Will AI Trade? A Computational Inversion of the No-Trade Theorem
This work addresses the problem of understanding trade dynamics for AI agents in computational economics, offering a novel inversion of the no-trade theorem.
The paper investigates whether AI agents with bounded computational rationality can engage in trade under common beliefs, finding that identical computational power leads to instability and persistent strategic adjustments, creating a more unpredictable trade environment than classic models.
Classic no-trade theorems attribute trade to heterogeneous beliefs. We re-examine this conclusion for AI agents, asking if trade can arise from computational limitations, under common beliefs. We model agents' bounded computational rationality within an unfolding game framework, where computational power determines the complexity of its strategy. Our central finding inverts the classic paradigm: a stable no-trade outcome (Nash equilibrium) is reached only when "almost rational" agents have slightly different computational power. Paradoxically, when agents possess identical power, they may fail to converge to equilibrium, resulting in persistent strategic adjustments that constitute a form of trade. This instability is exacerbated if agents can strategically under-utilize their computational resources, which eliminates any chance of equilibrium in Matching Pennies scenarios. Our results suggest that the inherent computational limitations of AI agents can lead to situations where equilibrium is not reached, creating a more lively and unpredictable trade environment than traditional models would predict.