Learning in Online Principal-Agent Interactions: The Power of Menus
Authors: Minbiao Han, Michael Albert, Haifeng Xu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide a thorough investigation of several online principal-agent problem settings and characterize their sample complexities, accompanied by the corresponding algorithms we have developed. and We propose the following algorithm 1 to learn the agent s true type 2 in log | | rounds. |
| Researcher Affiliation | Academia | 1Department of Computer Science, The University of Chicago 2Darden Business School, University of Virginia |
| Pseudocode | Yes | Algorithm 1: LEARNING-VIA-MENU and Algorithm 2: LEARNING-VIA-SINGLE-STRATEGY |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments with datasets that would require providing access information for training data. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets that would require providing specific dataset split information for validation. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameter values or training configurations. |