Evolutionary Recurrent Decision Model in Developing Adaptive and Maladaptive Behaviors

Andrew Hu

cs.AIAug 25, 2026

Evolutionary Recurrent Decision Model in Developing Adaptive and Maladaptive Behaviors

Authors: Andrew Hu

Abstract

This study introduces the evolutionarily recurrent decision model (ERDM), a computational reinforcement learning framework designed to examine how evolutionary mismatch, bounded rationality, and satisficing contribute to adaptive and maladaptive behavior. ERDM simulates agents across evolutionary recurrent environments, including threat, prey/goal-pursuits, and alliances. Agents learn through competing rewards abstracted from survival metrics. A validity study under varying adverse childhood experiences demonstrates that distinct adaptive and maladaptive strategies, such as learned helplessness, avoidance, healthy relationships, and aggression, emerge naturally without being hardwired. These results align with empirical literature, showcasing ecological validity. The results suggest that many psychopathology-relevant aspects may be interpreted as bounded cognitive systems operating under modern-ancestral environmental mismatch, positioning ERDM as a key computational cognitive tool that can be extended to other studies.

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Meta-learning ecological priors from large language models explains human learning and decision making

Akshay K. Jagadish, Mirko Thalmann, Julian Coda-Forno, Marcel Binz+1

Institute for Human-Centered AI, Helmholtz Computational Health Center, Munich, Germany · Computational Principles of Intelligence, Max Planck Institute for Biological Cybernetics, Tübingen, Germany · Eberhard Karls University of Tübingen, Tübingen, Germany+1

Computational Cognitive Modeling Meta-Learning Decision-Making under Uncertainty

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Human cognition is profoundly shaped by the environments in which it unfolds. Yet, it remains an open question whether learning and decision making can be explained as a principled adaptation to the statistical structure of real-world tasks. We introduce ecologically rational analysis, a computational framework that unifies the normative foundations of rational analysis with ecological grounding. Leveraging large language models to generate ecologically valid cognitive tasks at scale, and using meta-learning to derive rational models optimized for these environments, we develop a new class of learning algorithms: Ecologically Rational Meta-learned Inference (ERMI). ERMI internalizes the statistical regularities of naturalistic problem spaces and adapts flexibly to novel situations, without requiring hand-crafted heuristics or explicit parameter updates. We show that ERMI captures human behavior across 15 experiments spanning function learning, category learning, and decision making, outperforming several established cognitive models in trial-by-trial prediction. Our results suggest that much of human cognition may reflect adaptive alignment to the ecological structure of the problems we encounter in everyday life.

Abstract

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