate of an investment held at constant leverage. It was hypothesized that this optimal leverage is attracted to 1, such that, e.g., leveraging an investment in the market portfolio cannot yield long-term outperformance. This places a strong constraint on the stochastic properties of prices of traded assets, which we call "leverage efficiency." Market conditions that deviate from leverage efficiency are unstable and may create leverage-driven bubbles. Here we expand on the hypothesis and its implications. These include a theory of noise that explains how systemic stability rules out smooth price changes at any pricing frequency; a resolution of the so-called equity premium puzzle; a protocol for central bank interest rate setting to avoid leverage-driven price instabilities; and a method for detecting fraudulent investment schemes by exploiting differences between the stochastic properties of their prices and those of legitimately-traded assets. To submit the hypothesis to a rigorous test we choose price data from different assets: the S&P500 index, Bitcoin, Berkshire Hathaway Inc., and Bernard L. Madoff Investment Securities LLC. Analysis of these data supports the hypothesis.

This version of my PhD thesis has been produced for the open access open peer review platform <researchers.one>. I am interested in reviewer feedback. Please feel free to upload your reviews, (dis)agreements, typos, errors, etc. directly to <researchers.one> or email me. Compared to the original submission this version contains only minor corrections with regard to e.g. typos, misplaced citations and some resolved ordering issues in the bibliography.

The spread of infectious disease in a human community or the proliferation of fake news on social media can be modeled as a randomly growing tree-shaped graph. The history of the random growth process is often unobserved but contains important information such as thesource of the infection. We consider the problem of statistical inference on aspects of the latent history using only a single snapshot of the final tree. Our approach is to apply random labels to the observed unlabeled tree and analyze the resulting distribution of the growth process, conditional on the final outcome. We show that this conditional distribution is tractable under a shape-exchangeability condition, which we introduce here, and that this condition is satisfied for many popular models for randomly growing trees such as uniform attachment, linear preferential attachment and uniform attachment on a D-regular tree. For inference of the rootunder shape-exchangeability, we propose computationally scalable algorithms for constructing confidence sets with valid frequentist coverage as well as bounds on the expected size of the confidence sets. We also provide efficient sampling algorithms which extend our methods to a wide class of inference problems.

In this paper we review the current personal protective equipment (PPE) recommendations for healthcare workers in the setting of COVID19 pandemic and analyze the framework upon which authorities currently make these recommendations.  We examine multiple uncertainties within the model assumptions and conclude that precaution dictates that we should adopt a more stringent PPE policy for our healthcare workforce even in more routine helthcare settings.

Biochemical mechanisms are complex and consist of many interacting proteins, genes, and metabolites. Predicting the future states of components in biochemical processes is widely applicable to biomedical research. Here we introduce a minimal model of biochemical networks using a system of coupled linear differential equations and a corresponding numerical model. To capture biological reality, the model includes parameters for stochastic noise, constant time delay, and basal interactions from an external environment. The model is sufficient to produce key biochemical patterns including accumulation, oscillation, negative feedback, and homeostasis. Applying the model to the well-studied {\it lac} operon regulatory network reproduces key experimental observations under different metabolic conditions. By component subtraction, the model predicts the effect of genetic or chemical inhibition in the same {\it lac} regulatory network. Thus, the minimal model may lead to methods for motivating therapeutic targets and predicting the effects of experimental perturbations in biochemical networks.

Behavioural economics provides labels for patterns in human economic behaviour. Probability weighting is one such label. It expresses a mismatch between probabilities used in a formal model of a decision (i.e. model parameters) and probabilities inferred from real people's decisions (the same parameters estimated empirically). The inferred probabilities are called ``decision weights.'' It is considered a robust experimental finding that decision weights are higher than probabilities for rare events, and (necessarily, through normalisation) lower than probabilities for common events. Typically this is presented as a cognitive bias, i.e. an error of judgement by the person. Here we point out that the same observation can be described differently: broadly speaking, probability weighting means that a decision maker has greater uncertainty about the world than the observer. We offer a plausible mechanism whereby such differences in uncertainty arise naturally: when a decision maker must estimate probabilities as frequencies in a time series while the observer knows them a priori. This suggests an alternative presentation of probability weighting as a principled response by a decision maker to uncertainties unaccounted for in an observer's model.

In order to more effectively combat the coronavirus pandemic, the authors propose a system for daily analysis of residential wastewater at points of discharge from buildings. Results of testing should be used for the implementation of local quarantines as well as informed administration of tests for individuals.

A response to John Ioannidis's article A fiasco in the making? As the coronavirus pandemic takes hold, we are making decisions without reliable data (March 17, 2020) in which he downplays the severe risks posed by coronavirus pandemic.

Observed SM particle masses are explained fully as the products of a harmonic sequence generated by a thermal oscillator within a quantum field. For leptons and quarks the correlation is excellent, but less so for bosons. The interpretation of the SM particle masses by this hypothesis demands a reevaluation of quantum mechanics to incorporate missing thermal factor(s).

Sparse PCA is one of the most popular tools for the dimensional reduction of high-dimensional data. Although many computational methods have been proposed for sparse PCA, Bayesian methods are still very few. In particular, there is a lack of fast and efficient algorithms for Bayesian sparse PCA. To fill this gap, we propose two efficient algorithms based on the expectation–maximization (EM) algorithm and the coordinate ascent variational inference (CAVI) algorithm—the double parameter expansion-EM (dPX-EM) and the PX-coordinate ascent variation inference (PX-CAVI) algorithms. By using a new spike-and-slab prior and applying the parameter expansion approach, we are able to avoid directly dealing with the orthogonal constraint between eigenvectors, and thus making it easier to compute the posterior. Simulation studies showed that the PX-CAVI outperforms the dPX-EM algorithm as well as other two existing methods. The corresponding R code is available on the website https://github.com/Bo-Ning/Bayesian-sparse-PCA.

The lack of an explicit brain model has been holding back AI improvements leading to applications that don’t model language in theory. This paper explains Patom theory (PT), a theoretical brain model, and its interaction with human language emulation.

Patom theory explains what a brain does, rather than how it does it. If brains just store, match and use patterns comprised of hierarchical bidirectional linked-sets (sets and lists of linked elements), memory becomes distributed and matched both top-down and bottom-up using a single algorithm. Linguistics shows the top-down nature because meaning, not word sounds or characters, drives language. For example, the pattern-atom (Patom) “object level” that represents the multisensory interaction of things, is uniquely stored and then associated as many times as needed with sensory memories to recognize the object accurately in each modality. This is a little like a template theory, but with multiple templates connected to a single representation and resolved by layered agreement.

In combination with Role and Reference Grammar (RRG), a linguistics framework modeling the world’s diverse languages in syntax, semantics and discourse pragmatics, many human-like language capabilities become demonstrable. Today’s natural language understanding (NLU) systems built on intent classification cannot deal with natural language in theory beyond simplistic sentences because the science it is built on is too simplistic. Adoption of the principles in this paper provide a theoretical way forward for NLU practitioners based on existing, tested capabilities.

According to a proof in Euclidean geometry of the "Cardinality of the Continuum", that is attributed to Georg Cantor, a line has as many points as any line segment (not inclusive of the two end points). However, this proof uses parallel lines, and therefore assumes Euclid's Parallel Postulate as an axiom. But Non-Euclidean geometries have alternative axioms. In Hyperbolic geometry, at any point off of a given line, there are a plurality of lines parallel to the given line. In Elliptic geometry (which includes Spherical geometry), no lines are parallel, so two lines always intersect. In Absolute geometry, neither Euclid's parallel postulate nor its alternatives are axioms. We provide an example in Spherical geometry and an example in Hyperbolic geometry wherein the "Cardinality of the Continuum" is false. Therefore the "Cardinality of the Continuum" is also false in Absolute geometry. So the "Continuum Hypothesis" is false too, because it assumes that the "Cardinality of the Continuum" is true.

Quantum Electrodynamics (QED) Renormalizaion is a logical paradox, and thus is mathematically invalid. It converts divergent series into finite values by use of the Euler-Mascheroni constant. The definition of this constant is a conditionally convergent series. But the Riemann Series Theorem proves that any conditionally convergent series can be rearranged to be divergent. This contradiction (a series that is both convergent and divergent) violates the Law of Non-Contradiction (LNC) in "classical" and intuitionistic logics, and thus is a paradox in these logics. This result also violates the commutative and associative properties of addition, and the one-to-two mapping from domain to range violates the definition of a function in Zermelo-Fraenkel set theory.

In addition, Zeta Function Regularization is logically and mathematically invalid. It equates two definitions of the Zeta function: the Dirichlet series definition, and Riemann's definition. For domain values in the half-plane of "analytic continuation", the two definitions contradict: the former is divergent and the latter is convergent. Equating these contradictory definitions there creates a paradox (if both are true), or is logically invalid (if one is true and the other false). We show that Riemann's definition is false, because its derivation includes a contradiction: the use of both the Hankel contour and Cauchy's integral theorem. Also, a third definition of the Zeta function is proven to be false. The Zeta function is exclusively defined by the Dirichlet series, which has no zeros (and therefore the Riemann hypothesis is a paradox).

Despite the importance of having robust estimates of the time-asymptotic total number of infections, early estimates of COVID-19 show enormous fluctuations. Using COVID-19 data for different countries, we show that predictions are extremely sensitive to the reporting protocol and crucially depend on the last available data-point, before the maximum number of daily infections is reached. We propose a physical explanation for this sensitivity, using a Susceptible-Exposed-Infected-Recovered (SEIR) model where the parameters are stochastically perturbed to simulate the difficulty in detecting asymptomatic patients, different confinement measures taken by different countries, as well as changes in the virus characteristics. Our results suggest that there are physical and statistical reasons to assign low confidence to statistical and dynamical fits, despite their apparently good statistical scores. These considerations are general and can be applied to other epidemics.

Abstract—Using methods from extreme value theory, we examine the major pandemics in history, trying to understand their tail properties.

Applying the shadow distribution approach developed by the authors for violent conflicts [5], we provide rough estimates for quantities not immediately observable in the data.

Epidemics and pandemics are extremely heavy-tailed, with a potential existential risk for humanity. This property should override conclusions derived from local epidemiological models in what relates to tail events.

I will try to analyze Harry Crane's article "Naïve probabilism" and formalise what is, for me, "the problem of medicine" (being blind to scale and his consequences). I will introduce Post-Normal Science, criticise the Geoffrey Rose's approach, praise Marc Jamoulle's work and concluding that not all the precautions (at different scales) are the same "ting". A short list at the end is exposed in a summary style. This is for starting a debate about epistemology of medicine and his (for me) lack of skin in the game and second-order thinking. Not a closure. Massive review is welcome (and necessary).

Empirical distributions have their in-sample maxima as natural censoring. We look at the "hidden tail", that is, the part of the distribution in excess of the maximum for a sample size of n. Using extreme value theory, we examine the properties of the hidden tail and calculate its moments of order p.

The method is useful in showing how large a bias one can expect, for a given n, between the visible in-sample mean and the true statistical mean (or higher moments), which is considerable for α close to 1.

Among other properties, we note that the "hidden" moment of order 0, that is, the exceedance probabil- ity for power law distributions, follows an exponential distribution and has for expectation 1/n regardless of the parametrization of the scale and tail index.

When gambling, think probability.
When hedging, think plausibility.
When preparing, think possibility.
When this fails, stop thinking. Just survive.

Naive probabilism is the (naive) view, held by many technocrats and academics, that all rational thought boils down to probability calculations. This viewpoint is behind the obsession with `data-driven methods' that has overtaken the hard sciences, soft sciences, pseudosciences and non-sciences. It has infiltrated politics, society and business. It's the workhorse of formal epistemology, decision theory and behavioral economics. Because it is mostly applied in low or no-stakes academic investigations and philosophical meandering, few have noticed its many flaws. Real world applications of naive probabilism, however, pose disproportionate risks which scale exponentially with the stakes, ranging from harmless (and also helpless) in many academic contexts to destructive in the most extreme events (war, pandemic). The 2019--2020 coronavirus outbreak (COVID-19) is a living example of the dire consequences of such probabilistic naivet'e. As I write this on March 13, 2020, we are in the midst of a 6 continent pandemic, the world economy is collapsing and our future is bound to look very different from the recent past. The major damage caused by the spread of COVID-19 is attributable to a failure to act and a refusal to acknowledge what was in plain sight. This shared negligence stems from a blind reliance on naive probabilism and the denial of basic common sense by global and local leaders, and many in the general public.

This introductory chapter of Probabilistic Foundations of Statistical Network Analysis explains the major shortcomings of prevailing efforts in statistical analysis of networks and other kinds of complex data, and why there is a need for a new way to conceive of and understand data arising from complex systems.

This paper presents an overview of Ergodicity Economics (EE) in plain English.

Ergodicity Economics (EE) applies a modern mathematical formalization to familiar financial concepts to reveal implications, and consequences that were previously unseen.

EE provides a clear distinction between:

• methods of averaging (arithmetic means vs. geometric means),
• meanings of averaging (ensemble expectation vs. time average), and
• reasons for the different meanings (additive vs. multiplicative growth dynamics).

These are distinctions with a difference because the average experience of an ensemble over many trajectories may not be the average experience of an individual over a single life history. Using ensemble expectations inappropriately - i.e. for non-ergodic observables – misleads individuals because it implies a physical system of counterfactuals that cannot exist in a single life trajectory.

EE quantifies the differences and the trade-offs between the collective meaning and the individual meaning of financial methods. EE’s perspective opens up previously unseen distinctions for evidence-based recommendations. These distinctions enable the creation of previously unavailable recommendations for the explicit benefit of individual clients. This differentiating impact on economic theory, asset valuation, product development, and advisory best practices is developing rapidly.

We study the problem of non-parametric Bayesian estimation of the intensity function of a Poisson point process. The observations are $n$ independent realisations of a Poisson point process on the interval $[0,T]$. We propose two related approaches. In both approaches we model the intensity function as piecewise constant on $N$ bins forming a partition of the interval $[0,T]$. In the first approach the coefficients of the intensity function are assigned independent gamma priors, leading to a closed form posterior distribution. On the theoretical side, we prove that as $n\rightarrow\infty,$ the posterior asymptotically concentrates around the ``true", data-generating intensity function at an optimal rate for $h$-H"older regular intensity functions ($0 < h\leq 1$).

In the second approach we employ a gamma Markov chain prior on the coefficients of the intensity function. The posterior distribution is no longer available in closed form, but inference can be performed using a straightforward version of the Gibbs sampler. Both approaches scale well with sample size, but the second is much less sensitive to the choice of $N$.

Practical performance of our methods is first demonstrated via synthetic data examples. We compare our second method with other existing approaches on the UK coal mining disasters data. Furthermore, we apply it to the US mass shootings data and Donald Trump's Twitter data.

The third moment skewness ratio Skew is a standard measure to understand and categorize distributions. However, its usual estimator based on sample second and third moments is biased very low and sensitive to outliers.  Thus, we study two alternative measures, the Triples parameter of Randles, et al. (1980) and the third L-moment ratio of Hosking (1990). We show by simulation that their associated estimators have excellent small sample properties and can be rescaled to be practical replacements for the third moment estimator of Skew.

In unreplicated two-way factorial designs, it is typical to assume no interaction between two factors. However, violations of this additivity assumption have often been found in applications, and tests for non-additivity have been a recurring topic since Tukey's one-degree of freedom test (Tukey, 1949). In the context of randomized complete block designs, recent work by Franck et al. (2013) is based on an intuitive model with "hidden additivity," a type of non-additivity where unobserved groups of blocks exist such that treatment and block effects are additive within groups, but treatment effects may be different across groups. Their proposed test statistic for detecting hidden additivity is called the "all-con guration maximum interaction F-statistic" (ACMIF). The computations of the ACMIF also result in a clustering method for blocks related to the k-means procedure. When hidden additivity is detected, a new method is proposed here for con dence intervals of contrasts within groups that takes into account the error due to clustering by forming the union of standard intervals over a subset of likely con gurations.

An important but understudied question in economics is how people choose when facing uncertainty in the timing of events. Here we study preferences over time lotteries, in which the payment amount is certain but the payment time is uncertain. Expected discounted utility theory (EDUT) predicts decision makers to be risk-seeking over time lotteries. We explore a normative model of growth-optimality, in which decision makers maximise the long-term growth rate of their wealth. Revisiting experimental evidence on time lotteries, we find that growth-optimality accords better with the evidence than EDUT. We outline future experiments to scrutinise further the plausibility of growth-optimality.

The outbreak of a novel Coronavirus we are facing is poised to become a global pandemic if current approaches to stemming its spread prove to be insufficient. While we can't yet say what the ultimate impact of this event will be, this crisis and governments' responses to it reveal vulnerabilities and fragilities in the structure of our global socioeconomic milieux that will continue to produce cascading crises regardless of whether or not we are successful in preventing devastation from this particular pathogen. Here we discuss the implications and some strategic considerations.

We collected marathon performance data from a systematic sample of elite and sub-elite athletes over the period 2015 to 2019, then searched the internet for publicly-available photographs of these performances, identifying whether the Nike Vaporfly shoes were worn or not in each performance. Controlling for athlete ability and race difficulty, we estimated the effect on marathon times of wearing the Vaporfly shoes. Assuming that the effect of Vaporfly shoes is additive, we estimate that the Vaporfly shoes improve men's times between 2.1 and 4.1 minutes, while they improve women's times between 1.2 and 4.0 minutes. Assuming that the effect of Vaporfly shoes is multiplicative, we estimate that they improve men's times between 1.5 and 2.9 percent, women's performances between 0.8 and 2.4 percent. The improvements are in comparison to the shoe the athlete was wearing before switching to Vaporfly shoes, and represents an expected improvement rather than a guaranteed improvement.

Contrary to Ole Peters' claims in a recent Nature Physics article [1], the "Copenhagen Experiment" did not falsify Expected Utility Theory (EUT) and corroborate Ergodicity Econonomics.  The dynamic version of of EUT, multi-period EUT, predicts the same change in risk aversion that EE predicts when the dynamics are changed from multiplicative to additive.

The book investigates the misapplication of conventional statistical techniques to fat tailed distributions and looks for remedies, when possible.
Switching from thin tailed to fat tailed distributions requires more than "changing the color of the dress". Traditional asymptotics deal mainly with either n=1 or n=∞, and the real world is in between, under of the "laws of the medium numbers" --which vary widely across specific distributions. Both the law of large numbers and the generalized central limit mechanisms operate in highly idiosyncratic ways outside the standard Gaussian or Levy-Stable basins of convergence.
A few examples:

• The sample mean is rarely in line with the population mean, with effect on "naive empiricism", but can be sometimes be estimated via parametric methods.
• The "empirical distribution" is rarely empirical.
• Parameter uncertainty has compounding effects on statistical metrics.
• Dimension reduction (principal components) fails.
• Inequality estimators (GINI or quantile contributions) are not additive and produce wrong results.
• Many "biases" found in psychology become entirely rational under more sophisticated probability distributions
• Most of the failures of financial economics, econometrics, and behavioral economics can be attributed to using the wrong distributions.
  This book, the first volume of the Technical Incerto, weaves a narrative around published journal articles.

Valid prediction of future observations is an important and challenging problem. The two mainstream approaches for quantifying prediction uncertainty use prediction regions and predictive distribution, respectively, with the latter believed to be more informative because it can perform other prediction-related tasks. The standard notion of validity, what we refer to here as Type~1 validity, focuses on coverage probability bounds for prediction regions, while a notion of validity relevant to the other prediction-related tasks performed by predictive distributions is lacking. Here we present a new notion, called Type-2 validity, relevant to these other prediction tasks. We establish connections between Type-2 validity and coherence properties, and argue that imprecise probability considerations are required in order to achieve it. We go on to show that both types of prediction validity can be achieved by interpreting the conformal prediction output as the contour function of a consonant plausibility measure. We also offer an alternative characterization of conformal prediction, based on a new nonparametric inferential model construction, wherein the appearance of consonance is more natural, and prove its validity.

In this note, I analyse the code the data generated by M. Fodje's (2013, 2014) simulation programs epr-simple and epr-clocked. They are written in Python and were published on Github. Inspection of the program descriptions show that they make use of the detection-loophole and the coincidence-loophole respectively. I evaluate them with appropriate modified Bell-CHSH type inequalities: the Larsson detection-loophole adjusted CHSH, and the Larsson-Gill coincidence-loophole adjusted CHSH. The experimental efficiencies turn out to be approximately eta = 81% (close to optimal) and gamma = 55% (far from optimal). The observed values of CHSH are, as they must be, within the appropriate adjusted bounds Fodjes' detection-loophole model turns out to be very, very close to Pearle's famous 1970 model, so the efficiency is very close to optimal. The model also has the same defect as Pearle's: the joint detection rates exhibit signalling. Fodje's coincidence-loophole model is actually an elegant modification of his detection-loophole model. Because of this, however, it cannot lead to the optimal efficiency.

This is version 11 of this preprint. Earlier versions incorrectly stated that documentation of Michel Fodje's simulation was inexistent. This version also contains some further explanations of my terminology.

Inferential challenges that arise when data are censored have been extensively studied under the classical frameworks. In this paper, we provide an alternative generalized inferential model approach whose output is a data-dependent plausibility function. This construction is driven by an association between the distribution of the relative likelihood function at the interest parameter and an unobserved auxiliary variable. The plausibility function emerges from the distribution of a suitably calibrated random set designed to predict that unobserved auxiliary variable. The evaluation of this plausibility function requires a novel use of the classical Kaplan--Meier estimator to estimate the censoring rather than the event distribution. We prove that the proposed method provides valid inference, at least approximately, and our real- and simulated-data examples demonstrate its superior performance compared to existing methods.

One of the classic problems in complex systems is the existence and ubiquity of critically, characterized by scale-invariance in frequency space and a balance between emergence (randomness) and self-organization (order). Another universal characteristic of complex systems is their Antigrafility or the capacity of taking advantage from environmental randomness. Inhere we propose a primer hypothesis that both concepts are related and may be understood under an Information Theory framework using Fisher Information as unifying concept. We make some comments about possible connection with Autopoiesis and Contextuality.

Dark matter is the same as normal matter only excepting that dark matter quantum fields do not exhibit thermal oscillations which define time. Normal matter does exhibit stable thermal oscillations. There is no reason to postulate that dark matter is anything more mysterious than normal matter without heat oscillations. It requires no “new physics” either as laws or components.

Property taxes are a common revenue source for city governments. There are two property tax bases available--land value (site value, SV) or total property value (capital improved value, CIV). The choice of property tax base has implications for both the distribution and efficiency of the tax. We provide new evidence in favour of SV being both the more progressive and efficient property tax base. First, we use three Victorian datasets at different levels of geographic aggregation to model the SV-CIV ratio as a function of various household income measures. At all three levels, a higher SV-CIV ratio occurs in areas with higher incomes, implying that SV is the more progressive property tax base. Our range of results suggests that a one percent increase in the income of an area is associated with a 0.10 to 0.57 percentage point increase in the SV-CIV ratio. Second, we use historical council data to conduct a difference-in-difference analysis to compare the effect of switching from a CIV to SV tax base on the number of building approvals and value of construction investment. We find that switching from CIV to SV as a property tax base is associated with a 20% increase in the value of new residential construction. This is consistent with the view that changes to land value tax rates are non-neutral with respect to the timing of capital investment on vacant and under-utilised land, which we also demonstrate theoretically.

I implemented the data from a significant semiconductor fabrication company. Through supervised machine learning, I build a Random Forest classifier with up to 96% accuracy to detect defective wafers/lots after they have been produced, and I study which particular signals indicate the most to faults in fabricating. This research can provide information and support to prevent future failures in semiconductor fabrication.

We present a Gibbs sampler to implement the Dempster-Shafer (DS) theory of statistical inference for Categorical distributions with arbitrary numbers of categories and observations. The DS framework is trademarked by its three-valued uncertainty assessment (p, q, r), probabilities "for", "against", and "don't know", associated with formal assertions of interest. The proposed algorithm targets the invariant distribution of a class of random convex polytopes which encapsulate the inference, via establishing an equivalence between the iterative constraints of the vertex configuration and the non-negativity of cycles in a fully connected directed graph. The computational cost increases with the size of the input, linearly with the number of observations and polynomially in the number of non-empty categories. Illustrations of numerical examples include the testing of independence in 2 by 2 contingency tables and parameter estimation of the linkage model. Results are compared to alternative methods of Categorical inference.

We discuss how sampling design, units, the observation mechanism, and other basic statistical notions figure into modern network data analysis. These con- siderations pose several new challenges that cannot be adequately addressed by merely extending or generalizing classical methods. Such challenges stem from fundamental differences between the domains in which network data emerge and those for which classical tools were developed. By revisiting these basic statistical considerations, we suggest a framework in which to develop theory and methods for network analysis in a way that accounts for both conceptual and practical chal- lenges of network science. We then discuss how some well known model classes fit within this framework.

Bias resulting from model misspecification is a concern when predicting insurance claims. Indeed, this bias puts the insurer at risk of making invalid or unreliable predictions. A method that could provide provably valid predictions uniformly across a large class of possible distributions would effectively eliminate the risk of model misspecification bias. Conformal prediction is one such method that can meet this need, and here we tailor that approach to the typical insurance application and show that the predictions are not only valid but also efficient across a wide range of settings.

Purpose

To investigate transcutaneous core-needle biopsy of the supraclavicular fat as a minimally invasive and scar-free method of obtaining brown adipose tissue (BAT) samples.

Material and Methods

In a prospective clinical trial, 16 volunteers underwent biopsy on two separate occasions after FDG-PET had shown active BAT in the supraclavicular fossa with an FDG uptake (SUVmax) > 3 mg/dl. After identifying the ideal location for biopsy on FDG-PET/MRI, ultrasound-guided core-needle biopsy of supraclavicular fat with a 16G needle was performed under local anesthesia and aseptic conditions. Tissue samples were immediately shock-frozen in liquid nitrogen and processed for gene expression analysis of adipose tissue markers. Wounds were checked two weeks after the biopsy.

Results

Tissue sampling was successful in 15 volunteers in both scans and in one very lean volunteer (BMI=19.9 kg/m2) in only one visit, without any reported adverse events. Therefore 31 tissue samples were available for further analysis. Gene expression could be analyzed with high success rate in 30 out of 31 tissue biopsies. The intervention was well tolerated with local anesthetics. None of the volunteers showed any scarring.

Conclusion

Ultrasound-guided core-needle biopsy of FDG-positive supraclavicular fat yields sufficient BAT samples for quantification of molecular markers. It may, however, be limited in extremely lean individuals with very little supraclavicular fat.

We work on optimizing TSA security inspection process to improve the function of the airport security system and to help reduce passengers’ total time costs to get to the gates from security checkpoints. Our evaluation of a design of TSA security checkpoint process involves two aspects: Effectiveness and Cost. Based on this double-criterion evaluation process, we build an effectiveness-versus-cost strategy matrix in our conclusion to satisfy advisees with different demands and budgets. The major part of this paper will be focusing on discussing how we model and simulate the original process and all other 5 new versions with the proposed modification implemented. Our programmed models are constructed from the ground up, based on maximized realism. We verify the validity of the original version of the model by comparing the bottlenecks it identifies with our real- life experience. Then we formulate 5 new designs that target the bottlenecks to resolve the problem of long queueing time and high variance. The most insightful section of our paper is data Analysis. Data analysis is vitally crucial to the construction of our models. One important achievement of us is that we discovered the tetra-modal pattern of the ”time to get scanned property” data and interpret it in an inspirational way to excavate a huge amount of important information.

Catalan’s Conjecture was proven by Mihailescu in 2004. In this paper, I offer another simple proof. Firstly, the cyclotomic polynomial is explicitly constructed, which assumes fix prime exponents. Next the constraints are relaxed and another attempt is made, this time using elementary number theory not more complicated than Bezout’s Theorem and Fermat’s Little Theorem. The solution 3^2 − 2^3 = 1 is thus unique, and defends crucially upon the finiteness assumption, derivable from the Bertrand-Chebyshev theorem.

An important question in economics is how people choose between different payments in the future. The classical normative model predicts that a decision maker discounts a later payment relative to an earlier one by an exponential function of the time between them. Descriptive models use non-exponential functions to fit observed behavioral phenomena, such as preference reversal. Here we propose a model of discounting, consistent with standard axioms of choice, in which decision makers maximize the growth rate of their wealth. Four specifications of the model produce four forms of discounting - no discounting, exponential, hyperbolic, and a hybrid of exponential and hyperbolic - two of which predict preference reversal. Our model requires no assumption of behavioral bias or payment risk.

Meta-analysis based on only a few studies remains a challenging problem, as an accurate estimate of the between-study variance is apparently needed, but hard to attain, within this setting. Here we offer a new approach, based on the generalized inferential model framework, whose success lays in marginalizing out the between-study variance, so that an accurate estimate is not essential. We show theoretically that the proposed solution is at least approximately valid, with numerical results suggesting it is, in fact, nearly exact. We also demonstrate that the proposed solution outperforms existing methods across a wide range of scenarios.

Whether the predictions put forth prior to the 2016 U.S. presidential election were right or wrong is a question that led to much debate. But rather than focusing on right or wrong, we analyze the 2016 predictions with respect to a core set of {\em effectiveness principles}, and conclude that they were ineffective in conveying the uncertainty behind their assessments. Along the way, we extract key insights that will help to avoid, in future elections, the systematic errors that lead to overly precise and overconfident predictions in 2016. Specifically, we highlight shortcomings of the classical interpretations of probability and its communication in the form of predictions, and present an alternative approach with two important features.  First, our recommended predictions are safer in that they come with certain guarantees on the probability of an erroneous prediction; second, our approach easily and naturally reflects the (possibly substantial) uncertainty about the model by outputting plausibilities instead of probabilities.

This paper examines the development of Laplacean practical certainty from 1810, when Laplace proved his central limit theorem, to 1925, when Ronald A. Fisher published his Statistical Methods for Research Workers.

Although Laplace's explanations of the applications of his theorem were accessible to only a few mathematicians, expositions published by Joseph Fourier in 1826 and 1829 made the simplest applications accessible to many statisticians. Fourier suggested an error probability of 1 in 20,000, but statisticians soon used less exigent standards. Abuses, including p-hacking, helped discredit Laplace's theory in France to the extent that it was practically forgotten there by the end of the 19th century, yet it survived elsewhere and served as the starting point for Karl Pearson's biometry.

The probability that a normally distributed random variable is more than three probable errors from its mean is approximately 5%. When Fisher published his Statistical Methods, three probable errors was a common standard for likely significance. Because he wanted to enable research workers to use distributions other than the normal -- the t distributions, for example --- Fisher replaced three probable errors with 5%.

The use of significant after Fisher differs from its use by Pearson before 1920. In Pearson's Biometrika, a significant difference was an observed difference that signified a real difference. Biometrika's authors sometimes said that an observed difference is likely or very likely to be significant, but they never said that it is very significant, and they did not have levels of significance. Significance itself was not a matter of degree.

What might this history teach us about proposals to curtail abuses of statistical testing by changing its current vocabulary (p-value, significance, etc.)? The fact that similar abuses arose before this vocabulary was introduced suggests that more substantive changes are needed.

Big Bubble theory is a cosmological model where the universe is an expanding bubble in four-dimensional space. Expansion is driven by starlight and gravity acts like surface tension to form a minimal surface. This model is used to derive Minkowski’s spacetime geometrically from four-dimensional Euclidian space. Big Bubble cosmology is consistent with type 1a supernova redshifts without dark energy or expanding spacetime. A different origin for the cosmic microwave background is proposed. The size of the universe is estimated using Hubble’s constant and a doppler shift of the cosmic microwave background. A mechanism for Mach’s principle is described. Big Bubble theory is similar to Einstein’s 1917 cosmological model, which is shown to be a snapshot of a rapidly expanding universe in dynamic equilibrium, rather than a static universe. The orbital speed of stars in spiral galaxies can be reproduced with Newtonian dynamics and without dark matter. A quadratic equation is derived that predicts both faster and slower rotation than purely Kepler orbits, consistent with the behaviour of spiral and elliptical galaxies, and suggesting that spiral galaxies evolve into elliptical galaxies as they age. The Big Bubble physical concept provides a basis for some quantum physics phenomena.

The work argues that Nassim Taleb's precautionary principle should not apply to the domain of ‘GMOs’ any more than to other monopolizing economic domains, because the probability of systemic ruin stemming from the GM technology itself is dwarfed by other systemic risks of the Deductive-Optimization Economy of today.

The author proposes solutions of reinventing our imagination within specialized bodies of expertise by replacing the socially constructed fear to lose one’s face with a fear to miss out on an intellectual contribution. This may result in strengthening of public trust in the institutions and delay their demise.

Increased generation at the agricultural level (with the GM technology) absolutely must be accompanied by an even greater idea and dissent generation among professionals charged with developing and sustaining this complex system. Life starts with generation, not precaution; limiting the options is a path to extinction. We must limit the fear of loss instead. We will be less unsafe, insofar as it is possible to be safe from oneself, as long as the pace of idea generation within professional bodies outstrips the pace of complexity introduction into our life support systems, such as in agriculture.

A philosophical version

In this work of foresight, I communicated my perception of Taleb's policy paper and the Black Swan problem discussed in it. To this effect, I:

1. Re-conceptualized the concept of "foresight,” non-teleologically, and its “method”;
2. Revived Empedocles’ non-teleological philosophy of evolution with modern scientific data;
3. Located the real GMO safety problem in (you guessed it) teleology: in the suppression of dissent within institutions under a seeming assumption of knowing what waste is.

The idea of the paper is to think about the result presented in Numberphile (http://www. numberphile.com/) talk (https://www.youtube.com/watch?v=w-I6XTVZXww) where they claim that 1 + 2 + 3 + ..., the Gauss sum, converges to −1/12. In the video they make two strong statements: one that the Grandi’s Series 1 − 1 + 1 − 1 + 1 − 1 + ... tends to 1/2 and the second that as bizarre as the −1/12 result for the Gauss sum might appears, as it is connected to Physics (this result is related with the number of dimensions in String Theory) then it is plausible. In this work we argue that these two statements reflect adhesion to a particular probability narrative and to a particular scientific philosophical posture. We argue that by doing so, these (Gauss and Grandi series) results and String Theory ultimately, might be mathematical correct but they are scientifically (in the Galileo-Newton-Einstein tradition) inconsistent (at least). The philosophical implications of this problem are also discussed, focusing on the role of evidence and scientific demarcation.

A covering problem posed by Henri Lebesgue in 1914 seeks to find the convex shape of smallest area that contains a subset congruent to any point set of unit diameter in the Euclidean plane.  Methods used previously to construct such a covering can be refined and extended to provide an improved upper bound for the optimal area. An upper bound of 0.8440935944 is found.

In the context of predicting future claims, a fully Bayesian analysis---one that specifies a statistical model, prior distribution, and updates using Bayes's formula---is often viewed as the gold-standard, while Buhlmann's credibility estimator serves as a simple approximation. But those desirable properties that give the Bayesian solution its elevated status depend critically on the posited model being correctly specified. Here we investigate the asymptotic behavior of Bayesian posterior distributions under a misspecified model, and our conclusion is that misspecification bias generally has damaging effects that can lead to inaccurate inference and prediction. The credibility estimator, on the other hand, is not sensitive at all to model misspecification, giving it an advantage over the Bayesian solution in those practically relevant cases where the model is uncertain. This begs the question: does robustness to model misspecification require that we abandon uncertainty quantification based on a posterior distribution? Our answer to this question is No, and we offer an alternative Gibbs posterior construction. Furthermore, we argue that this Gibbs perspective provides a new characterization of Buhlmann's credibility estimator.

The Wilcoxon Rank Sum is a very competitive robust alternative to the two-sample t-test when the underlying data have tails longer than the normal distribution. Extending to the one-way model with k independent samples, the Kruskal-Wallis rank test is a competitive alternative to the usual F for testing if there are any location differences. However, these positives for rank methods do not extend as readily to methods for making all pairwise comparisons used to reveal where the differences in location may exist. We demonstrate via examples and simulation that rank methods can have a dramatic loss in power compared to the standard Tukey-Kramer method of normal linear models even for non-normal data. We also show that a well-established robust rank-like method can recover the power but does not fully control the familywise error rate in small samples.

Daniel Bernoulli’s study of 1738 [1] is considered the beginning of expected utility theory. Here I point out that in spite of this, it is formally inconsistent with today’s standard form of expected utility theory. Bernoulli’s criterion for participating in a lottery, as written in [1], is not the expected change in utility.

An inferential model encodes the data analyst's degrees of belief about an unknown quantity of interest based on the observed data, posited statistical model, etc. Inferences drawn based on these degrees of belief should be reliable in a certain sense, so we require the inferential model to be valid. The construction of valid inferential models based on individual pieces of data is relatively straightforward, but how to combine these so that the validity property is preserved? In this paper we analyze some common combination rules with respect to this question, and we conclude that the best strategy currently available is one that combines via a certain dimension reduction step before the inferential model construction.

Cooperation is a persistent behavioral pattern of entities pooling and sharing resources. Its ubiquity in nature poses a conundrum: whenever two entities cooperate, one must willingly relinquish something of value to the other. Why is this apparent altruism favored in evolution? Classical treatments assume a priori a net fitness gain in a cooperative transaction which, through reciprocity or relatedness, finds its way back from recipient to donor. Our analysis makes no such assumption. It rests on the insight that evolutionary processes are typically multiplicative and noisy. Fluctuations have a net negative effect on the long-time growth rate of resources but no effect on the growth rate of their expectation value. This is a consequence of non-ergodicity. Pooling and sharing reduces the amplitude of fluctuations and, therefore, increases the long-time growth rate for cooperators. Put simply, cooperators' resources outgrow those of similar non-cooperators. This constitutes a fundamental and widely applicable mechanism for the evolution of cooperation. Furthermore, its minimal assumptions make it a candidate explanation in simple settings, where other explanations, such as emergent function and specialization, are implausible. An example of this is the transition from single cells to early multicellular life.

Abstract

These papers - one proposition paper and ten responses - comprise a debate on shaken baby syndrome. This is the hypothesis that a Triad of indicators in the head of a dead baby reveal that it has been shaken to death, and that the killer was the person last in charge of the baby. The debate was scheduled to have appeared in Prometheus, a journal concerned with innovation rather than matters medical. It struck the editors of Prometheus that a hypothesis that had survived nearly half a century and was still resistant to challenge and change was well within the tradition of Prometheus debate. The debate focuses on the role of the expert witness in court, and especially the experiences of Waney Squier, a prominent paediatric pathologist, struck from the medical register in the UK for offering opinions beyond her core expertise and showing insufficient respect for established thinking and its adherents. The debate’s responses reveal much about innovation, and most about the importance of context, in this case the incompatibility of medicine and the law, particularly when constrained by the procedures of the court. Context was also important in the reluctance of Taylor & Francis, the publisher of Prometheus, to publish the debate on the grounds that its authors strayed from their areas of expertise and showed insufficient respect for established thinking.

Prometheus shaken baby debate

Contents

Introduction

The shaken baby debate - Stuart Macdonald

Proposition paper

Shaken baby syndrome: causes and consequences of conformity - Waney Squier

Response papers

Shaken baby syndrome: a fraud on the courts - Heather Kirkwood

Shaken baby: an evolving diagnosis deformed by the pressures of the courtroom - Susan Luttner

Waney Squier’s ordeal and the crisis of the shaken baby paradigm - Niels Lynøe

Another perspective - simply my brief thoughts - Dave Marshall

Has Squier been treated fairly? - Brian Martin

Commentary on the paper by Waney Squier: ‘Shaken baby syndrome: causes and consequences of conformity’ - Michael J Powers

Waney Squier and the shaken baby syndrome case: a clarion call to science, medicine and justice - Toni C Saad

The role of the General Medical Council - Terence Stephenson

When experts disagree - Stephen J. Watkins

The General Medical Council’s handling of complaints: the Waney Squier case - Peter Wilmshurst

In this paper we adopt the familiar sparse, high-dimensional linear regression model and focus on the important but often overlooked task of prediction. In particular, we consider a new empirical Bayes framework that incorporates data in the prior in two ways: one is to center the prior for the non-zero regression coefficients and the other is to provide some additional regularization. We show that, in certain settings, the asymptotic concentration of the proposed empirical Bayes posterior predictive distribution is very fast, and we establish a Bernstein--von Mises theorem which ensures that the derived empirical Bayes prediction intervals achieve the targeted frequentist coverage probability. The empirical prior has a convenient conjugate form, so posterior computations are relatively simple and fast. Finally, our numerical results demonstrate the proposed method's strong finite-sample performance in terms of prediction accuracy, uncertainty quantification, and computation time compared to existing Bayesian methods.

The universe is formed from proto-quantum field(s) (PQFs). The initiating event is the formation of a thermal gradient which establishes synchronous oscillations which describes time. Time is not quantised. Concomitantly PQFs, either directly or indirectly, differentiate into all the quantum fields required for the standard model of particles and forces, and three dimensional space. The transition of PQFs to functional quantum fields is a continuous process at the boundary of a spherical universe, a “ring of fire”, necessary to maintain time.

The universe is formed from proto-quantum field(s) (PQFs). The initiating event is the formation of a thermal gradient which establishes synchronous oscillations which describes time. Time is not quantised. Concomitantly PQFs, either directly or indirectly, differentiate into the all the quantum fields required for the standard model of particles and forces, and three dimensional space. The transition of PQFs to functional quantum fields is a continuous process at the boundary of a spherical universe, a “ring of fire”, necessary to maintain time.

This analysis shows that a special relativity interpretation matches observed type 1a supernova redshifts. Davis & Lineweaver reported in 2003 that a special relativity match to supernova redshift observations can be ruled out at more than 23σ, but MacLeod’s 2004 conclusion that this finding was incorrect and due to a mathematical error is confirmed. MacLeod’s plot of special relativity against observation has been further improved by using celerity (aka proper velocity) instead of peculiar velocity. A Hubble plot of type 1a supernova celerity against retarded distance has a straight line of 70 km s-1 Mpc-1 for as far back in time as we can observe, indicating that, with a special relativity interpretation of cosmological redshift, expansion of the universe is neither accelerating nor decelerating, and it is not necessary to invoke the existence of dark energy.

It’s been more than a century since Einstein’s special theory of relativity showed that Newton’s concept of time is incorrect, but society and science continue to use predominantly Newtonian language and thought. The words normally used to describe time don’t distinguish when time is a dimension, used for locating objects and events in spacetime, and when it’s a property of objects that can age at different rates. It is proposed to bring relativity’s terminology of coordinate time and proper time into everyday language, and thereby distinguish between ‘cotime’ (a dimensional property) and ‘protime’ (a property of objects related to energy). The differences between cotime and protime are significant and cotime might be a spatial dimension with units of length.

Statistics has made tremendous advances since the times of Fisher, Neyman, Jeffreys, and others, but the fundamental and practically relevant questions about probability and inference that puzzled our founding fathers remain unanswered. To bridge this gap, I propose to look beyond the two dominating schools of thought and ask the following three questions: what do scientists need out of statistics, do the existing frameworks meet these needs, and, if not, how to fill the void? To the first question, I contend that scientists seek to convert their data, posited statistical model, etc., into calibrated degrees of belief about quantities of interest. To the second question, I argue that any framework that returns additive beliefs, i.e., probabilities, necessarily suffers from false confidence---certain false hypotheses tend to be assigned high probability---and, therefore, risks systematic bias. This reveals the fundamental importance of non-additive beliefs in the context of statistical inference. But non-additivity alone is not enough so, to the third question, I offer a sufficient condition, called validity, for avoiding false confidence, and present a framework, based on random sets and belief functions, that provably meets this condition. Finally, I discuss characterizations of p-values and confidence intervals in terms of valid non-additive beliefs, which imply that users of these classical procedures are already following the proposed framework without knowing it.

A perfect economic storm emerged in M'exico in what was called (mistakenly under our analysis) The December Error (1994) in which Mexico's economy collapsed. In this paper, we show how Theoretical Psychics may help us to understand the under processes for this kind of economic crisis and eventually perhaps to develop an early warning. We specifically analyze monthly historical time series for inflation from January 1969 to November 2018. We found that Fisher information is insensible to inflation growth in the 80's decade but capture quite good The December Error (TDE). Our results show that under Salinas administration Mexican economy was characterized by unstable stability must probably due to hidden risk policies in the form of macro-economy controls that artificially suppress aleatority out of the system making it fragile. And so, we conclude that it was not at all a December error but a sexenal sustained error of fragilization.

Inhere we present a proposal of how to teach complexity using a Problem Based Learning approach under a set of philosophical principles inspired by the pedagogical experience in sustainability Sciences. We described the context in which we put on practise y these ideas that was a graduate course on Complexity and Data Science applied to Ecology.  In part two we present the final work presented by the students as they wrote it and which we believe could be submitted to a journal by its own merits

Inhere we expand the concept of Holobiont to incorporate niche construction theory in order to increase our understanding of the current planetary crisis. By this, we propose a new ontology, the Ecobiont, as the basic evolutionary unit of analysis. We make the case of \textit{Homo Sapiens} organized around modern cities (technobionts) as a different Ecobiont from classical \textit{Homo Sapiens} (i.e. Hunter-gatherers \textit{Homo Sapiens}). We consider that Ecobiont ontology helps to make visible the coupling of \textit{Homo Sapiens} with other biological entities under processes of natural and cultural evolution. Not to see this coupling hidden systemic risks and enhance the probability of catastrophic events. So Ecobiont ontology is necessary to understand and respond to the current planetary crisis.