Before revision: In the context of a sequential search problem, I explore large-generations learning dynamics for agents who suffer from the "gambler's fallacy  " - the statistical bias of anticipating too much regression to the mean for realizations of independent random events. Searchers are uncertain about search poolqualities of different periods but infer these fundamentals from search outcomes of the previous generation.
After revision: In the context of a sequential search problem, I explore large-generations learning dynamics for agents who suffer from the "gambler's fallacy " Biased agents face an optimal-stopping problem, such as managers conducting sequential interviews. They are uncertain about search poolqualities of different periods but infer these fundamentals from search outcomes of the previous generation.