Learning in a Small/Big World
The study models how a decision-maker learns about a world based on its complexity relative to their cognitive ability. In small worlds, optimal learning follows Bayesian rules; in big worlds, it is optimal to ignore states and use non-Bayesian heuristics like correlation neglect. This links environmental complexity to the scope for non-Bayesian learning behaviors.