Selfish Algorithm and Emergence of Collective Intelligence

TL;DR

This paper introduces a selfish algorithm model with three learning mechanisms that demonstrates how individual self-interest can lead to collective intelligence, cooperation, and resilient social networks without pre-defined structures.

Contribution

It presents a novel selfish algorithm with reinforcement, trust, and connection mechanisms that generalize existing models and show emergent collective behaviors from selfish agents.

Findings

Emergence of mutual cooperation among agents

Development of dynamic, adaptive social networks

Resilience of social systems after perturbations

Abstract

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.