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Temporal network epistemology: On reaching consensus in a real-world setting.

Radosław Michalski1, Damian Serwata1, Mateusz Nurek1

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This study introduces a temporal network epistemology model to simulate learning in dynamic networks. Network dynamics significantly impact consensus, revealing new phenomena like uninformed agents and structural influence on belief spreading.

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Area of Science:

  • Complex Systems
  • Network Science
  • Computational Social Science

Background:

  • Understanding collective learning and consensus formation in dynamic environments is crucial.
  • Traditional models often overlook the impact of temporal network evolution on information diffusion.

Purpose of the Study:

  • To develop and validate a temporal network epistemology model for simulating learning processes.
  • To investigate the influence of network temporal dynamics on consensus achievement and information flow.

Main Methods:

  • Simulation of learning processes on a temporal social network generated by the CogSNet model.
  • Comparison with static network topologies to isolate the effects of temporal dynamics.
  • Analysis of consensus dynamics, agent states, and the impact of network structure changes.

Main Results:

  • Temporal network dynamics significantly alter learning outcomes and consensus dynamics compared to static models.
  • Observed novel phenomena including uninformed agents and varied consensus states in disconnected network components.
  • Demonstrated that network structure evolution can be a critical factor in achieving consensus.

Conclusions:

  • The temporal network epistemology model provides a robust framework for studying collective learning in dynamic systems.
  • Network temporal evolution plays a critical role in shaping consensus formation and information propagation.
  • Findings have implications for understanding scientific problem-solving and societal belief spreading.