What are we weighting for? A mechanistic model for probability weighting

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  1. G CharlesSeptember 4th, 2020 at 07:18 pm

    Please find attached, comments on the paper in MS Word format. Unfortunately, some of the math fonts prevent the comments from being displayed here.

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Abstract

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.

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➤  Version 1 (2020-04-29)

Citation

Ole Peters, Alexander Adamou, Mark Kirstein and Yonatan Berman (2020). What are we weighting for? A mechanistic model for probability weighting. Researchers.One, https://researchers.one/articles/what-are-we-weighting-for-a-mechanistic-model-for-probability-weighting/5f52699d36a3e45f17ae7e7c/v1.

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