We propose a novel program of heuristic reasoning within artificial intelligence (AI) systems. Through a series of innovative experiments, including variations of the classic Linda problem and a novel application of the Beauty Contest game, we uncover trade-offs between accuracy maximization and effort reduction that shape the conditions under which AIs transition between exhaustive logical processing and the use of cognitive shortcuts (heuristics). We distinguish between the 'instrumental' use of heuristics to match resources with objectives, and 'mimetic absorption,' whereby heuristics are learned from humans, and manifest randomly and universally. We provide evidence that AI, despite lacking intrinsic goals or self-awareness, manifests an adaptive balancing of precision and efficiency, consistent with principles of resource-rational human cognition as explicated in classical theories of bounded rationality and dual-process theory.

翻译：我们提出了一项人工智能（AI）系统内的新启发式推理研究计划。通过一系列创新实验，包括经典莉达问题的变体及“选美比赛”博弈的新颖应用，我们揭示了准确度最大化与努力程度降低之间的权衡，该权衡塑造了AI在穷举逻辑处理与认知捷径（启发式）使用之间转换的条件。我们区分了启发式的“工具性”使用（即匹配资源与目标）与“模仿性吸收”（即启发式从人类处习得，并随机、普遍地显现）。我们提供证据表明，尽管AI缺乏内在目标或自我意识，但其表现出精准性与效率之间的适应性平衡，这与经典有限理性理论和双系统理论中阐述的资源理性人类认知原则相一致。