演講者簡介 : Professor Ravid received his Ph.D. in Economics from Princeton University in 2015. He is currently an Assistant Professor at University of Chicago. His research fields are economic theory, microeconomics, game and choice theory, and behavioral economics.
演講摘要 : An agent acquires a costly flexible signal before making a decision. We explore the degree to which knowledge of the agent's information costs help predict her behavior. We establish an impossibility result: learning costs alone generate no testable restrictions on choice without also imposing constraints on actions' state-dependent utilities. By contrast, for most utility functions, knowing both the utility and information costs enables a unique behavioral prediction. When the utility function is known to belong to a given set, we provide an exact characterization of rationalizable behavior. Finally, we show that for smooth costs, most choices from a menu uniquely pin down the agent's decisions in all submenus.
