Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings.

ABSTRACT: Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty. In our task, a Bayesian learner distinguishes three equally salient levels of uncertainty. First, the Bayesian perceives irreducible uncertainty or risk: even knowing the payoff probabilities of a given arm, the outcome remains uncertain. Second, there is (parameter) estimation uncertainty or ambiguity: payoff probabilities are unknown and need to be estimated. Third, the outcome probabilities of the arms change: the sudden jumps are referred to as unexpected uncertainty. We document how the three levels of uncertainty evolved during the cours

SUBMITTER: Payzan-LeNestour E 

PROVIDER: S-EPMC3024253 | biostudies-literature | 2011 Jan

REPOSITORIES: biostudies-literature