Commonsense reasoning about causality: deriving behavior from structure
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
The use of design descriptions in automated diagnosis
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
VERIFY: a program for proving correctness of digital hardware designs
Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Qualitative reasoning about physical systems
Qualitative reasoning about physical systems
Domain specific DSS tools for knowledge-based model building
Decision Support Systems
An intelligent system for formulating linear programs
Decision Support Systems
An introduction to structured modeling
Management Science
The use of interval arithmetic in uncovering structure of linear systems
Reliability in computing: the role of interval methods in scientific computing
Automated model construction: a logic based approach
Annals of Operations Research
Readings in qualitative reasoning about physical systems
Readings in qualitative reasoning about physical systems
ACM Transactions on Mathematical Software (TOMS)
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Mathematical modeling and analysis are a valuable tool in decision support contexts. Consequently, model management system(s) have become a key component of decision support system generators. Model management systems support modelers in various phases of the modeling life cycle including model representation, formulation, selection, integration, and execution. An important phase of the modeling life cycle involves analyzing and explaining the behavior of the formulated model. Such an analysis is necessary to understand the structure and behavior of the model, and to verify the appropriateness of the model to the problem. Traditional computer-based methods of analysis use the model solution as a basis to explain the model structure that caused the solution. This article presents an alternate approach that explains model behavior using the model structure and commonsense mathematical rules. The approach builds upon the qualitative reasoning methodology, developed in the artificial intelligence area to explain the behaviors of physical devices. A benefit of the qualitative reasoning approach is that it may describe the causes of modeling errors in terms of model structure. I also describe an implemented prototype model preprocessor that uses qualitative reasoning to provide qualitative explanations of the model behavior prior to solving the model. While I do not claim the sufficiency of the qualitative reasoning approach to detect and describe all types of modeling behavior, the article presents several examples demonstrating the utility of the approach.