Publicado em: 30/01/2013

Dissertação de Mestrado em Inteligência Artificial

UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL
INSTITUTO DE INFORMÁTICA
PROGRAMA DE POS-GRADUAÇÃO EM COMPUTAÇÃO
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DEFESA DE DISSERTAÇÃO DE MESTRADO

Aluno: Diego Vrague Noble
Orientador: Prof. Dr. Luis da Cunha Lamb
Coorientação: Ricardo Matsumura Araújo

Título: The Impact of Social Context in Social Problem-Solving
Linha de Pesquisa: Inteligência Artificial

Data: 05/02/2013
Hora: 14h
Local: Prédio 43413(67) – Sala Auditório Inferior

Banca Examinadora:
Prof. Dr. Paulo Roberto Ferreira Júnior (UFPel)
Profa. Dra. Carla Maria Dal Sasso Freitas (UFRGS)
Profa. Dra. Renata de Matos Galante (UFRGS)

Presidente da Banca: Prof. Dr. Luis da Cunha Lamb

Resumo:

Our inability to perceive and understand all the factors that account for real-world phenomena forces us to rely on clues when reasoning and making decisions about the world. Clues can be internal such as our psychological state and our motivations; or external, such as the resources available, the physical environment, the social environment, etc. The social environment, or social context, encompasses the set of relationships and cultural settings by which we interact and function in a society. Much of our thinking is influenced by the social environment and we constantly change the way we solve problems in response to our social environment. Nevertheless, this human trait has not been captured by current computational models of human social problem-solving, for these models have lacked the heterogeneity and self-adaptive behavior observed in humans. In this work, we address this issue by investigating the impact of social context in social problem solving by means of extensive numerical simulations using a modified social model. We show evidences that social context plays a key role in how the system behaves and performs. More precisely, we show that the centrality of an agent in the network is an unreliable predictor the agent’s contribution when this agent can change its problem-solving strategy according to social context. Another finding is that social context information can be used to improve the convergence speed of the group to good solutions and that diversity in search strategies does not necessarily translates into diversity in solutions. We also determine that even if nodes perceive social context in same way, the way they react to it may lead to different outcomes along the search process. Together, these results contribute to the understanding that social context does indeed impact in social problem-solving. We conclude discussing the overall impact of this work and pointing future directions.

Palavras-Chave: Computer Science; Artificial Intelligence; Social Computing.