Truthful Univariate Estimators
Authors: Ioannis Caragiannis, Ariel Procaccia, Nisarg Shah
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We revisit the classic problem of estimating the population mean of an unknown singledimensional distribution from samples, taking a game-theoretic viewpoint. In our setting, samples are supplied by strategic agents, who wish to pull the estimate as close as possible to their own value. In this setting, the sample mean gives rise to manipulation opportunities, whereas the sample median does not. Our key question is whether the sample median is the best (in terms of mean squared error) truthful estimator of the population mean. We show that when the underlying distribution is symmetric, there are truthful estimators that dominate the median. Our main result is a characterization of worst-case optimal truthful estimators, which provably outperform the median, for possibly asymmetric distributions with bounded support. |
| Researcher Affiliation | Academia | Ioannis Caragiannis CARAGIAN@CEID.UPATRAS.GR University of Patras, Greece Ariel D. Procaccia ARIELPRO@CS.CMU.EDU Carnegie Mellon University, USA Nisarg Shah NKSHAH@CS.CMU.EDU Carnegie Mellon University, USA |
| Pseudocode | No | The paper is theoretical and does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information about open-source code for the described methodology. |
| Open Datasets | No | The paper focuses on theoretical analysis of estimators for distributions, not empirical training on specific datasets. Therefore, it does not provide concrete access information for a public dataset for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits (train/validation/test) for reproduction. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers used for experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |