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Sequential Peer Prediction: Learning to Elicit Effort using Posted Prices
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use sequential posted-price learning with matching-based estimates of worker accuracy | SPP_PostPrice · Sequential peer prediction, game-theoretic workers | regret: R(T) \leq \tilde{O}(T^{3/4}) | Helped |
| Use matching-based accuracy estimates under a mean-field assumption | SPP_PostPrice · Sequential peer prediction, non-game-theoretic workers | regret: R(T)& \leq O(\lceil T^{\theta+z}\log T \rceil) +2C(\theta)L_1 (\frac{2}{(2p_L-1)^2}+\frac{1}{2p_L-1}) T^{1-\theta/2} + L | Helped |
| Use the informative threshold effort policy and truthful reporting | DG13 · One-stage peer prediction | worker utility at equilibrium | Helped |
| Split workers into groups and use the other group’s samples for learning | SPP_PostPrice · Sequential peer prediction with returning workers | incentive compatibility | Helped |
| Require only task answers rather than workers’ private costs | SPP_PostPrice · Sequential peer prediction | additional information required from workers | Helped |
Closest papers by meaning
- Peer Prediction for Learning Agents (NeurIPS 2022 · similarity 0.90)
- Deep Bayesian Trust: A Dominant and Fair Incentive Mechanism for Crowd (AAAI 2019 · similarity 0.89)
- Peer Neighborhood Mechanisms: A Framework for Mechanism Generalization (AAAI 2024 · similarity 0.89)
- Carrot and Stick: Eliciting Comparison Data and Beyond (NeurIPS 2024 · similarity 0.88)
- Stochastically Dominant Peer Prediction (NeurIPS 2025 · similarity 0.87)
- A Cramér–von Mises Approach to Incentivizing Truthful Data Sharing (NeurIPS 2025 · similarity 0.85)
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