Under Review

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

Decision Theory Prospect Theory Probability Weighting Ergodicity Economics

Cite as:

Ole Peters and Alexander Adamou and Mark Kirstein and Yonatan Berman (2020). What are we weighting for? A mechanistic model for probability weighting. RESEARCHERS.ONE, https://www.researchers.one/article/2020-04-14.

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.