A continuous-time Markov process is proposed to analyze how a group of humans solves a complex task, which consists in the search of the optimal set of decisions on a fitness landscape. Individuals change their opinions driven by the self-directed behavior, which pushes them to increase their own fitness value, and by the social interactions, which push individuals to find a common opinion. Results show that increasing the strength of social interactions makes the decision-making process more effective. However, too high values of social interaction strength worsen the performance of the group. We also show that a moderate level of knowledge is already enough to guarantee high performance of the decision-making process.
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Online at: http://scholar.google.com/scholar_url?url=http://computationalsocialscience.org/wp-content/uploads/2015/10/CSSSA_2015_submission_26.pdf&hl=en&sa=X&scisig=AAGBfm0z5spDpv0lLwGMAer64h-mJeODQA&nossl=1&oi=scholaralrt
Decision making, social interactions, complexity, Markov chains
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