进化群体结合复杂内部表示与简单输出
Evolved Collectives Combine Complex Internal Representations with Simple Outputs
AI总结：
研究在感官和行动约束下，进化群体中浅层神经控制器如何通过增加隐藏层复杂性和线性化输出映射来实现有序集体行为，揭示了表征复杂性与行动线性化共存的控制器组织。
摘要：
集体智能源于有限信息智能体之间的局部交互，但内部控制器组织与涌现集体秩序之间的关系仍不清楚。在这里，我们研究了在明确感官和行动约束下具有浅层神经控制器的进化群体，并比较了3024种条件下的集体秩序与隐藏层复杂性和输出非线性。在这些约束下，最有序的机制表现出两种同时存在且看似矛盾的效果：隐藏层复杂性增加，而有效输出映射变得更加线性。循环集体行为的多样性在控制参数上呈非单调变化，模式丰富性由参数特定的权衡塑造，而非单一通用约束最优。未进化的控制表明，输出线性化在无适应的情况下持续存在，而隐藏复杂性关系依赖于优化。这两种效应分别与必要复杂性和生态理性定律一致，表明适应性集体智能可以通过一种分区控制器组织产生，其中表征复杂性和行动级线性化在同一系统中共存。
英文摘要：
Collective intelligence emerges from local interactions among agents with limited information, yet how internal controller organization relates to emergent collective order remains unclear. Here, we study evolved swarms with shallow neural controllers under explicit sensory and actuation constraints and compare collective order with hidden-layer complexity and output nonlinearity across 3024 conditions. Under these constraints, the most ordered regimes exhibit two simultaneous and seemingly contrasting effects: hidden-layer complexity increases, while the effective output mapping becomes more linear. The diversity of recurrent collective behaviors varies nonmonotonically across the control parameters, with pattern richness shaped by parameter-specific tradeoffs rather than a single generic constraint optimum. Unevolved controls show that output linearization persists without adaptation, whereas the hidden-complexity relation depends on optimization. These two effects are respectively consistent with the law of requisite complexity and ecological rationality, suggesting that adaptive collective intelligence can arise through a partitioned controller organization in which representational complexity and action-level linearization coexist within the same system.
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