A bacterial-based algorithm to simulate complex adaptive systems

Abstract

Bacteria have demonstrated an amazing capacity to overcome environmental changes by collective adaptation through genetic exchanges. Using a distributed communication system and sharing individual strategies, bacteria propagate mutations as innovations that allow them to survive in different environments. In this paper we present an agent-based model which is inspired by bacterial conjugation of DNA plasmids. In our approach, agents with bounded rationality interact in a common environment guided by local rules, leading to Complex Adaptive Systems that are named 'artificial societies'. We have demonstrated that in a model based on free interactions among autonomous agents, optimal results emerge by incrementing heterogeneity levels and decentralizing communication structures, leading to a global adaptation of the system. This organic approach to model peer-to-peer dynamics in Complex Adaptive Systems is what we have named 'bacterial-based algorithms' because agents exchange strategic information in the same way that bacteria use conjugation and share genome. © 2014 Springer International Publishing Switzerland.