Exploring Sender Constraints in Two-Phase Bayesian Persuasion Trials

This section details a two-phase Bayesian persuasion model where only phase I is sender-designed, while phase II experiments are fixed. The sender’s strategy hinges on selecting a phase-I probability pair to influence the receiver’s action indirectly.


This content originally appeared on HackerNoon and was authored by Bayesian Inference

:::info Authors:

(1) Shih-Tang Su, University of Michigan, Ann Arbor (shihtang@umich.edu);

(2) Vijay G. Subramanian, University of Michigan, Ann Arbor and (vgsubram@umich.edu);

(3) Grant Schoenebeck, University of Michigan, Ann Arbor (schoeneb@umich.edu).

:::

Abstract and 1. Introduction

2. Problem Formulation

2.1 Model of Binary-Outcome Experiments in Two-Phase Trials

3 Binary-outcome Experiments in Two-phase Trials and 3.1 Experiments with screenings

3.2 Assumptions and induced strategies

3.3 Constraints given by phase-II experiments

3.4 Persuasion ratio and the optimal signaling structure

3.5 Comparison with classical Bayesian persuasion strategies

4 Binary-outcome Experiments in Multi-phase trials and 4.1 Model of binary-outcome experiments in multi-phase trials

4.2 Determined versus sender-designed experiments

4.3 Multi-phase model and classical Bayesian persuasion and References

2.1 Model of Binary-Outcome Experiments in Two-Phase Trials

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\ We end this section by emphasizing that this model is the only non-trivial two-phase trial configuration when determined and designed experiments coexist. In other configurations such that some of the phase-II experiments can be designed by sender, the model can be reduced to a corresponding single-phase trial in the sense that the single-phase trial will yield the same payoffs for both sender and receiver when they play optimally. (Note that the reduced model may have a different prior if the experiment in phase-I is determined).

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:::info This paper is available on arxiv under CC 4.0 license.

:::



This content originally appeared on HackerNoon and was authored by Bayesian Inference


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