Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
6-2023
Abstract
In this paper, we propose to enhance the state-of-the-art quantal cognitive hierarchy (QCH) model with iterative population learning (IPL) to estimate the empirical distribution of agents’ reasoning levels and fit human agents’ behavioral data. We apply our approach to a real-world dataset from the Swedish lowest unique positive integer (LUPI) game and show that our proposed approach outperforms the theoretical Poisson Nash equilibrium predictions and the QCH approach by 49.8% and 46.6% in Wasserstein distance respectively. Our approach also allows us to explicitly measure an agent’s reasoning level distribution, which is not previously possible.
Keywords
behavioral game theory, cognitive hierarchy model, quantal cognitive hierarchy model, lowest unique positive integer game
Discipline
Artificial Intelligence and Robotics | Databases and Information Systems
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems, London, 2023 May 29 - June 2
First Page
2718
Last Page
2720
Identifier
10.5555/3545946.3599054
Publisher
ACM
City or Country
New York
Citation
XU, Yuhong; CHENG, Shih-Fen; and CHEN, Xinyu.
Improving quantal cognitive hierarchy model through iterative population learning. (2023). Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems, London, 2023 May 29 - June 2. 2718-2720.
Available at: https://ink.library.smu.edu.sg/sis_research/8071
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.5555/3545946.3599054