Research map / Multi-agent systems and games / Games and equilibria
Peer Prediction for Learning Agents
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use less exploration (FPL4 instead of FPL8) | CA mechanism with Follow the Perturbed Leader agents · Simulated peer prediction, 400 runs x 800 rounds | converge proportion (truthful convergence rate) | Helped |
| remove exploration entirely (FTL instead of FPL2) | CA mechanism with Follow the Leader agents · Simulated peer prediction, 400 runs x 800 rounds | converge proportion (truthful convergence rate) | No clear effect |
| use epsilon-greedy with decreasing epsilon, outside the reward-based family | CA mechanism · Simulated peer prediction, 400 runs x 800 rounds | truthful convergence | Helped |
| use non-no-regret Follow the Leader learner | CA mechanism · Simulated peer prediction, 400 runs x 800 rounds | truthful convergence | No clear effect |
Closest papers by meaning
- Stochastically Dominant Peer Prediction (NeurIPS 2025 · similarity 0.90)
- Carrot and Stick: Eliciting Comparison Data and Beyond (NeurIPS 2024 · similarity 0.89)
- Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations (AAAI 2024 · similarity 0.88)
- Incentivizing Truthful Language Models via Peer Elicitation Games (NeurIPS 2025 · similarity 0.88)
- Nash Incentive-compatible Online Mechanism Learning via Weakly Differentially Private Online Learning (ICML 2024 · similarity 0.87)
- Impact of Decentralized Learning on Player Utilities in Stackelberg Games (ICML 2024 · similarity 0.86)
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