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Cohesion is the attraction between molecules of the same type, such as water molecules. Water molecules have an overall neutral charge but are polar molecule. An oxygen atom in one water molecule has a partial negative charge that can bind to a hydrogen atom with a partial positive charge in a second water molecule, forming a hydrogen bond. Each water molecule can form up to four hydrogen bonds with other water molecules. Hydrogen bonds are responsible for water's cohesive nature.
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The HoneyComb Paradigm for Research on Collective Human Behavior
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Multispecies Cohesion: Humans, Machinery, AI, and Beyond.

Frank Yingjie Huo1, Pedro D Manrique1, Neil F Johnson1

  • 1Physics Department, <a href="https://ror.org/00y4zzh67">George Washington University</a>, Washington, DC 20052, USA.

Physical Review Letters
|January 3, 2025
PubMed
Summary
This summary is machine-generated.

A new aggregation model explains how diverse systems of humans, machines, and AI can rapidly form cohesive behaviors. This theory helps predict and control emergent phenomena in complex interacting systems.

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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.