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The Dynamics of Social Interaction Among Evolved Model Agents
Haily Merritt1, Gabriel J Severino2, Eduardo J Izquierdo3
1Indiana University Bloomington Cognitive Science Program Luddy School of Informatics, Computing, and Engineering.
Artificial Life
|November 21, 2023
Summary
This study challenges individualistic social cognition analysis using perceptual crossing simulations. Findings highlight the importance of dynamic, non-clonal interactions in artificial sociality.
Area of Science:
- Artificial intelligence
- Computational social science
- Cognitive science
Background:
- Methodological individualism is a dominant paradigm in social cognition analysis.
- Perceptual crossing simulations offer a platform to study social interaction dynamics.
- Existing simulations often lack rigorous testing and dynamic analysis.
Purpose of the Study:
- To challenge methodological individualism in social cognition through advanced simulation.
- To identify robust interaction circuits in artificial agents.
- To explore the role of nonequilibrium dynamics and non-clonal interactions in sociality.
Main Methods:
- Evolving and systematically testing artificial agents in rigorous conditions.
- Transforming sensor input from discrete to continuous for bifurcation analysis.
- Examining agent performance with diverse partners and decoy stimuli.
Main Results:
- Identified 26
- robust circuits
- with high and generalizable performance.
- Discovered that nonequilibrium dynamics are crucial for maintaining interaction.
- Observed variable nonclonal performance not predicted by genotypic distance.
Conclusions:
- Dynamical and nonclonal analyses are essential for understanding simulated sociality.
- Simulation studies provide valuable insights into social interactions, complementing human studies.
- Emphasizes the need for dialogue between artificial and human-based social cognition research.
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