**G Charles**September 4th, 2020 at 07:18 pmPlease find attached, comments on the paper in MS Word format. Unfortunately, some of the math fonts prevent the comments from being displayed here.

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.

➤ Version 1 (2020-04-29) |

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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