The Adjusted Winner Procedure: Characterizations and Equilibria
Authors: Haris Aziz, Simina Brânzei, Aris Filos-Ratsikas, Søren Kristoffer Stiil Frederiksen
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show that Adjusted Winner admits several elegant characterizations, which further shed light on the outcomes reached with strategic agents. We find that the procedure may not admit pure Nash equilibria in either the discrete or continuous variants, but is guaranteed to have ϵ-Nash equilibria for each ϵ > 0. Moreover, under informed tiebreaking, exact pure Nash equilibria always exist, are Pareto optimal, and their social welfare is at least 3/4 of the optimal. |
| Researcher Affiliation | Academia | Haris Aziz NICTA and UNSW, Sydney, Australia haris.aziz@nicta.com.au Simina Brˆanzei Aarhus University, Denmark simina@cs.au.dk Aris Filos-Ratsikas Aarhus University, Denmark filosra@cs.au.dk Søren Kristoffer Stiil Frederiksen Aarhus University, Denmark ssf@cs.au.dk |
| Pseudocode | No | The paper describes the Adjusted Winner procedure in text but does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper refers to "The Adjusted Winner website3" for examples, but does not provide a link to source code for the methodology presented in this paper. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for validation or training. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |