| Texto completo | |
| Autor(es): |
Fontanari, Jose F.
[1]
Número total de Autores: 1
|
| Afiliação do(s) autor(es): | [1] Univ Sao Paulo, Inst Fis Sao Carlos, Sao Carlos, SP - Brazil
Número total de Afiliações: 1
|
| Tipo de documento: | Artigo Científico |
| Fonte: | PLoS One; v. 9, n. 10 OCT 16 2014. |
| Citações Web of Science: | 14 |
| Resumo | |
The notion that cooperation can aid a group of agents to solve problems more efficiently than if those agents worked in isolation is prevalent in computer science and business circles. Here we consider a primordial form of cooperation - imitative learning - that allows an effective exchange of information between agents, which are viewed as the processing units of a social intelligence system or collective brain. In particular, we use agent-based simulations to study the performance of a group of agents in solving a cryptarithmetic problem. An agent can either perform local random moves to explore the solution space of the problem or imitate a model agent - the best performing agent in its influence network. There is a trade-off between the number of agents N and the imitation probability p, and for the optimal balance between these parameters we observe a thirtyfold diminution in the computational cost to find the solution of the cryptarithmetic problem as compared with the independent search. If those parameters are chosen far from the optimal setting, however, then imitative learning can impair greatly the performance of the group. (AU) | |
| Processo FAPESP: | 13/17131-0 - Estudos no modelo de Axelrod: transições de fase de não-equilíbrio, difusão de inovações, e computação coletiva |
| Beneficiário: | José Fernando Fontanari |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |