Ryosuke Takata, Yujin Tang, Yingtao Tian, Norihiro Maruyama, Hiroki Kojima, Takashi Ikegami,
Collective Intelligence
This study simulates the dynamics of a collection of clonal agents responding to chemical gradients (chemotaxis) to demonstrate the evolution of individual variation. To build our multi-agent simulation, we first optimized single agents that rely on a neural network to perform chemotaxis. We then constructed multi-agent simulations using clones of these evolved individuals. We find that mutual interactions lead to the emergence of behavioral variation. We also find population-level performance degradation during later evolutionary stages, despite maintained high individual performance and simplified neural architectures. This decline occurred because agents developed reduced sensory-motor coupling. This latter finding demonstrates that incentives for individual variation worked against the collective interest.
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Complexity Digest 