Bayesian Persuasion in Sequential Trials

TL;DR

This paper develops a model for Bayesian persuasion in multi-phase trials with both determined and non-determined experiments, deriving optimal signaling strategies and analyzing the impact of signaling constraints in sequential decision-making.

Contribution

It introduces a novel framework for Bayesian persuasion with exogenously fixed experiments, providing a dynamic programming approach for optimal signaling in multi-phase trials.

Findings

Optimal signaling policy for two-phase binary experiments derived.

Dynamic programming algorithm for multi-phase trials developed.

Signaling constraints significantly alter classical persuasion strategies.

Abstract

We consider a Bayesian persuasion or information design problem where the sender tries to persuade the receiver to take a particular action via a sequence of signals. This we model by considering multi-phase 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 real-world situations where this occurs: e.g., multi-phase drug trials where the FDA determines some of the experiments, funding of a startup by a venture capital firm, start-up acquisition by big firms where late-stage assessments are determined by the potential acquirer, multi-round job interviews where the candidates signal initially by presenting their qualifications but the rest of the screening procedures are determined by the interviewer. The non-determined experiments (signals) in the multi-phase 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 non-trivial configuration of a two-phase trial with binary-outcome experiments. We then generalize to multi-phase trials with binary-outcome experiments where the determined experiments can be placed at any chosen node in the trial tree. Here we present a dynamic programming algorithm to derive the optimal signaling policy that uses the two-phase 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.