Modelling Complex Dynamics and Distributed Generation of Knowledge with Bacterial-Based Algorithms

Abstract

This study aimed to test that connected and heterogeneous societies with peer-to-peer (P2P) exchanges are more resilient than centralized and homogeneous ones. In agent-based modeling, agents with bounded rationality interact in a common environment guided by local rules, leading to Complex Adaptive Systems that are named 'artificial societies'. These simplified models of human societies grow from the bottom up in computational environments and can be used as a laboratory to test some hypotheses. We have demonstrated that in a model based on free interactions among autonomous agents, optimal results emerge by incrementing diversity and decentralization of communication structures, as much as in real societies Internet is leading to the emergence of improvements in collective intelligence. In order to achieve a real “Knowledge Society”, what we have named a “P2P Society”, it is necessary to increase decentralization and heterogeneity through information policies, distributed communication networks, open e-learning approaches and initiatives like public domain licenses, free software and open data.