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Group formation on a small-world: experiment and modelling.

Kunal Bhattacharya1, Tuomas Takko1, Daniel Monsivais1

  • 1Aalto University School of Science , PO Box 15400, FI-00076 Aalto , Finland.

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Human participants in an online network game formed groups through local coordination. Agent-based models revealed that while humans optimally perceived risk, agents sometimes exhibited more efficient group formation strategies.

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Area of Science:

  • Computational social science
  • Agent-based modeling
  • Human-computer interaction

Background:

  • Studying human-agent collectives is crucial for understanding complex group dynamics.
  • Local coordination mechanisms are key to emergent group formation in networks.
  • Limited information environments pose challenges for coordinated behavior.

Purpose of the Study:

  • To investigate human decision-making in a cooperative network game.
  • To develop a data-driven agent model for benchmarking human behavior.
  • To explore the efficiency of human versus agent strategies in group formation.

Main Methods:

  • An online game involving human participants cooperating on a small-world network.
  • Development of a probability-matching, data-driven agent model.
  • Agent-based simulations with varied parameters to benchmark human performance.

Main Results:

  • Human players utilized neighborhood information sparingly but demonstrated optimal risk perception.
  • Agent-based simulations showed agents could outperform humans in efficiency under certain conditions.
  • The study provides insights into the rationality and decision-making processes of human players.

Conclusions:

  • Agent-based modeling offers a valuable tool for simulating and understanding human-agent collectives.
  • Human coordination in networks is influenced by information availability and risk assessment.
  • Optimized agent strategies can surpass human efficiency in specific group formation tasks.