AI交易并非全因认知差异，计算能力不均反催生持续博弈。
Will AI Trade? A Computational Inversion of the No-Trade Theorem
- 用动态博弈建模AI的计算理性极限，以算力决定策略复杂度。
- 算力相近时无法达成稳定均衡，反而产生持续策略调整的交易现象。
- 适合研究AI市场行为、博弈机制与非理性交易的学者参考。
经典无交易定理认为交易源于信念差异。本文重新审视人工智能代理在共同信念下的交易可能，探究计算限制是否可引发交易。通过在展开式博弈框架中建模代理的有限计算理性，我们发现：当代理几乎理性且计算能力略有差异时，才可能达到稳定的无交易均衡（纳什均衡）。令人意外的是，当代理计算能力完全相同时，反而难以收敛至均衡，导致持续的战略调整，形成一种新型交易。若代理可主动低估自身算力，则在匹配硬币等场景下根本无法达到均衡。结果表明，AI代理的内在计算局限可能导致均衡无法达成，从而产生比传统模型预测更活跃、更不可预测的交易环境。
原文摘要 · Abstract (English)
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.
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