Exactly predicting the future of an evolving large scale socio-technical system is impossible. Yet, if we are to sustainably manage the industrial and infrastructure systems our society depends on, we must understand how the actions we take today will affect the evolution of these systems. Simulating how the social and technical networks co-evolve over time allows us to explore possible system futures. This knowledge can help today’s decision makers to steer the system away from undesirable evolutionary pathways. Creating models that capture the complexity of socio-technical systems co-evolution requires multiple formalisms to be encoded in a modeling framework that itself evolves. This thesis presents a method for creating Agent Based Models that suitably represent complex evolving systems. The method involves a co-evolution between the technical aspects of model development, the social process involving the stakeholders, the collection of relevant domain knowledge and the encoding of facts. Through seven case studies the method is demonstrated to yield subsequent generations of richer and ever more useful simulation models.