Motivated by practical concerns in applying information design to markets and service systems, we consider a persuasion problem between a sender and a receiver where the receiver may not be an expected utility maximizer. In particular, the receiver’s utility may be nonlinear in her belief; we deem such receivers as riskconscious. Such utility models arise, for example, when the receiver exhibits sensitivity to the variability and the risk in the payoff on choosing an action (e.g., waiting time for a service). In the presence of such nonlinearity, the standard approach of using revelationprinciple style arguments fails to characterize the set of signals needed in the optimal signaling scheme. Our main contribution is to provide a theoretical framework, using results from convex analysis, to overcome this technical challenge. In particular, in general persuasion settings with riskconscious agents, we prove that the sender’s problem can be reduced to a convex optimization program. Furthermore, using this characterization, we obtain a bound on the number of signals needed in the optimal signaling scheme.
We apply our methods to study a specific setting, namely binary persuasion, where the receiver has two possible actions (0 and 1), and the sender always prefers the receiver taking action 1. Under a mild convexity assumption on the receiver’s utility and using a geometric approach,we show that the convex program can be further reduced to a linear program. Furthermore, this linear program yields a canonical construction of the set of signals needed in an optimal signaling mechanism. In particular, this canonical set of signals only involves signals that fully reveal the state and signals that induce uncertainty between two states.We illustrate our results in the setting of signaling wait time information in an unobservable queue with customers whose utilities depend on the variance of their waiting times.
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Bayesian Persuasion in Sequential Trials
We consider a Bayesian persuasion problem where the sender tries to persuade the receiver to take a particular action via a sequence of signals. This we model by considering multiphase trials with different experiments conducted based on the outcomes of prior experiments. In contrast to most of the literature, we consider the problem with constraints on signals imposed on the sender. This we achieve by fixing some of the experiments in an exogenous manner; these are called determined experiments. This modeling helps us understand realworld situations where this occurs: e.g., multiphase drug trials where the FDA determines some of the experiments, startup acquisition by big firms where latestage assessments are determined by the potential acquirer, multiround job interviews where the candidates signal initially by presenting their qualifications but the rest of the screening procedures are determined by the interviewer. The nondetermined experiments (signals) in the multiphase trial are to be chosen by the sender in order to persuade the receiver best. With a binary state of the world, we start by deriving the optimal signaling policy in the only nontrivial configuration of a twophase trial with binaryoutcome experiments. We then generalize to multiphase trials with binaryoutcome experiments where the determined experiments can be placed at arbitrary nodes in the trial tree. Here we present a dynamic programming algorithm to derive the optimal signaling policy that uses the twophase trial solution’s structural insights. We also contrast the optimal signaling policy structure with classical Bayesian persuasion strategies to highlight the impact of the signaling constraints on the sender.
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 NSFPAR ID:
 10332940
 Editor(s):
 Feldman, M.
 Date Published:
 Journal Name:
 Web and Internet Economics. WINE 2021. Lecture Notes in Computer Science()
 Volume:
 13112
 Page Range / eLocation ID:
 2240
 Format(s):
 Medium: X
 Sponsoring Org:
 National Science Foundation
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