Alternatives to Bayesian Updating

We discuss models of updating that depart from Bayes' rule even when it is well-defined. After reviewing Bayes' rule and its foundations, we beginour analysis with models of non-Bayesian behavior arising from a bias, apull toward suboptimal behavior due to a heuristic or a mistake. Next, weexplore deviations caused by individuals questioning the prior probabilitiesthey initially used. We then consider non-Bayesian behavior resulting fromthe optimal response to constraints on perception, cognition, or memory, aswell as models based on motivated beliefs or distance minimization. Finally,we briefly discuss models of updating after zero probability events.