Heuristic Reasoning in AI: Instrumental Use and Mimetic Absorption
This work addresses the problem of modeling AI cognition more realistically for researchers in AI and cognitive science, though it appears incremental in extending existing theories of bounded rationality.
The paper tackled the problem of understanding AI cognition by proposing heuristic reasoning, distinguishing instrumental use and mimetic absorption, and found through experiments like the Linda problem and Beauty Contest game that AIs adaptively balance accuracy and effort, emulating human cognitive principles.
Deviating from conventional perspectives that frame artificial intelligence (AI) systems solely as logic emulators, we propose a novel program of heuristic reasoning. We distinguish between the 'instrumental' use of heuristics to match resources with objectives, and 'mimetic absorption,' whereby heuristics manifest randomly and universally. 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 maximizing accuracy and reducing effort that shape the conditions under which AIs transition between exhaustive logical processing and the use of cognitive shortcuts (heuristics). We provide evidence that AIs manifest 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. Our findings reveal a nuanced picture of AI cognition, where trade-offs between resources and objectives lead to the emulation of biological systems, especially human cognition, despite AIs being designed without a sense of self and lacking introspective capabilities.