arXiv:2606.20485v1 Announce Type: cross Abstract: This paper develops a general framework for analyzing multi-agent systems with feedback loops between agents actions and collective observations. The framework is built on two fundamental agent-level variables: power, which measures agent influence on collective outcomes, and response functions, which determine how agents react to observations. We derive how macroscopic properties, including total power, useful power, entropy, order, fragility, and mobility, emerge from these two variables of heterogeneous agents. To study the trade off between
The increasing complexity of AI systems and multi-agent interactions necessitates refined theoretical frameworks to understand and manage emergent behaviors.
This research provides a fundamental scientific framework for understanding, predicting, and potentially controlling emergent properties in complex multi-agent systems, critical for developing advanced AI.
Our understanding of how individual agent actions aggregate into macroscopic system properties like order, fragility, and mobility will be significantly enhanced, offering new levers for system design.
- · AI researchers
- · Systems architects
- · Complex adaptive systems developers
- · Developers of simplistic AI systems
- · Organizations reliant on black-box AI
Improved design and control of sophisticated multi-agent AI systems and autonomous swarms will become possible.
Enhanced predictability and robustness of AI-driven economies and defense systems could emerge.
A deeper scientific understanding of societal and economic emergent properties influenced by individual agent behaviors might be achieved.
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Read at arXiv cs.AI