Addressing Strategic Uncertainty with Incentives and Information, with Marina Halac and Elliot Lipnowski
A principal privately contracts with a set of agents who then simultaneously make a binary decision. Each contract specifies an individual allocation and the information the agent is given about a fundamental state and other agents’ contracts. We study the principal’s optimal scheme that induces a desired action profile as the unique rationalizable outcome. Our main result reduces this multi-agent problem to a two-step procedure in which information is designed agent-by-agent: the principal chooses a fundamental-state-contingent distribution over agent rankings and, separately for each agent, the agent’s information about the realized ranking and fundamental states. We illustrate with an application to team production.
A principal privately contracts with a set of agents who then simultaneously make a binary decision. Each contract specifies an individual allocation and the information the agent is given about a fundamental state and other agents’ contracts. We study the principal’s optimal scheme that induces a desired action profile as the unique rationalizable outcome. Our main result reduces this multi-agent problem to a two-step procedure in which information is designed agent-by-agent: the principal chooses a fundamental-state-contingent distribution over agent rankings and, separately for each agent, the agent’s information about the realized ranking and fundamental states. We illustrate with an application to team production.
When does society eventually learn the truth, or underlying state, via sequential observational learning? This paper develops the interplay of preferences satisfying single-crossing differences (SCD) and a new informational condition, directionally un- bounded beliefs (DUB). SCD preferences and DUB information are a jointly minimal pair of sufficient conditions for learning. When there are more than two states, DUB is weaker than unbounded beliefs, which characterizes learning for all preferences (Smith and Sorensen, 2000). Unlike unbounded beliefs, DUB is compatible with the monotone likelihood ratio property, and satisfied, for example, by normal information.
Rank Uncertainty in Organizations, with Marina Halac and Elliot Lipnowski (American Economic Review, 2021)
A principal incentivizes a team of agents to work by privately offering them bonuses contingent on team success. We study the principal’s optimal incentive scheme that implements work as a unique equilibrium. This scheme leverages rank uncertainty to address strategic uncertainty. Each agent is informed only of a ranking distribution and his own bonus, the latter making work dominant provided that higher-rank agents work. If agents are symmetric, their bonuses are identical. Thus, discrimination is strictly suboptimal, in sharp contrast with the case of public contracts (Winter, 2004). We characterize how agents’ ranking and compensation vary with asymmetric effort costs.
Evidence and Skepticism in Verifiable Disclosure Games
A key feature of communication with evidence is skepticism: to the extent possible, a receiver will attribute any incomplete disclosure to the sender concealing unfavorable evidence. I study when a change in the receiver’s prior belief about the sender’s evidence induces more skepticism, i.e. induces the receiver, regardless of his preferences, to take an equilibrium action that is less favorable for the sender following every message. I provide a definition of when one receiver prior belief expects more evidence than another and show that this characterizes more skepticism. As an input to this result, I fully characterize receiver optimal equilibrium outcomes in general verifiable disclosure games. I apply the results to a dynamic disclosure game in which the sender obtains and discloses evidence gradually. I identify the condition on the evidence structure such that the receiver cannot benefit from early communication with the sender.
Single-Crossing Differences on Distributions, with Navin Kartik and SangMok Lee
We characterize when choices among lotteries over arbitrary allocations are monotonic in an expected-utility agent’s type. Our necessary and sufficient condition is on the von Neumann-Morgenstern utility function; we identify an order over lotteries that generates the choice monotonicity when the condition holds. We discuss applications to cheap-talk games, costly signaling games, and collective choice problems. Our characterization requires some new results on monotone comparative statics and aggregating single-crossing functions, a by-product of which is a characterization of the monotone likelihood ratio property.
Incentivizing Information Design, with Valentin Somma
We study a principal who hires an agent to acquire costly information that will influence the decision of a third party. While the realized piece of information is observable and contractible, the experimental process is not. Assuming a general family of information cost functions (inclusive of Shannon’s mutual information), we show that the first best is achievable when the agent has limited liability or when he is risk averse, in contrast to standard moral hazard models. However, when the agent is both risk averse and has limited liability, efficiency losses arise generically. Specifically, we show that the principal obtains his first best outcome if and only if he intends to implement a ”symmetric” experiment, i.e. one in which the cost of generating each piece of evidence is the same. On the other hand, ”asymmetric” experiments that are uninformative with high probability but occasionally produce conclusive evidence will bear large agency costs.
Works in Progress:
Delegation with a Reputational Bias
Humility In Experts
Humility In Experts
