Generalization in Decision Research: The Role of Formal Models

A generalization is a working hypothesis, typically expressed in the form of cause-effect relations. Generalizations decay because a) it is difficult to identify appropriate cause-effect relations and b) such relations are sensitive to the influences of environmental conditions. Whereas scientists should be realistic in their aspirations to create generalizable knowledge, much can be done to improve performance through more extensive use of formal models. Two basic types of model are distinguished: replica models and symbolic models. It is particularly important that theories permit comparisons between models and data at multiple levels involving processes, environmental conditions, and predictions. Scientists should avoid the extremes of " models without data" and "data without models." Symbolic models should be subjected to "strong" empirical tests via predictions (rather than tests of statistical significance), and the competing predictions of alternatives. In addition to suggesting what experimental evidence should be collected, it is proposed that symbolic models also serve the important function of determining when data collection would be of little value. The nature of evidence generated by replica models (i. e., experiments) is considered from three viewpoints: 1) asymmetries in the way data and theory interact in affecting conclusions; 2) apparent but illusory conflicts between the goals of internal and external validity; and 3) the importance of conducting experiments despite poor prospects of creating knowledge that can be generalized.