Randomized trial investigates learning dynamics in repeated games, suggesting that instability informs strategic behavior.
Key Points
This paper aims to reinterpret learning dynamics in repeated games by considering bounded rationality and adaptive behavior.
Revisits traditional models like Q-learning and replicator dynamics through bounded rationality frameworks.
Utilizes information theory and dynamical systems analysis, including entropy measures and Lyapunov exponents for quantitative analysis of instability.
Proposes operational frameworks for analyzing learning behaviors under realistic constraints.
Identifies instability and oscillation as natural adaptive processes rather than failures of learning.
Suggests non-convergent trajectories may reveal insights about strategic experimentation and adaptation.
Finds that treating instability as informative can enhance understanding of adaptive flexibility in complex systems.
Cite This Study
Pratyush Mahadevaiah (2026) studied this question.