Heuristic Reasoning in AI: Instrumental Use and Mimetic Absorption

Heuristics in human cognition--cognitive shortcuts that facilitate mental processing--are situated within contrasting narratives. Simon's notion of bounded rationality (Simon 1955) casts heuristics as tools that enable navigation in environments too complex for the unaided mind. When aligned with psychological capacities and grounded in ecological rationality, a parallel view advocates for a'fast and frugal' approach to cognition (Gigerenzer and Goldstein 1996), where heuristics serve as scaffolds in decisions that might prove unnecessary, intractable, or suboptimal if reliant solely on analytic processing (Simon 1956). In contrast, a'heuristics as bias' view frames heuristics as leading to systematic and predictable deviations from optimal decision-making, given standards of complete information processing (Gilovich et al. 2002, Tversky and Kahneman 1974). Implicit in the latter perspective is the assumed feasibility of complete analytic processing--the use of a shortcut only yields a suboptimal outcome (i.e., biased decision-making leads to suboptimal outcomes) if the optimal is achievable; clearly in situations where analytic processing is infeasible, a heuristic can yield a better decision than random chance. Drawing from human cognition, our paper proposes a novel program of heuristic reasoning as it applies to artificial intelligence (AI) cognition.