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The HoneyComb Paradigm for Research on Collective Human Behavior
Published on: January 19, 2019
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The entropic basis of collective behaviour
Richard P Mann1, Roman Garnett2
1Professorship of Computational Social Science, ETH Zurich, Zurich, Switzerland Department of Mathematics, Uppsala University, Uppsala, Sweden mannr@ethz.ch.
Journal of the Royal Society, Interface
|April 3, 2015
Summary
Intelligent agents
Area of Science:
- Collective behavior
- Agent-based modeling
- Theoretical neuroscience
Background:
- Social interactions in human and animal groups exhibit complex patterns.
- Understanding the underlying principles of collective intelligence is a significant challenge.
Purpose of the Study:
- To develop a general abstract model for the future lives of intelligent agents.
- To investigate the causal entropic principle as a predictor of social interactions.
- To derive social interaction rules based on maximum entropy principles.
Main Methods:
- Development of a general abstract model for agent decision-making and future possibilities.
- Application of the causal entropic principle to predict group behavior.
- Derivation of social interaction rules for discrete and continuous decision spaces.
Main Results:
- The causal entropic principle predicts observed features of social interactions in groups.
- Group cohesion is a strategy for agents to maximize future path entropy, leading to collective intelligence.
- Social interactions are predicted to follow Weber's law, aligning with neurological findings.
Conclusions:
- The causal entropic principle offers a unified framework for understanding collective behavior.
- Agent cohesion and adherence to Weber's law are key to emergent collective intelligence.
- This model provides a novel basis for simulating social forces in collective behavior studies.
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