The Analysis of Assumptions in Model Bases Using Metagraphs
提出一种基于元图的分析方法，用于表示和检验模型库中多个模型及其假设之间的关系，帮助判断哪些模型适用、假设是否必要或充分。
Decision models are often based on certain assumptions as to their validity. Relevant assumptions may include value-based assumptions, such as limitations on the range or values of some input variables or exogenous factors, as well as assumptions about model structure (e.g., linearity). In a model base consisting of many models, there may be several models (or collections of models) that can be used to solve a particular problem. We may wish to know what the applicable models are, what assumptions are associated with these models, and whether a given set of assumptions is necessary and/or sufficient for solving the problem. We describe an analytical approach, based on a graph-theoretic construct called a metagraph, and show how it can be used to represent and analyze assumptions in model bases.