Selfish algorithm and emergence of collective intelligence

We propose a model for demonstrating spontaneous emergence of collective intelligent behaviour (i.e. adaptation and resilience of a social system) from selfish individual agents. Agents' behaviour is modelled 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. SAL generates adaptive reciprocity between the agents with a level of mutual cooperation that depends on the temptation of the individuals to cheat. Adding SAT or SAC to SAL improves the adaptive reciprocity between selfish agents, raising the level of mutual cooperation. Importantly, the mechanisms in the SA are self-tuned by the internal dynamics that depend only on the change in the agent's own payoffs. This is in contrast to any pre-established reciprocity mechanism (e.g. predefined connections among agents) or awareness of the behaviour or payoffs of other agents. Also, we study adaptation and resilience of the social systems utilizing SA by turning some of the agents to zealots to show that agents reconstruct the reciprocity structure in such a way to eliminate the zealots from getting advantage of a cooperative environment. The implications and applications of the SA are discussed.