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We have decided to discontinue the publication of preprints on our preprint server as of 1 March 2024. The publication culture within mathematics has changed so much due to the rise of repositories such as ArXiV (www.arxiv.org) that we are encouraging all institute members to make their preprints available there. An institute's repository in its previous form is, therefore, unnecessary. The preprints published to date will remain available here, but we will not add any new preprints here.

MiS Preprint
48/2020

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

Ole Peters, Alexander Adamou, Mark Kirstein and Yonatan Berman

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.

Received:
May 7, 2020
Published:
May 8, 2020
MSC Codes:
91B06, 91B30, 91B84
PACS:
89.65.Gh, 05.45.Tp
Keywords:
Ergodicity Economics, Prospect Theory, Probability Weighting, Decision Theory

Related publications

Preprint
2020 Repository Open Access
Ole Peters, Alexander Adamou, Mark Kirstein and Yonatan Berman

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