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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
An Adversarial Interpretation of Information-Theoretic Bounded Rationality
Authors: Pedro Ortega, Daniel Lee
AAAI 2014 | Venue PDF | LLM Run Details | Input Tokens: 14,060 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,328 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Here, we show that a single-agent free energy optimization is equivalent to a game between the agent and an imaginary adversary. The adversary can, by paying an exponential penalty, generate costs that diminish the decision maker s payoffs. It turns out that the optimal strategy of the adversary consists in choosing costs so as to render the decision maker indifferent among its choices, which is a deο¬ning property of a Nash equilibrium, thus tightening the connection between free energy optimization and game theory. |
| Researcher Affiliation | Academia | Pedro A. Ortega and Daniel D. Lee School of Engineering and Applied Sciences University of Pennsylvania Philadelphia, PA 19104, USA EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | No statement about open-source code availability or links to a code repository were found. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training; thus, no information about public dataset availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for validation. |
| 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 specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |