Anton Kolotilin

Experimental Design to Persuade

A sender chooses ex ante how information will be disclosed ex post. A receiver obtains public information and information disclosed by the sender. Then he takes one of two actions. The sender wishes to maximize the probability that the receiver takes the desired action. I show that the sender optimally discloses only whether the receivers utility is above a cutoff. I derive necessary and sufficient conditions for the senders and receivers welfare to be monotonic in information. Most notably, the senders welfare increases with the precision of the senders potential information and decreases with the precision of public information.

PERSUASION OF A PRIVATELY INFORMED RECEIVER

We study persuasion mechanisms in linear environments. A privately informed receiver chooses between two actions. A sender designs a persuasion mechanism that can condition the information disclosed to the receiver on the receiver’s report about his type. We establish the equivalence of implementation by persuasion mechanisms and by experiments. We also characterize the optimal persuasion mechanisms. In particular, if the density of the receiver’s type is log-concave, then the optimal persuasion mechanism reveals the state if and only if the state is below a threshold. We apply our results to the design of media censorship policies.

Optimal limited authority for principal

This article studies a principal-agent problem where the only commitment for the uninformed principal is to restrict the set of decisions she makes following a report by the informed agent. We show that an ex ante optimal equilibrium for the principal corresponds to a finite partition of the state space, and each retained decision is ex post suboptimal for the principal, biased toward the agentʼs preference. Generally an optimal equilibrium does not maximize the number of decisions the principal can credibly retain. Compared to no commitment, limited authority improves the quality of communication from the agent. As a result, it can give the principal a higher expected payoff than delegating the decision to the agent.

Optimal Information Disclosure: Quantity vs. Quality

A sender chooses ex ante how her information will be disclosed to a privately informed receiver who then takes one of two actions. The sender wishes to maximize the probability that the receiver takes the desired action. The sender faces an ex ante quantity-quality tradeoff: sending positive messages more often (in terms of the senders information) makes it less likely that the receiver will take the desired action (in terms of the receivers information). Interestingly, the senders and receivers welfare is not monotonic in the precision of the receivers private information: the sender may find it easier to influence a more informed receiver, and the receiver may suffer from having more precise private information. Necessary and sufficient conditions are derived for full and no information revelation to be optimal.

Optimal Information Disclosure: A Linear Programming Approach

An uninformed sender designs a mechanism that discloses information about her type to a privately informed receiver, who then decides whether to act. I impose a single-crossing assumption, so that the receiver with a higher type is more willing to act. Using a linear programming approach, I characterize optimal information disclosure and provide conditions under which full and no revelation are optimal. Assuming further that the senders utility depends only on the senders expected type, I provide conditions under which interval revelation is optimal. Finally, I show that the expected utilities are not monotonic in the precision of the receivers private information.

The heterogeneity of concentrated prescribing behavior: Theory and evidence from antipsychotics

We present two new findings based on annual antipsychotic US prescribing data from IMS Health on 2867 psychiatrists who wrote 50 or more prescriptions in 2007. First, many of these psychiatrists have prescription patterns that are statistically significantly different than random draws from national market shares for prescriptions by psychiatrists. For example, many have prescription patterns that are significantly more concentrated than such draws. Second, among psychiatrists who are the most concentrated, different prescribers often concentrate on distinct drugs. Motivated by these two findings, we then construct a model of physician learning-by-doing that fits these facts and generates two further predictions: both concentration (on one or a few drugs) and deviation (from the prescription patterns of others) should be smaller for high-volume physicians. We find empirical support for these predictions. Furthermore, our model outperforms an alternative theory concerning detailing by pharmaceutical representatives.

Estimation of a Scale-Free Network Formation Model

Growing evidence suggests that many social and economic networks are scale free in that their degree distribution has a power-law tail. A common explanation for this phenomenon is a random network formation process with preferential attachment. For a general version of such a process, we develop the pseudo maximum likelihood and generalized method of moments estimators. We prove consistency of these estimators by establishing the law of large numbers for growing networks. Simulations suggest that these estimators are asymptotically normally distributed and outperform the commonly used non-linear least squares and Hill (1975) estimators in finite samples. We apply our estimation methodology to a co-authorship network.

Timing Information Flows

At an exogenous deadline, Receiver must take an action, the payoff of which depends on Sender’s private binary type. Sender privately observes whether and when an opportunity to start a public flow of information about her type arrives. She then chooses when to seize this opportunity. Starting the information flow earlier exposes to greater scrutiny but signals credibility. We characterize the set of equilibria and show that Sender always delays the information flow and completely withholds it with strictly positive probability. Focusing on the stable equilibrium, we derive comparative statics, and discuss implications for organizations, politics, and financial markets.