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).
:::
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
4.3 Multi-phase model and classical Bayesian persuasion
\ Note that the pruned tree will potentially be unbalanced.
\ Lemma 7. Given an N-phase trial M with binary-outcome experiments, if there exists a pruned N-phase trial model P run(M) such that the following two conditions hold, then the sender’s expected utility is given by an equivalent single-phase Bayesian persuasion model.
\
For every non-trivial determined experiment, its sibling is either a trivial or a sender-designed experiment.
\
There exists a least one sender-designed experiment in each (from root to leaf) experiment sequence of P run(M).
References
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:::info This paper is available on arxiv under CC 4.0 license.
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This content originally appeared on HackerNoon and was authored by Bayesian Inference
Bayesian Inference | Sciencx (2024-11-11T12:00:12+00:00) Generalizing Signaling Strategies in Multi-phase Trials. Retrieved from https://www.scien.cx/2024/11/11/generalizing-signaling-strategies-in-multi-phase-trials/
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