Job Market Seminar

Abstract: This paper studies how the cost of distinguishing similar situations shapes the pattern of mistakes in binary choices. I consider an agent who decides how much effort to invest in learning about a continuous state before choosing between two actions whose payoffs depend on that state. In perceptual tasks, the state can represent the relative strength of two stimuli. Building on the Fisher-information cost introduced by Hébert and Woodford (2021), I characterize the optimal state-dependent choice rule and give conditions under which it generates an S-shaped psychometric curve. Furthermore, I derive comparative statics showing how incentives and prior beliefs alter the response curve. In symmetric tasks, stronger incentives reduce mistakes, while greater concentration of the prior near the decision boundary increases errors. More generally, an action is chosen more often at every state when its relative payoff rises or when beliefs shift toward states that favor it. The results provide a costly-information foundation for psychometric curves and link their shape to the economic environment.