Showing posts from June, 2010

Workshop on Self-Organizing Systems

I am co-chairing the technical programme committe, together with Christian Bettstetter , IWSOS 2011, the Fifth International Workshop on Self-Organizing Systems, to be held on February, 23-25, 2011 in Karlsruhe, Germany. The general co-chairs are Martina Zitterbart and Hermann de Meer . Call for Papers will be distributed soon. Check the webpage at

BEng thesis published: Artificial Societies of Intelligent Agents

My BEng thesis (from 2001) was just published as a book. You can order a hardcopy at . It is still available electronically in pdf and html . Gershenson, C. (2010 ). Artificial Societies of Intelligent Agents: Virtual Experiments of Individual and Social Behaviour . LAP Lambert Academic Publishing, ISBN 3838357736 . Summary : In this book we use artificial societies to understand and simulate adaptive behaviour and social processes. We obtain this in three parallel ways: First, we present a behaviours production system capable of reproducing a high number of properties of adaptive behaviour and of exhibiting emergent lower cognition. Second, we introduce a simple model for social action, obtaining emergent complex social processes from simple interactions of imitation and induction of behaviours in agents. And third, we present our approximation to a behaviours virtual laboratory, integrating our behaviours production system and our social action model in virtual animats.

New draft: Guiding the Self-organization of Random Boolean Networks

Gershenson, C. (2010). Guiding the Self-organization of Random Boolean Networks. C3 Report 2010.05. Abstract : Random Boolean networks (RBNs) are models of genetic regulatory networks. It is useful to describe RBNs as self-organizing systems to study how changes in the nodes and connections affect the global network dynamics. This article reviews seven different methods for guiding the self-organization of RBNs. In particular, the article is focussed on guiding RBNs towards the critical dynamical regime, which is near the phase transition between the ordered and dynamical phases. The properties and advantages of the critical regime for life, computation, adaptability, evolvability, and robustness are revised. The guidance methods of RBNs can be used for engineering systems with the features of the critical regime, as well as for studying how natural selection evolved living systems, which are also critical. Full text :