Supporting decisions with (less than perfect) social science models

It is increasingly appreciated that models of combat and social-political behavior can be informative, helping us to anticipate possible future developments and the possible implications of contemplated actions. However, these models often cannot be relied upon to make predictions as accurate as those possible with models that have been developed in engineering and the physical sciences. Consequently, different standards for quality and methods for developing and exploiting these models are needed. Failure to understand this has resulted in general misuse of anticipatory modeling, either in treating anticipatory models as predictive, or by relegating such models to weak uses such as hypothesis generation. For many decision support applications, much of our knowledge is best expressed as computer models, and so it is important to develop valid means for aggressively exploiting anticipatory models. This paper will describe methods and technology developed at Evolving Logic for exploiting models that contain class knowledge, but do not accurately predict future events. Particular emphasis will be given to recent work which demonstrated the feasibility of developing robust decision options based on anticipatory social science models