The ABC of Heuristics: Approximate Bayesian Computation as a Framework for Heuristic Inference

Heuristics have long been viewed as simple decision rules that sacrifice optimality for cognitive efficiency. However, this perspective fails to fully account for their effectiveness in complex, real-world environments. This paper proposes a reinterpretation of heuristics as realizations of likelihood-free approximation to Bayesian inference (LFI), bridging the gap between heuristic and normative approaches to decision-making. We argue that many heuristics can be understood as implementations of Approximate Bayesian Computation (ABC), where observed data are compared to prior predictions via summary statistics (a form of LFI). This view situates heuristics within a probabilistic framework, explaining how they can represent uncertainty and adapt to environmental structure. Our approach addresses three key challenges in the field: It relaxes strong assumptions about cue-target relationships in heuristic models, accounts for implicit uncertainty representation in heuristic decision-making, and invites a more appropriate benchmark for evaluating heuristics' performance. To demonstrate the practical applicability of our framework, we present two case studies --- a classic cue-based decision task and an ecological causal learning task. These show that an ABC model using summary statistics can well approximate the benchmark performance provided by unconstrained Bayesian inference, explain the apparent emergence of different heuristics given environmental changes, and capture human behaviors better than a rational Bayesian benchmark without introducing new mysteries. By unifying heuristics and Bayesian inference through the lens of likelihood-free methods, this work provides a more nuanced understanding of human decision-making, and provides a bridge to the literature on inference-by-sampling.