Heuristic Reasoning in AI: Instrumental Use and Mimetic Absorption

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.