Multispecies Cohesion: Humans, Machinery, AI, and Beyond

Area of Science:

• Complex Systems Science
• Computational Social Science
• Statistical Physics

Background:

• The 2024 technology meltdown underscores the need to understand emergent behaviors in human-machine systems.
• Predicting and controlling large-scale cohesive phenomena in diverse interacting entities is crucial.

Purpose of the Study:

• To develop a theoretical framework for predicting emergent cohesive behaviors in systems with diverse interacting entities.
• To provide a microscopic explanation for observed anomalous nonlinear growth in real-world systems.

Main Methods:

• Introduction of a novel multidimensional aggregation model accounting for inter- and intraspecies diversity.
• Derivation of exact analytic solutions for time to cohesion and growth of cohesion.
• Generalization of solutions for an arbitrary number of species.

Main Results:

• The model reproduces anomalous nonlinear growth features observed in current real-world systems.
• Analytic solutions provide a microscopic explanation for emergent cohesion dynamics.
• The theory predicts that increased interaction intensifies both positive and negative emergent "surprises".

Conclusions:

• The developed aggregation model offers a rigorous approach to understanding and controlling emergent behaviors in complex systems.
• Future systems with greater human, machine, and AI interaction will experience more frequent and intense emergent phenomena.
• This work provides a foundation for anticipating and managing future large-scale cohesive behaviors.