arXiv subjects

Denis Shishkin

Publications and source records attributed to Denis Shishkin.

3 recordsLinked to original sources

arXiv2024

Perfect Bayesian Persuasion

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.

econ.TH↗

arXiv2022

Withholding Verifiable Information

We study a class of finite-action disclosure games in which the sender's preferences are state-independent and the receiver's optimal action depends only on the expected state. While receiver-preferred equilibria in these games involve full revelation, other equilibria are less well understood. We show that any equilibrium payoff can be obtained with a disclosure strategy corresponding to a partition with a laminar structure that allows pooling of nonadjacent states. In a sender-preferred equilibrium, such a structure balances inducing more sender-favorable actions with deterring deviations. Leveraging this insight, we identify conditions under which the sender does not benefit from commitment power. We then apply these results to study selling with quality disclosure and influencing voters.

econ.TH↗

arXiv2022

On the limitations of data-based price discrimination

The classic third degree price discrimination (3PD) model requires the knowledge of the distribution of buyer valuations and the covariate to set the price conditioned on the covariate. In terms of generating revenue, the classic result shows that 3PD is at least as good as uniform pricing. What if the seller has to set a price based only on a sample of observations from the underlying distribution? Is it still obvious that the seller should engage in 3PD? This paper sheds light on these fundamental questions. In particular, the comparison of the revenue performance between 3PD and uniform pricing is ambiguous overall when prices are set based on samples. This finding is in the nature of statistical learning under uncertainty: a curse of dimensionality, but also other small sample complications.

cs.GT↗