Preprint 48/2020

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

Ole Peters, Alexander Adamou, Mark Kirstein, and Yonatan Berman

Contact the author: Please use for correspondence this email.
Submission date: 07. May. 2020
Pages: 19
Bibtex
MSC-Numbers: 91B06, 91B30, 91B84
PACS-Numbers: 89.65.Gh, 05.45.Tp
Keywords and phrases: Ergodicity Economics, Prospect Theory, Probability Weighting, Decision Theory
Download full preprint: PDF (780 kB)
Link to arXiv:See the arXiv entry of this preprint.

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.

25.05.2020, 16:29