Bounded Memory and Biases in Information Processing. Issue 6 (November 2014) - Wilson, Andrea
<x xml:space="preserve">Abstract</x> Before choosing among two actions with state‐dependent payoffs, a Bayesian decision‐maker with a finite memory sees a sequence of informative signals, ending each period with fixed chance. He summarizes information observed with a finite‐state automaton. I characterize the optimal protocol as an equilibrium of a dynamic game of imperfect recall; a new player runs each memory state each period. Players act as if maximizing expected payoffs in a common finite action decision problem. I characterize equilibrium play with many multinomial signals. The optimal protocol rationalizes many behavioral phenomena, like "stickiness, " salience, confirmation bias, and belief polarization.