Agent-based modeling as organizational and public policy simulators.
Agent-based models are an increasingly powerful tool for simulating social systems because they can represent important phenomenon difficult to capture in other mathematical formalisms. But, agent-based models have provided only limited support for policy-making because their distinctive abilities are often most useful in situations where the future is unpredictable. In such situations, the traditional analytic methods for applying simulation models to support decision-making are least effective. Fortunately, new analytic approaches for decision-making under conditions of deep uncertainty--emphasizing large ensembles of model-created scenarios and adaptive policies evaluated with the criteria of robustness, rather than with optimality or efficiency--can unleash the full potential of agent-based policy simulators.
Lempert
Robert
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RAND, 1700 Main Street, Santa Monica, CA 90407, USA. lempert@rand.org