Selfish algorithm and emergence of collective intelligence
Abstract 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.