Heuristic Reasoning in AI: Instrumental Use and Mimetic Absorption

TL;DR

This paper introduces a new perspective on AI reasoning, emphasizing heuristic strategies that balance accuracy and effort, and demonstrates how AI systems emulate human cognitive trade-offs through experiments.

Contribution

It proposes a novel framework distinguishing instrumental heuristics from mimetic absorption, supported by experiments showing adaptive resource management in AI reasoning.

Findings

AI systems balance accuracy and effort through heuristics

Heuristics enable AI to emulate human cognitive trade-offs

Experimental results demonstrate adaptive switching between logical processing and heuristics

Abstract

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.