Characterising Simulation-Based Program Equilibria
Authors: Emery Cooper, Caspar Oesterheld, Vincent Conitzer
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose a generalisation to Oesterheld s (2019) ϵGroundedπBot. We prove a folk theorem for our programs in a setting with access to a shared source of randomness. We then characterise their equilibria in a setting without shared randomness. Both with and without shared randomness, we achieve a much wider range of equilibria than Oesterheld s (2019) ϵGroundedπBot. Finally, we explore the limits of simulation-based program equilibrium, showing that the Tennenholtz folk theorem cannot be attained by simulation-based programs without access to shared randomness. |
| Researcher Affiliation | Academia | Emery Cooper, Caspar Oesterheld, Vincent Conitzer Carnegie Mellon University EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Correlated ϵGroundedπi Bot... Algorithm 2 uncorrelated ϵGroundedπBot |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes conceptual game theory scenarios (e.g., Example 1, Table 1, Table 2, Table 3, Table 4) and payoff matrices, but it does not use or provide access information for any publicly available or open datasets for empirical evaluation. |
| Dataset Splits | No | Since the paper does not involve empirical experiments using datasets, there is no mention of dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments, thus no hardware specifications for running experiments are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any empirical experiments, thus no specific software dependencies with version numbers are mentioned for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments, thus no specific experimental setup details such as hyperparameters or training configurations are provided. |