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Robustness is a key criterion for evaluating alternative decisions under conditions of deep uncertainty. However, no systematic, general approach exists for finding robust strategies using the broad range of models and data often available to decision makers. This study demonstrates robust decision making (RDM), an analytic method that helps design robust strategies through an iterative process that first suggests candidate robust strategies, identifies clusters of future states of the world to which they are vulnerable, and then evaluates the trade-offs in hedging against these vulnerabilities. This approach can help decision makers design robust strategies while also systematically generating clusters of key futures interpretable as narrative scenarios. Our study demonstrates the approach by identifying robust, adaptive, near-term pollution-control strategies to help ensure economic growth and environmental quality throughout the 21st century.decision making under uncertainty, robust decision making, deep uncertainty, adaptive planning, scenario planning

Exploratory modeling is using computational experiments to assist in reasoning about systems where there is significant uncertainty. While frequently confused with the use of models to consolidate knowledge into a package that is used to predict system behavior, exploratory modeling is a very different kind of use, requiring a different methodology for model development. This paper distinguishes these two broad classes of model use describes some of the approaches used in exploratory modeling, and suggests some technological innovations needed to facilitate it.

A clear consensus among the papers in this Colloquium is that agent-based modeling is a revolutionary development for social science. However, the reasons to expect this revolution lie more in the potential seen in this tool than through realized results. In order for the anticipated revolution to occur, a series of challenges must be met. This paper surveys the challenges suggested by the papers of this session.A gent-based modeling (ABM) has been gaining growing acceptance and enthusiasm in various fields of social science in recent years. Whatever disagreements may have emerged in this Colloquium, there is one point of clear consensus: ABM holds out the promise of a revolutionary advance in social science theory. Two questions are worth asking. What are the reasons ABM is thought to be revolutionary, and what important next steps in developing ABM as a tool for social science are needed in order for this revolution to occur?There are numerous precedents in history of a new tool catalyzing revolutionary developments in the science that used that tool. It was developments in lens grinding which allowed the creation of telescopes that made astronomy possible. Similarly, the microscope was necessary for bacteriology. And the study of physics was fundamentally transformed by the invention of the calculus. So, there is no reason to doubt the plausibility that a new modeling technique might have profound implications for those sciences that make use of it. There are also, of course, many examples of brilliant tools whose impact was much less profound. The revolutionary tool innovations are distinguished from those with lesser importance not by the technological virtuosity of their creation, but by the needs they served in the sciences that adopted them. So, to evaluate this proposed revolution, what matters is not the computer science advances that make ABM possible, but rather the social science challenges that make it necessary.Surveying the papers of the Colloquium, one can discern three generic reasons cited for the potential importance of ABM to social science. These are: (i) the unsuitability of competing modeling formalisms to address the problems of social science, (ii) agents as a natural ontology for many social problems, and (iii) emergence. In the remainder of this paper, I consider each of these reasons in turn. Each one provides a vantage point to suggest important next steps in developing ABM. If the anticipated revolution is actually to occur, and the potential of ABM is to be realized, these steps will need to be taken.The most fundamental reason for the enthusiasm for ABM is the dissatisfaction with the restrictions imposed by alternative modeling formalisms. The most widely used alternatives are systems of differential equations and statistical modeling. Both of these competing tools have made important contributions to social science, but both are viewed as imposing restrictive or unrealistic assumptions that limit their use for many problems. The list of assumptions that have been objected to is...

Agent-based models (ABM) are examples of complex adaptive systems, which can be characterized as those systems for which no model less complex than the system itself can accurately predict in detail how the system will behave at future times. Consequently, the standard tools of policy analysis, based as they are on devising policies that perform well on some best estimate model of the system, cannot be reliably used for ABM. This paper argues that policy analysis by using ABM requires an alternative approach to decision theory. The general characteristics of such an approach are described, and examples are provided of its application to policy analysis.