LLM-Induced Coordination Cascades: When Artificial Intelligence Disrupts Human Collective Intelligence

While Burton et al. explore how LLMs can enhance collective intelligence, they don't address the flip side: how LLMs might disrupt existing coordination mechanisms. This research would examine cases where LLM participation leads to coordination cascades—chain reactions of misalignment that degrade group performance. For instance, in legal settings like Kaomea's "super-intelligent law firm," LLMs might introduce subtle biases that compound across human interactions. We'd develop a computational model combining network theory with behavioral economics to trace these cascades, then test predictions in mixed human-AI teams. The novelty lies in viewing LLMs not just as amplifiers of collective intelligence but as potential coordination disruptors—a perspective absent in current literature. This bridges Burton et al.'s forward-looking vision with Gupta et al.'s findings about unexpected negative outcomes, creating a comprehensive theory of AI-mediated coordination dynamics. The impact could be transformative for organizations deploying AI in collaborative settings.

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