Sequential Peer Prediction: Learning to Elicit Effort using Posted Prices
Authors: Yang Liu, Yiling Chen
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We propose a sequential posted-price mechanism to dynamically learn the optimal reward level from workers contributions and to incentivize effort exertion and truthful reporting. We show that (1) in our mechanism, workers exerting effort according to a nondegenerate threshold policy and then reporting truthfully is an equilibrium that returns highest utility for every worker, and (2) The regret of our learning mechanism w.r.t. offering the optimal reward (price) is upper bounded by O(T 3/4) where T is the learning horizon. We further show the power of our learning approach when the reports of workers do not necessarily follow the game-theoretic equilibrium. |
| Researcher Affiliation | Academia | Yang Liu, Yiling Chen Harvard University, Cambridge MA, USA {yangl,yiling}@seas.harvard.edu |
| Pseudocode | Yes | Mechanism 1 (SPP Post Price) and Mechanism 2 (Explore Crowd) are presented as structured algorithm blocks with numbered steps. |
| Open Source Code | No | The paper does not include an unambiguous statement about the release of source code for the described methodology, nor does it provide a direct link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not describe experiments using a specific dataset for training. It defines a problem formulation and a sequential learning setting but does not refer to real-world or public datasets. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments or specific dataset splits (e.g., for validation). |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any empirical experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters, training configurations, or system-level settings. |