Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions
Authors: Nivasini Ananthakrishnan, Nika Haghtalab, Chara Podimata, Kunhe Yang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Overall, our findings suggest that repeated strategic interactions alone cannot facilitate learning effectively enough to earn an uninformed player her Stackelberg value. and Our current setup provides information-theoretic results |
| Researcher Affiliation | Collaboration | 1UC Berkeley, {nivasini,nika,kunheyang}@berkeley.edu 2MIT & Archimedes AI, podimata@mit.edu |
| Pseudocode | No | The paper describes algorithms conceptually but does not include any labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or links to a code repository. |
| Open Datasets | No | This paper is theoretical and does not utilize datasets for training or evaluation. |
| Dataset Splits | No | This paper is theoretical and does not discuss dataset splits for validation. |
| Hardware Specification | No | This paper is theoretical and does not describe experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | This paper is theoretical and does not describe experiments, thus no software dependencies with version numbers are listed. |
| Experiment Setup | No | This paper is theoretical and does not describe empirical experiments, therefore no experimental setup details like hyperparameters are provided. |