Abstract. Bacteria have demonstrated an amazing capacity to overcome envi-ronmental 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 envi-ronments. 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 dem-onstrated 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 or-ganic approach to model peerto-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.

This article argues that an autopoietic perspective of human communities would allow to understand societies as self-organized systems and thus promote information literacy as a facilitator of social development. Peer-to-peer (P2P) social dynamics generate public infor-mation available worldwide in digital repositories, websites and bibliographic resources. However, processing such amount of data is not achievable by a single central-controlled system. We claim that distributed and heterogeneous networks of coordinated mechan-isms, composed by both specialized human and artificial agents, are needed to improve information retrieval, knowledge inference and decision-making, but also to produce social value, goods and services. Handling these issues implies the collective construction of glob-al semantic networks but also the active labor of knowledge producers and consumers. We conclude that information literacy is as much important as any technical implementation and, therefore, may lead to networks of Commons-oriented communities which would uti-lize P2P infrastructures.

Abstract. Following a bacterial-based modeling approach, the authors want to model and analyze the impact of both decentralization and heterogeneity on group behavior and collective learning. The paper aims to discuss these issues.Inspired by bacterial conjugation, the authors have defined an artificial society in which agents' strategies adapt to changes in resources location, allowing migration, and survival in a dynamic sugarscape-like scenario. To study the impact of these variables the authors have simulated a scenario in which resources are limited and localized. The authors also have defined three constraints in genetic information processing (inhibition of plasmid conjugation, inhibition of plasmid reproduction and inhibition of plasmid mutation). The results affirmed the hypothesis that efficiency of group adaptation to dynamic environments is better when societies are varied and distributed than when they are homogeneous and centralized.The authors have demonstrated that in a model based on free interactions among autonomous agents, optimal results emerge by incrementing heterogeneity levels and decentralization of communication structures, leading to a global adaptation of the system. This organic approach to model peer-to-peer dynamics in complex adaptive systems (CAS) is what the authors have named "bacterial-based algorithms" because agents exchange strategic information in the same way that bacteria use conjugation and share genome.

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