A bacterial-based algorithm to simulate complex adaptive systems

Following a bacterial-based modeling approach, we want to model and analyze the impact of both decentralization and heterogeneity on group behavior and collective learning. Inspired by bacterial conjugation, we 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 we have simulated a scenario in which resources are limited and localized. We also have defined three constraints in genetic information processing (inhibition of plasmid conjugation, inhibition of plasmid reproduction and inhibition of plasmid mutation). Our 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