Selfish Algorithm and Emergence of Collective Intelligence
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We propose a model for demonstrating spontaneous emergence of collective intelligent behavior from selfish individual agents. Agents' behavior is modeled using our proposed selfish algorithm (SA) with three learning mechanisms: reinforced learning (SAL), trust (SAT) and connection (SAC). Each of these mechanisms provides a distinctly different way an agent can increase the individual benefit accrued through playing the prisoner's dilemma game (PDG) with other agents. The SA provides a generalization of the self-organized temporal criticality (SOTC) model and shows that self-interested individuals can simultaneously produce maximum social benefit from their decisions. The mechanisms in the SA are self-tuned by the internal dynamics and without having a pre-established network structure. Our results demonstrate emergence of mutual cooperation, emergence of dynamic networks, and adaptation and resilience of social systems after perturbations. The implications and applications of the SA are discussed.
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