Agent-based models of collective intelligence
Sandro M. Reia, Andr\'e C. Amado, Jos\'e F. Fontanari

TL;DR
This paper reviews two agent-based models of cooperative problem-solving, demonstrating that cooperation accelerates solution times without changing the fundamental statistical nature of the search process.
Contribution
It provides a comparative analysis of imitative learning and blackboard models, revealing their performance and limitations in collective problem-solving.
Findings
Cooperation speeds up problem-solving compared to independent search.
Imitative learning can lead to groupthink and poor performance when overused.
Blackboard organization performs best with limited shared information.
Abstract
Collective or group intelligence is manifested in the fact that a team of cooperating agents can solve problems more efficiently than when those agents work in isolation. Although cooperation is, in general, a successful problem solving strategy, it is not clear whether it merely speeds up the time to find the solution, or whether it alters qualitatively the statistical signature of the search for the solution. Here we review and offer insights on two agent-based models of distributed cooperative problem-solving systems, whose task is to solve a cryptarithmetic puzzle. The first model is the imitative learning search in which the agents exchange information on the quality of their partial solutions to the puzzle and imitate the most successful agent in the group. This scenario predicts a very poor performance in the case imitation is too frequent or the group is too large, a phenomenon…
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