A knowledge-based methodology for tuning analytical models

A description is presented of a methodology, called knowledge-based tuning, that allows a human analyst and a knowledge-based system to collaborate in adjusting an analytic model. Such a methodology makes the model more acceptable to a decision-maker, and offers the potential for making better decisions than either an analyst or a model can make alone. In knowledge-base tuning, subjective judgments about missing factors are specified by the analyst in terms of linguistic variables. These linguistic variables and knowledge of the model error history are used by the tuning system to infer a specific model adjustment. A logic programming system was developed that illustrates the tuning methodology for a macroeconometric forecasting model. It empirically demonstrates how the predictability of the model can be improved