Agent-based models of collective intelligence

Autor(es)

Reia, Sandro M. ^[1] ; Amado, Andre C. ^[2] ; Fontanari, Jose F. ^[1]

Afiliação do(s) autor(es)

^[1] Univ Sao Paulo, Inst Fis Sao Carlos, Caixa Postal 369, BR-13560970 Sao Carlos, SP - Brazil

^[2] Univ Fed Pernambuco, Dept Fis, BR-50670901 Recife, PE - Brazil

• Tipo de documento: Artigo de Revisão
• Fonte: PHYSICS OF LIFE REVIEWS; v. 31, p. 320-331, DEC 2019.
• Citações Web of Science: 2

Resumo

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 akin to Groupthink of social psychology. The second model is the blackboard organization in which agents read and post hints on a public blackboard. This brainstorming scenario performs the best when there is a stringent limit to the amount of information that is exhibited on the board. Both cooperative scenarios produce a substantial speed up of the time to solve the puzzle as compared with the situation where the agents work in isolation. The statistical signature of the search, however, is the same as that of the independent search. (C) 2019 Elsevier B.V. All rights reserved. (AU)

• Processo FAPESP: 15/17277-0 - Difusão de inovações: modelagem computacional baseada no modelo de Axelrod
• Beneficiário: Sandro Martinelli Reia
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 17/23288-0 - Tópicos em inteligência coletiva: Brainstorming, Vox Populi e Physarum polycephalum
• Beneficiário: José Fernando Fontanari
• Modalidade de apoio: Auxílio à Pesquisa - Regular