Approximate Equilibrium and Incentivizing Social Coordination
Authors: Elliot Anshelevich, Shreyas Sekar
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study techniques to incentivize self-interested agents to form socially desirable solutions in scenarios where they benefit from mutual coordination. Towards this end, we consider coordination games where agents have different intrinsic preferences but they stand to gain if others choose the same strategy as them. For non-trivial versions of our game, stable solutions like Nash Equilibrium may not exist, or may be socially inefficient even when they do exist. This motivates us to focus on designing efficient algorithms to compute (almost) stable solutions like Approximate Equilibrium that can be realized if agents are provided some additional incentives. |
| Researcher Affiliation | Academia | Elliot Anshelevich Rensselaer Polytechnic Institute Troy, NY-12180, USA eanshel@cs.rpi.edu. Shreyas Sekar Rensselaer Polytechnic Institute Troy, NY-12180, USA sekars@rpi.edu. |
| Pseudocode | Yes | Algorithm 1: Pick a strategy and allow all players who want to deviate from this strategy to perform bestresponse until no player wants to deviate. Now allow all players who want to deviate from the other strategy to perform best-response. |
| Open Source Code | No | The paper does not provide any explicit statements about making its source code available or links to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and analysis, not empirical studies that would involve datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data; therefore, it does not specify training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and focuses on algorithm design and analysis, not empirical experiments that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on computational experiments. Therefore, it does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations, as it does not conduct empirical experiments. |