A knowledge-based methodology for tuning analytical models
IEEE Transactions on Systems, Man and Cybernetics
Many computer-based analytical models for decision-making and forecasting have been developed in recent years, particularly in the areas of economics and finance. Analytic models have an important limitation which has restricted their use: a model cannot anticipate every factor that may be important in making a decision. Some analysts attempt to compensate for this limitation by making heuristic adjustments to the model in order to "tune" the results. Tuning produces a model forecast that is
... forecast that is consistent with intuitive expectations, and maintains the detail and structure of the analytic model. This is a very difficult task unless the user has expert knowledge of the model and the task domain. This paper describes a new 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 of improving the decisions that either an analyst or a model can make alone. In knowledge-based 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 that empirically demonstrates how the predictability of the model can be improved.