Identifying Latent Intentions via Inverse Reinforcement Learning in Repeated Linear Public Good Games

2026-01-13

AI Summary

Standard theories struggle to account for the pronounced fluctuations in contribution behavior observed in repeated public goods games, particularly due to limitations in modeling heterogeneous social preferences across multiple rounds of interaction. This study proposes a novel framework integrating dynamic time warping (DTW) clustering with hierarchical inverse Q-learning to uncover latent patterns of strategic intent switching through behavioral trajectory analysis. The work identifies, for the first time, a distinct “switcher” type comprising 21.4% of participants, whose frequent oscillations between cooperation and defection have previously eluded effective modeling. These findings support a new mechanism suggesting that intention volatility can paradoxically sustain cooperation. The approach successfully discriminates among persistent cooperators, free riders, and switchers, indicating that tolerance toward transient defection may help prevent the collapse of cooperative norms.

Technology Category

• Game Theory and Economic Paradigms: Cooperative Game Theory
• Multiagent Systems: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Behavior in repeated public goods games continues to challenge standard theory: heterogeneous social preferences can explain first-round contributions, but not the substantial volatility observed across repeated interactions. Using 50,390 decisions from 2,938 participants, we introduce two methodological advances to address this gap. First, we cluster behavioral trajectories by their temporal shape using Dynamic Time Warping, yielding distinct and theoretically interpretable behavioral types. Second, we apply a hierarchical inverse Q-learning framework that models decisions as discrete switches between latent cooperative and defective intentions. This approach reveals a large (21.4%) and previously unmodeled behavioral type -- Switchers -- who frequently reverse intentions rather than commit to stable strategies. At the same time, the framework recovers canonical strategic behaviors such as persistent cooperation and free-riding. Substantively, recognizing intentional volatility helps sustain cooperation: brief defections by Switchers often reverse, so strategic patience can prevent unnecessary breakdowns.

Problem

• public goods games
• behavioral volatility
• latent intentions
• repeated interactions
• cooperative behavior

Innovation

• Inverse Reinforcement Learning
• Dynamic Time Warping
• Behavioral Typology
• Intention Switching
• Public Goods Game