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

### Mark Kirstein

DAAD Prime Fellow in Ergodicity Economics

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.

### Location

Group Meeting at Max-Planck-Institute for Mathematics in the Sciences, Leipzig

### Date & Time

Tuesday 3^{rd} November 2020 at 15.00hrs (CET)