Approximation-Variance Tradeoffs in Facility Location Games
Authors: Ariel Procaccia, David Wajc, Hanrui Zhang
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We revisit the well-studied problem of constructing strategyproof approximation mechanisms for facility location games, but offer a fundamentally new perspective by considering risk averse designers. Specifically, we are interested in the tradeoff between a randomized strategyproof mechanism s approximation ratio, and its variance (which has long served as a proxy for risk). When there is just one facility, we observe that the social cost objective is trivial, and derive the optimal tradeoff with respect to the maximum cost objective. When there are multiple facilities, the main challenge is the social cost objective, and we establish a surprising impossibility result: under mild assumptions, no smooth approximation-variance tradeoff exists. We also discuss the implications of our work for computational mechanism design at large. |
| Researcher Affiliation | Academia | Ariel D. Procaccia Computer Science Department Carnegie Mellon University David Wajc Computer Science Department Carnegie Mellon University Hanrui Zhang Department of Computer Science Duke University |
| Pseudocode | No | The paper describes mechanisms like GENERALIZED-LRMα in prose and mathematical notation but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any specific links to open-source code or state that code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and focuses on mathematical proofs and mechanism design. It does not involve empirical experiments with datasets, training, or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets. Therefore, there are no training/test/validation splits discussed. |
| Hardware Specification | No | The paper is theoretical and does not discuss the hardware used for any experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or versions used for computational experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments, and therefore no experimental setup details like hyperparameters or training configurations are provided. |