Inertial Updating

TL;DR

This paper introduces inertial updating, a flexible belief updating framework that encompasses Bayesian, non-Bayesian, and zero-probability updating rules, unifying various belief revision methods.

Contribution

It formalizes inertial updating as minimizing subjective distance, unifying multiple belief updating rules, and connects it to existing models like HT and CPS.

Findings

Inertial updating includes Bayesian, non-Bayesian, and zero-probability updating as special cases.

The model is behaviorally equivalent to the Hypothesis Testing model.

Application to a persuasion game demonstrates the framework's practical relevance.

Abstract

We introduce and characterize inertial updating of beliefs. Under inertial updating, a decision maker (DM) chooses a belief that minimizes the subjective distance between their prior belief and the set of beliefs consistent with the observed event. Importantly, by varying the subjective notion of distance, inertial updating provides a unifying framework that nests three different types of belief updating: (i) Bayesian updating, (ii) non-Bayesian updating rules, and (iii) updating rules for events with zero probability, including the conditional probability system (CPS) of Myerson (1986a,b). We demonstrate that our model is behaviorally equivalent to the Hypothesis Testing model (HT) of Ortoleva (2012), clarifying the connection between HT and CPS. We apply our model to a persuasion game.