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Agent-based models of collective intelligence.

Sandro M Reia1, André C Amado2, José F Fontanari1

  • 1Instituto de Física de São Carlos, Universidade de São Paulo, Caixa Postal 369, 13560-970 São Carlos, SP, Brazil.

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Summary
This summary is machine-generated.

Cooperative agents solve problems faster than individuals, but group intelligence does not change the search process. Two models, imitative learning and blackboard organization, show speed-up without altering the search

Keywords:
Blackboard systemsCollective intelligenceImitative learning

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Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Social Psychology

Background:

  • Collective intelligence enhances problem-solving efficiency through cooperation.
  • The impact of cooperation on the statistical nature of search remains unclear.

Purpose of the Study:

  • To analyze two agent-based models of cooperative problem-solving.
  • To determine if cooperation qualitatively alters search signatures.

Main Methods:

  • Review of imitative learning search model.
  • Analysis of blackboard organization model.
  • Comparison with isolated agent problem-solving.

Main Results:

  • Imitative learning can fail with high imitation frequency or large groups (Groupthink).
  • Blackboard organization is optimal with limited information sharing.
  • Both models significantly reduce puzzle-solving time compared to isolated agents.

Conclusions:

  • Cooperative strategies accelerate problem-solving.
  • Cooperation does not change the fundamental statistical signature of the search process.