Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Beyond Symmetry in Repeated Games with Restarts
Authors: Henry Fleischmann, Kiriaki Fragkia, Ratip Emin Berker
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We characterize which goal strategies players can be incentivized to play in equilibrium, and we consider the computational problem of finding such sequences of actions with minimal cost for the agents. We show that this problem is NP-hard in general. However, when the goal sequence maximizes social welfare, we give a pseudo-polynomial time algorithm. ... Theorem 21. MINHAZING is (weakly) NP-hard. ... Theorem 27. MAXWELFAREMINHAZING(Γ, γ, B) is solvable in O(poly(n)r2B2) time and O(r2B2) space, where |A| = n and γ Ar. |
| Researcher Affiliation | Academia | 1Carnegie Mellon University 2Foundations of Cooperative AI Lab (FOCAL) EMAIL |
| Pseudocode | Yes | Algorithm 1 Dynamic Programming Algorithm for MAXWELFAREMINHAZING |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that source code for the described methodology is publicly available. Phrases like 'We release our code...' or links to repositories are absent. |
| Open Datasets | No | The paper defines small game matrices (e.g., Table 1: Symmetric repeated game, Table 2: Group Project) for illustrative purposes and does not mention using or providing public datasets for empirical evaluation. |
| Dataset Splits | No | The paper focuses on theoretical analysis and algorithm design and does not involve empirical experiments with datasets. Therefore, no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical in nature, focusing on game-theoretic analysis and algorithm design. It does not describe any experimental setup or results that would require specific hardware specifications. |
| Software Dependencies | No | The paper presents theoretical concepts and algorithms. It does not describe any specific implementation or provide details about software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not include any empirical experiments. Therefore, it does not describe specific experimental setup details, hyperparameters, or training configurations. |