Certain types of infrastructure--critical infrastructure (CI)--play vital roles in underpinning our economy, security and way of life. These complex and often interconnected systems have become so ubiquitous and essential to day-to-day life that they are easily taken for granted. Often it is only when the important services provided by such infrastructure are interrupted--when we lose easy access to electricity, health care, telecommunications, transportation or water, for example--that we are conscious of our great dependence on these networks and of the vulnerabilities that stem from such dependence. Unfortunately, it must be assumed that many terrorists are all too aware that CI facilities pose high-value targets that, if successfully attacked, have the potential to dramatically disrupt the normal rhythm of society, cause public fear and intimidation, and generate significant publicity. Indeed, revelations emerging at the time of this writing about Al Qaida's efforts to prepare for possible attacks on major financial facilities in New York, New Jersey, and the District of Columbia remind us just how real and immediate such threats to CI may be. Simply being aware that our nation's critical infrastructure presents terrorists with a plethora of targets, however, does little to mitigate the dangers of CI attacks. In order …

Fifteen papers and abstracts are included. Separate abstracts were prepared for six papers and nine abstracts were listed by title.

The many motivations for employing agent-based computation in the social sciences are reviewed. It is argued that there exist three distinct uses of agent modeling techniques. One such use--the simplest--is conceptually quite close to traditional simulation in operations research. This use arises when equations can be formulated that completely describe a social process, and these equations are explicitly soluble, either analytically or numerically. In the former case, the agent model is merely a tool for presenting results, while in the latter it is a novel kind of Monte Carlo analysis. A second, more commonplace usage of computational agent models arises when mathematical models can be written down but not completely solved. In this case the agent-based model can shed significant light on the solution structure, illustrate dynamical properties of the model, serve to test the dependence of results on parameters and assumptions, and be a source of counter-examples. Finally, there are important classes of problems for which writing down equations is not a useful activity. In such circumstances, resort to agent-based computational models may be the only way available to explore such processes systematically, and constitute a third distinct usage of such models.