Qualitative models can be used for decision making under uncertainty. This provides a useful framework for evaluating the models. If the costs for every action/state of nature combination are known, decisions made using a well-calibrated model would result in actual costs being close to expected costs. In addition, the actual cost can be compared to the cost of perfect foresight actions, giving a bound on the value of a better model. Application of these procedures is made using a logit model developed to predict Missouri country grain elevator bankruptcies.

One method for making analogies is to access and instantiate abstract domain principles, and one method for acquiring knowledge of abstract principles is to discover them from experience. We view generalization over experiences in the absence of any prior knowledge of the target principle as the task of hypothesis formation, a subtask of discovery. Also, we view the use of the hypothesized principles for analogical design as the task of hypothesis testing, another subtask of discovery. In this paper, we focus on discovery of physical principles by generalization over design experiences in the domain of physical devices. Some important issues in generalization from experiences are what to generalize from an experience, how far to generalize, and what methods to use. We represent a reasoner's comprehension of specific designs in the form of structure-behavior-function (SBF) models. An SBF model provides a functional and causal explanation of the working of a device. We represent domain principles as device-independent behavior-function (BF) models. We show that (1) the function of a device determines what to generalize from its SBF model, (2) the SBF model itself suggests how far to generalize, and (3) the typology of functions indicates what method to use.