Negative Coordination in Human-AI Collectives: Developing Frameworks for Constructive Avoidance

While existing research focuses on positive coordination mechanisms (what groups should do together), this would be the first systematic study of negative coordination in collective intelligence—coordinated avoidance of counterproductive interactions. Building on Wu et al.'s insight that group coordination catalyzes intelligence, we'd explore how coordinated avoidance of certain interaction patterns might be equally important. In human-AI collectives like Burton et al.'s LLM-enhanced groups or Kaomea's law firm model, this could mean defining "exclusion zones" where AI and humans shouldn't interact, or "temporal coordination" where certain activities are deliberately separated. We'd develop a theoretical framework distinguishing productive negative coordination from mere neglect, then test interventions in mixed human-AI teams solving complex problems. This challenges the fundamental assumption in collective intelligence research that more coordination is always better—a perspective supported by Gupta et al.'s findings about negative nudge effects. The innovation lies in creating a formal theory of when and how groups should coordinate to avoid certain interactions rather than pursue them. This could transform how we design human-AI systems by recognizing that sometimes the smartest collective decision is what not to do together.

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