How Optimal Signaling Outperforms Classical Bayesian Strategies in Multi-Phase Trials by@bayesianinference
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How Optimal Signaling Outperforms Classical Bayesian Strategies in Multi-Phase Trials
by Bayesian InferenceNovember 11th, 2024
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Optimal signaling strategies can yield higher utility gains than classical Bayesian persuasion (BBP) strategies, especially at higher priors. BBP strategies, which mix states in one signal and reveal the true state in another, align closely with optimal signaling only at lower priors. As prior increases, a utility gap emerges, favoring alternative signaling strategies.
Authors:
(1) Shih-Tang Su, University of Michigan, Ann Arbor ([email protected]);
(2) Vijay G. Subramanian, University of Michigan, Ann Arbor and ([email protected]);
(3) Grant Schoenebeck, University of Michigan, Ann Arbor ([email protected]).
Table of Links
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
4.2 Determined versus sender-designed experiments
4.3 Multi-phase model and classical Bayesian persuasion and References
3.5 Comparison with classical Bayesian persuasion strategies
Given the optimal signaling strategy derived in Lemma 5, one natural followup question is the quantification of the sender’s utility improvement obtained by adopting the optimal signaling strategy in comparison to using strategies structurally similar to the optimal strategies in classical Bayesian persuasion for a binary state of the world. Owing the page limit, we directly define a class of strategies structurally similar to the classical Bayesian persuasion strategy below and provide the justification in our online version [23].
Definition 4. With binary states of the world, a (binary-state) Bayesian persuasion (BBP) strategy is a strategy that “mixes two possible states in one signal and reveals the true state on the other signal”.
This paper is available on arxiv under CC 4.0 license.
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