On the Robustness of Mechanism Design under Total Variation Distance
Authors: Anuran Makur, Marios Mertzanidis, Alexandros Psomas, Athina Terzoglou
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the problem of designing mechanisms when agents valuation functions are drawn from unknown and correlated prior distributions. In particular, we are given a prior distribution D, and we are interested in designing a (truthful) mechanism that has good performance for all true distributions that are close to D in Total Variation (TV) distance. We show that DSIC and BIC mechanisms in this setting are strongly robust with respect to TV distance, for any bounded objective function O, extending a recent result of Brustle et al. ([BCD20], EC 2020). At the heart of our result is a fundamental duality property of total variation distance. |
| Researcher Affiliation | Academia | Anuran Makur Purdue University amakur@purdue.edu Marios Mertzanidis Purdue University mmertzan@purdue.edu Alexandros Psomas Purdue University apsomas@cs.purdue.edu Athina Terzoglou Purdue University aterzogl@purdue.edu |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. Its content is purely theoretical with mathematical definitions, lemmas, and theorems. |
| Open Source Code | No | The paper is theoretical and does not mention releasing any source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or refer to publicly available datasets for training or empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical data splits (train/validation/test). |
| Hardware Specification | No | The paper is purely theoretical and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is purely theoretical and does not list any software dependencies with version numbers. |
| Experiment Setup | No | The paper is purely theoretical and does not describe any experimental setup details, hyperparameters, or training configurations. |