How Intelligence Emerges: A Minimal Theory of Dynamic Adaptive Coordination

A new theoretical paper by researcher Stefano Grassi, titled 'How Intelligence Emerges: A Minimal Theory of Dynamic Adaptive Coordination,' proposes a radical shift in understanding intelligence. Instead of viewing it as the product of agents optimizing a central goal or learning individually, the framework models it as a structural property of a coupled dynamical system. This system consists of three core components: adaptive agents, a persistent environment that stores coordination signals, and a distributed incentive field that transmits those signals locally. The result is a recursively closed feedback architecture where coordination emerges from the dynamics themselves.

The paper establishes three key structural results. First, it shows the system can maintain viability within a bounded region without needing to achieve global optimality. Second, it proves that when incentives depend on environmental memory, the dynamics cannot be reduced to a simple, static global objective function. Third, it demonstrates that this environmental persistence makes the system's behavior inherently sensitive to its history. A minimal linear model illustrates how coupling, persistence, and energy dissipation govern stability and potential oscillatory behavior.

This work provides a formal, mathematical foundation for emergent intelligence in multi-agent systems, which includes applications like swarm robotics, decentralized AI, and economic networks. It challenges the prevailing assumptions in AI and economics that intelligence requires centralized design, rational expectations, or welfare maximization, offering a new lens focused on the dynamics of interaction.