Research

Abstract: We analyze situations where players build reputations for honesty rather than for playing particular actions. A patient player faces a sequence of short-run opponents. Before players act, the patient player announces their intended action after observing both a private payoff shock and a signal of what actions will be feasible that period. The patient player is either an honest type who keeps their word whenever their announced action is feasible, or an opportunistic type who freely chooses announcements and feasible actions. Short-run players only observe the current-period announcement and whether the patient player has kept their word in the past. We provide sufficient conditions under which the patient player can secure their optimal commitment payoff by building a reputation for honesty. Our proof introduces a novel technique based on concentration inequalities.

Abstract: We consider the disclosure problem of a sender with a large dataset of hard evidence. The sender has an incentive to drop observations before submitting the data to the receiver to persuade them to take a favorable action. We predict which observations the sender discloses using a model with a continuum of data, and show that this model approximates the outcomes with large, multi-variable datasets. In the receiver's preferred equilibrium, the sender submits a body of evidence that imitates the natural distribution under a more desirable target state. As a result, it is enough for an experiment to record data on outcomes that maximally distinguish higher states. A characterization of these strategies shows that senders with little data or a favorable state fully disclose their data, but still suffer from the receiver’s skepticism, and therefore are worse-off than they are under full information. On the other hand, senders with large datasets can benefit from voluntary disclosure by dropping observations under low states.

Abstract: Organizations feature local, divisional information that allows for close ordinal comparison of related projects. We consider how such partial rankings can be used in an information design environment to inform selection and funding decisions when reported to the decision-maker, and/or given as feedback to agents. When agents compete using costly effort to be selected, a decision-maker who wants to preserve the optimal prize allocation, but also discourage unnecessary effort, should use rankings of related projects to rule out some options. In addition, the most efficient way to inform agents about their place in the ranking is to give no feedback. Lastly, if divisional rankings are held by self-interested division managers who can privately inform their subordinates, then they will be communicated truthfully to the decision-maker, but will also be leaked to agents.

Abstract: We study the Federal Reserve’s problem of disclosing the models it uses in supervisory stress tests of large banks. Banks argue that nondisclosure leads to inefficiencies stemming from uncertainty, but regulators are concerned that full disclosure can lead to banks gaming the system. We formalize the intuition behind this trade-off in a stylized model where both the regulator and banks have imperfect, private “models” about a risky asset, and the regulator uses its own model to ‘stress test’ the investment. We show that if the regulator uses its model to test the banks’ investment, full disclosure is suboptimal, and the regulator may benefit from hiding the model when the bank’s model is more precise than the regulator’s own model. The key idea is that hiding the regulator’s model forces the bank to guess it using the bank’s own models, effectively eliciting the bank’s private information. We also show that if the regulator can fine-tune disclosure policies, the regulator can approximately enforce the first-best action of banks, as if the regulator fully knew all the private information held by banks. The intuition is closely related to the Cremer and McLean (1988) information rent extraction result.