Doron Ravid

Working Papers

Robust Predictions in Games with Rational Inattention (with Tommaso Denti, abstract in Economics and Computation 2024) [Online Appendix]

We derive robust predictions in games involving flexible information acquisition, also known as rational inattention (Sims, 2003). These predictions remain accurate regardless of the exact specification of players’ learning abilities. Compared to scenarios where information is predetermined, rational inattention reduces welfare and introduces additional constraints on behavior. We show these constraints generically do not bind; the two knowledge regimes are behaviorally indistinguishable in most environments. Yet, we demonstrate the welfare difference they generate is robust: optimal policy depends on whether one assumes information is given or acquired. We provide the necessary tools for policy analysis in this context.

Predicting Choice from Information Costs (with Elliot Lipnowski, abstract in Economics and Computation 2023)

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. Finally, we show that for smooth costs, most choices from a menu uniquely pin down the agent's decisions in all submenus.

Monopoly, Product Quality, and Flexible Learning  (with Jeffrey Mensch, abstract in Economics and Computation 2024)

A seller offers a buyer a schedule of transfers and associated product qualities, as in Mussa and Rosen (1978). After observing this schedule, the buyer chooses a flexible costly signal about his type. We show it is without loss to focus on a class of mechanisms that compensate the buyer for his learning costs. Using these mechanisms, we prove the quality always lies strictly below the efficient level. This strict downward distortion holds even if the buyer acquires no information or when the buyer's posterior type is the highest possible given his signal, reversing the ``no distortion at the top'' feature that holds when information is exogenous. 

Perfect Bayesian Persuasion (with Elliot Lipnowski and Denis Shishkin)

A sender commits to an experiment to persuade a receiver. Accounting for the sender’s experiment-choice incentives, and not presupposing a receiver tie-breaking rule when indifferent, we characterize when the sender’s equilibrium payoff is unique and so coincides with her “Bayesian persuasion” value. A sufficient condition in finite models is that every action which is receiver-optimal at some belief is uniquely optimal at some other belief—a generic property. We similarly show the equilibrium sender payoff is typically unique in ordered models. In an extension, we show uniqueness generates robustness to imperfect sender commitment.

Focus, Then Compare (with: Kai Steverson)

We study the following random choice procedure. First, the agent focuses on an option at random from the set of available options. Then, she compares the focal option to each other available alternative. Comparisons are binary, random and independent of each other. The agent chooses the focal option if it passes all comparisons favorably. Otherwise, the agent draws a new focal option with replacement. We characterize the procedure's revealed preference implications, show that it accommodates the Attraction effect and Choice overload, and discuss how to conduct welfare comparisons. We conclude by showing that while utility maximization is the procedure's unique deterministic special case, nearly deterministic versions of the procedure can exhibit context effects.