Document Type
Article
Publication Date
7-29-2025
Abstract
In this paper, we introduce a revealed strategy method to capture heterogeneous behavioral patterns in repeated public goods experiments. This method identifies an individual’s strategic type—a stable decision-making tendency in interactive settings—by analyzing behavioral profiles. We conduct a repeated public goods game to collect participants’ behavioral data and then apply both k-medians and hierarchical clustering algorithms to classify them into five distinctive types: free riders, strong cooperators, above-average conditional cooperators, below-average conditional cooperators, and hump-shaped players. These classifications are then used to construct and calibrate an agent-based simulation model. Our simulation results suggest that policies targeting how conditional cooperators respond to perceived social norms may effectively promote cooperation. The revealed strategy method offers a useful tool for experimenters to better characterize group composition and understand strategic interactions, while also providing empirical validation for agent-based models and theoretical studies.
Keywords
agent-based simulation, behavioral heterogeneity, cooperation, machine learning, social dilemma
Language
English
Publication Title
Journal of Economic Interaction and Coordination
Rights
© The Author(s) 2025. This is an Open Access work distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Wang, S. Classifying heterogeneous cooperation in social dilemmas: experimental evidence and simulation insights. J Econ Interact Coord 20, 925–958 (2025).https://doi.org/10.1007/s11403-025-00451-55
Manuscript Version
Final Publisher Version