R&D for computational cognitive and social models : foundations for model evaluation through verification and validation (final LDRD report).

Slepoy, Alexander ; Mitchell, Scott A. ; Backus, George A. ; McNamara, Laura A. ; Trucano, Timothy Guy

关键词: 97;    DECISION MAKING;    HUMAN FACTORS;    COMPUTERIZED SIMULATION;    EVALUATION;    PERFORMANCE;    VALIDATION;    VERIFICATION;    SOCIOLOGY;    BEHAVIOR;    MATHEMATICAL MODELS Probabilities-Human factors.;    Psychology;    Social.;    Social sciences-Mathematical models.;   

DOI  :  10.2172/945901
RP-ID  :  SAND2008-6453
PID  :  OSTI ID: 945901
Others  :  TRN: US200904%%9

学科分类：社会科学、人文和艺术（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

Sandia National Laboratories is investing in projects that aim to develop computational modeling and simulation applications that explore human cognitive and social phenomena. While some of these modeling and simulation projects are explicitly research oriented, others are intended to support or provide insight for people involved in high consequence decision-making. This raises the issue of how to evaluate computational modeling and simulation applications in both research and applied settings where human behavior is the focus of the model: when is a simulation 'good enough' for the goals its designers want to achieve? In this report, we discuss two years' worth of review and assessment of the ASC program's approach to computational model verification and validation, uncertainty quantification, and decision making. We present a framework that extends the principles of the ASC approach into the area of computational social and cognitive modeling and simulation. In doing so, we argue that the potential for evaluation is a function of how the modeling and simulation software will be used in a particular setting. In making this argument, we move from strict, engineering and physics oriented approaches to V&V to a broader project of model evaluation, which asserts that the systematic, rigorous, and transparent accumulation of evidence about a model's performance under conditions of uncertainty is a reasonable and necessary goal for model evaluation, regardless of discipline. How to achieve the accumulation of evidence in areas outside physics and engineering is a significant research challenge, but one that requires addressing as modeling and simulation tools move out of research laboratories and into the hands of decision makers. This report provides an assessment of our thinking on ASC Verification and Validation, and argues for further extending V&V research in the physical and engineering sciences toward a broader program of model evaluation in situations of high consequence decision-making.