When in doubt average your priors: a latent cause model of risk and ambiguity aversion

Abstract In decision-making, the likelihood of outcomes is often partly unknown, a form of uncertainty known as ambiguity. Previous studies report that people tend to be averse to ambiguity. However, existing models of decision making under uncertainty fail to explain why people will sometimes show an actual preference for ambiguity, particularly in contexts where reward appears unlikely. Likewise, models of ambiguity do not provide predictions regarding decisions under risk, wherein reward probabilities are explicit. Here we apply a model wherein ambiguity attitudes hinge on a Bayesian average over prior beliefs about reward probabilities, where priors correspond to alternative latent causes governing the distribution of reward. By postulating that all gambles inherently embody a degree of ambiguity, our approach can seamlessly integrate decisions made under both risk and ambiguity. We provide empirical support for predictions of this model in two behavioural experiments. Firstly, as predicted by our model, we show that ambiguity attitude seamlessly transitions from ambiguity seeking at low reward probabilities to ambiguity aversion at higher reward probabilities. Secondly, the model accounts for an empirical observation of non-linear probability weighting for both risky and ambiguous choices. Our approach highlights a continuum between risk and ambiguity, providing an integrated framework for interpreting decision-making under uncertainty.