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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
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Conflict and convention in dynamic networks.

Michael Foley1, Patrick Forber2, Rory Smead3

  • 1Network Science Institute, Northeastern University, Boston, MA, USA.

Journal of the Royal Society, Interface
|March 23, 2018
PubMed
Summary
This summary is machine-generated.

Dynamic networks help resolve conflict by evolving conventions, often favoring host-guest norms over ownership norms. This network-driven learning shapes social structures differently than traditional models.

Keywords:
conventionevolutionary dynamicsgame theorynetworksreinforcement learning

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Area of Science:

  • Evolutionary Game Theory
  • Network Science
  • Behavioral Economics

Background:

  • Conflict resolution in games like hawk-dove often relies on conventions, such as correlated equilibria.
  • Dynamic social networks offer an alternative mechanism for conflict resolution through network connections, rather than strategy changes.

Purpose of the Study:

  • To model the emergence of conventions as correlated equilibria within dynamic social networks.
  • To investigate how behavioral strategies coevolve with network structures.
  • To explain the origins and robustness of social conventions.

Main Methods:

  • Agent-based modeling of dynamic networks.
  • Simulation of evolutionary game theory scenarios (snowdrift, hawk-dove, chicken).
  • Analysis of strategy coevolution and network structure formation.

Main Results:

  • Dynamic networks exhibit a strong bias in breaking symmetry between conventional solutions.
  • The host-guest norm (aggressive away, not at home) commonly evolves, contrasting with the ownership norm.
  • Learning to avoid conflict generates realistic network structures, distinct from preferential attachment models.

Conclusions:

  • Dynamic networks are crucial for the emergence and bias of social conventions.
  • The host-guest norm observed in simulations aligns with common human interaction patterns.
  • Network-driven learning provides a novel mechanism for social structure formation.