ABSTRACT
In the Introduction to this volume’s predecessor, Simulating societies: the computer simulation of social phenomena (Gilbert & Doran 1994), the editors, Nigel Gilbert and Jim Doran, described the state of the art in research on social simulation and defined social simulation as a particular approach to the study of societies that involved “the use of precisely specified simulation models, often formulated and run on computers”. This approach, said the editors, has been “fruitful in the past and…appears likely to be even more so in the future” (p. 1). Their forecast has proved to be correct even within the short time since it was made. Three different indicators suggest that the use of computer simulations for studying essential aspects of societies has gained importance:
(a) The number of studies relying on this methodology has increased. (b) The number of disciplines involved has also grown. Once, studies in social simulation
were drawn from the central core of the social sciences and from computer science. Now other disciplines, such as cognitive science, biology, neuroscience, and some artificial intelligence (AI) subfields, such as distributed artificial intelligence and research on multi-agent systems, have been showing a growing interest in the computer-based study of societies. The motivations that are leading people with a nonsocial scientific background to this common area of investigation are obviously heterogeneous. However, there are some broad questions of cross-disciplinary interest that seem to demand the use of computer simulation. These include evolutionary and dynamic models, the problem of complexity and the paradigm of emergence. Later in this chapter we shall return to these questions and to the necessity of using computer simulation to deal with them successfully.
