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: A receiver observes a large, multidimensional dataset that is voluntarily disclosed by a strategic sender who cherrypicked it, without knowing how much data were originally obtained. We use a continuous model of big data to characterize equilibrium inferences and give an exact decision-robust welfare order on experiments that describes when, in the presence of cherrypicking, the receiver prefers one experiment over another. A preferable experiment gives stark likelihood-ratio differences in the distribution of data under sender-preferred vs. sender-dispreferred states, so that the sender faces a greater cost when dropping data to mimic more favorable distributions. These predictions formally capture the large-data limit.

Abstract: Managers in organizations have access to local, divisional information that allows for close comparison of related projects. We consider how this information can be incorporated in the design of meritocratic contests for selection and funding. Using a multi-agent cheap talk model, we ask what managers can truthfully report to a decision-maker or give as feedback to agents. A decision-maker who wants to efficiently allocate a prize while discouraging unnecessary effort should ask for reports of ordinal rankings to be used as nominations, and wishes to, but cannot, block communication of rankings to agents. When managers have richer, cardinal information but agents receive feedback about what was reported, we show that incentive constraints prevent the decision-maker who elicits that information from doing any better than in the ordinal case.

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.