Forgetful updating and stubborn decision-makers

A decision-maker receives an informative signal each period and is randomly required to make a terminal action based on the signals received so far. The decision-maker is restricted to use a (stochastic) finite automaton no larger than a given size to process information. In contrast to the existing literature that focuses on very low probability of termination, I consider information structures with a (nearly) revealing signal, in which analytical solutions are available for all probability values of termination. Results from that model reveal two robust predictions regarding constrained optimal behaviour. First, it is optimal to ignore small (in terms of informativeness) signals. Second, when deterministic schemes are optimal, big signals with similar strengths should be treated similarly; otherwise, randomization takes a lexicographic order according to the strengths of the signals. I also identify a new behavioural bias, information stubbornness, according to which the decision maker does not respond to further informative signals after seeing a nearly revealing signal. As a result, the decision-maker can persistently choose the wrong action even after an unbounded number of informative signals.