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].
Achieving Sustainable Cooperation in Generalized Prisoner’s Dilemma with Observation Errors
Authors: Fuuki Shigenaka, Tadashi Sekiguchi, Atsushi Iwasaki, Makoto Yokoo
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | A repeated game, where players repeatedly play the same stage game over an infinite time horizon, is a formal model for analyzing cooperation in long-term relationships and has received considerable attention in AI, multi-agent systems, and economics literature. ... Theorem 1 σL /n forms a belief-free equilibrium if and only if the following Inequality (1) holds: ... Proof. We are going to show that player i has no incentive to deviate from σL /n, regardless of the current states of other players. |
| Researcher Affiliation | Academia | 1: Kyushu University, Motooka 744, Fukuoka, Japan. {shigenaka@agent., yokoo@}inf.kyushu-u.ac.jp 2: Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto, Japan. EMAIL 3: University of Electro-Communications, Chofugaoka 1-5-1, Chofu, Tokyo, Japan. EMAIL |
| Pseudocode | No | The paper contains diagrams of Finite-State Automata (FSA) in Figure 1, but these are graphical representations of strategies and not structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is open-source or publicly available. |
| Open Datasets | No | This is a theoretical paper that uses formal models of game theory (e.g., Prisoner's Dilemma, team production problem) and mathematical proofs. It does not describe empirical experiments with datasets, and therefore, no information on dataset availability for training is provided. |
| Dataset Splits | No | This is a theoretical paper that uses formal models and mathematical proofs. It does not describe empirical experiments with datasets, and therefore, no information on training/validation/test splits is provided. |
| Hardware Specification | No | This is a theoretical paper focusing on game theory models and proofs. It does not describe any computational experiments or their hardware specifications. |
| Software Dependencies | No | This is a theoretical paper focusing on game theory models and proofs. It does not describe any computational experiments or their software dependencies. |
| Experiment Setup | No | This is a theoretical paper that analyzes game theory models. It discusses model parameters like discount factor (δ), error rate (ϵ), and punishment (α), but these are part of the theoretical model, not specific experimental setup details such as hyperparameters for training empirical models. |