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New model explores adaptive and maladaptive behaviors via evolutionary mismatch

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an Evolutionary Recurrent Decision Model (ERDM), a computational framework using reinforcement learning to explore how evolutionary mismatch and bounded rationality lead to adaptive and maladaptive behaviors. The model simulates agents in various environments, learning through competing rewards abstracted from survival metrics. A study using ERDM demonstrated that distinct strategies like learned helplessness, avoidance, and aggression can emerge naturally, suggesting that many aspects of psychopathology may stem from cognitive systems operating under environmental mismatch. AI

IMPACT This model offers a new computational tool for understanding the roots of maladaptive behaviors, potentially informing future AI safety and alignment research.

RANK_REASON The cluster contains an academic paper detailing a new computational model. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New model explores adaptive and maladaptive behaviors via evolutionary mismatch

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The cluster contains an academic paper detailing a new computational model. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Andrew Hu ·

    Evolutionary Recurrent Decision Model in Developing Adaptive and Maladaptive Behaviors

    arXiv:2608.23932v1 Announce Type: new 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 an…

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