Information Elicitation Mechanisms for Statistical Estimation
Authors: Yuqing Kong, Grant Schoenebeck, Biaoshuai Tao, Fang-Yi Yu2095-2102
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study learning statistical properties from strategic agents with private information. In this problem, agents must be incentivized to truthfully reveal their information even when it cannot be directly veriļ¬ed. Moreover, the information reported by the agents must be aggregated into a statistical estimate. We study two fundamental statistical properties: estimating the mean of an unknown Gaussian, and linear regression with Gaussian error. The information of each agent is one point in a Euclidean space. Our main results are two mechanisms for each of these problems which optimally aggregate the information of agents in the truth-telling equilibrium. |
| Researcher Affiliation | Academia | Yuqing Kong,1 Grant Schoenebeck,2 Biaoshuai Tao,2 Fang-Yi Yu2 1CFCS, Computer Science Dept., Peking University, 2University of Michigan, Ann Arbor yuqkong.kong@pku.edu.cn, {schoeneb, bstao, fayu}@umich.edu |
| Pseudocode | Yes | Mechanism 1 The metric mechanism Mmetric [...] Mechanism 2 Proxy-BTS mechanism Mproxy [...] Mechanism 3 The disagreement mechanism Mdisagree |
| Open Source Code | No | The paper does not contain any statements about making source code available or links to code repositories. |
| Open Datasets | No | The paper is theoretical and focuses on mechanism design and proofs, not on empirical studies or the use of datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with training, validation, or test dataset splits. |
| Hardware Specification | No | The paper describes theoretical mechanisms and provides mathematical proofs; it does not report on any empirical experiments that would require hardware specifications. |
| Software Dependencies | No | The paper describes theoretical mechanisms and provides mathematical proofs; it does not report on any empirical experiments that would require software dependencies. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments, thus it does not describe an experimental setup with hyperparameters or system-level training settings. |