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The following HTML text is provided to enhance online readability. Many aspects of typography translate only awkwardly to HTML. Please use the page image as the authoritative form to ensure accuracy. Finally, practitioners should consider multiple stories that reveal new aspects of situations. WHY MODELS DON’T FORECASTLaura A. McNamara The title of this paper, “Why Models Don’t Forecast,” has a deceptively simple answer: models don’t forecast because people forecast. Yet this statement has significant implications for computational social modeling and simulation in national security decision making. Specifically, it points to the need for robust approaches to the problem of how people and organizations develop, deploy, and use computational modeling and simulation technologies. I argue that the challenge of evaluating computational social modeling and simulation technologies extends far beyond verification and validation and includes the relationship between a simulation technology and the people and organizations using it. This challenge of evaluation is not just one of usability and usefulness for technologies but extends to the assessment of how new modeling and simulation technologies shape human and organizational judgment. The robust and systematic evaluation of organizational decision-making processes, and the role of computational modeling and simulation technologies therein, are a critical problem for the organizations that promote, fund, develop, and seek to use computational social science tools, methods, and techniques in high-consequence decision making. A PERSPECTIVE ON MODELING, DATA, AND KNOWLEDGERobert G. Sargent This paper presents and discusses the problem-solving methodology used in operations research. The advantages presented using this methodology include (1) the development of a problem statement, (2) the construction and use of a causal mathematical model based on system knowledge, and (3) the data requirements determined from the steps of the methodology. Also discussed is how this methodology differs from the method of first collecting significant amounts of data and then attempting to develop models from that data. Two major types of models, causal and empirical, are compared and discussed; this includes the strengths and weaknesses of each type. This paper also discusses why causal models are preferred, the importance of understanding that causal models contain system relationships and empirical models contain data relationships, and the different kinds of graphical and mathematical models for each model type. Different
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