Research map / Multi-agent systems and games / Games and equilibria
Bayesian Persuasion in Sequential Decision-Making
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Optimize signaling instead of providing no signals | Myopic-agent setting · General random instances (β=0) | principal payoff / FullControl: 0.5535 -> 0.8900 | Helped |
| Optimize advice-myopic signaling instead of providing no signals | Advice-myopic setting (equivalent payoff for Threat-FS) · General random instances (β=0) | principal payoff / FullControl: 0.5550 -> 0.8898 | Helped |
| Optimize signaling instead of providing no signals | Myopic-agent setting · Road navigation instances (β=0.5) | FullControl / strategy cost: 0.40159215 -> 0.702008504 | Helped |
| Use advice-myopic optimized signaling, with threats against a far-sighted agent, instead of no signals | Advice-myopic / threat-based setting · Road navigation instances (β=0.5) | FullControl / strategy cost: 0.658510602 -> 0.809816162 | Helped |
Closest papers by meaning
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- Computational Aspects of Bayesian Persuasion under Approximate Best Response (NeurIPS 2024 · similarity 0.89)
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- Online Bayesian Persuasion Without a Clue (NeurIPS 2024 · similarity 0.88)
- Persuasive Prediction via Decision Calibration (NeurIPS 2026 · similarity 0.88)
- Learning to Persuade a Biased Receiver (NeurIPS 2026 · similarity 0.87)
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