The literature on transnational regulatory networks identified interdependence as their main rationale, downplaying domestic factors. Typically, relevant contributions use the word “network” only metaphorically. Yet, informal ties between regulators constitute networked structures of collaboration, which can be measured and explained. Regulators choose their frequent, regular network partners. What explains those choices? This article develops an Exponential Random Graph Model of the network of European national energy regulators to identify the drivers of informal regulatory networking. The results show that regulators tend to network with peers who regulate similarly organised market structures. Geography and European policy frameworks also play a role. Overall, the British regulator is significantly more active and influential than its peers, and a divide emerges between regulators from EU-15 and others. Therefore, formal frameworks of cooperation (i.e. a European Agency) were probably necessary to foster regulatory coordination across the EU.

Studying the evolutive and adaptative machanisms of prokayotes is a complicated task.As these machanisms cannot be easily studied "in vivo", it is necessary to consider other methods.We have therefore developed the RAevol model, a model designed to study the evolution of bacteria and their adaptationto the environment.Our model simulates the evolution of a population of artificial bacteria in a changing environment, providing us with an insight into the strategies that digital organisms develop to adapt to new conditions.In this paper we describe the principle and architecture of the model, focusing on the mechanisms of the regulatory networks of artificial organisms.Experiments were conducted on populations of artificial bacteria under conditions of stress.We study the ways in which organisms adapt to environmental changes and examine the strategies they adopt.An analysis of these adaptation strategies is presented and a brief overview was proposed concerning the patterns and topological characteristics of the evolved regulatory networks.