Using multiple objectives to approximate normative models

Abstract

This paper discusses the rationale for the use of additive models involving multiple objectives as approximations to normative analyses. Experience has shown us that organizations often evaluate important decisions with multiple objective models rather than reducing all aspects of the problem to a single criterion, dollars, as many normative economic models prescribe. We justify this practice on two grounds: managers often prefer to think about a problem in terms of several dimensions and a multiple objective model may provide an excellent approximation to the more complex normative model. We argue that a useful analysis based on a multiple objective model will fulfill both conditions—it will provide insights for the decision maker as well as a good approximation to the normative model.

We report several real-world examples of managers using multiple objective models to approximate such normative models as the risk-adjusted net present value and the value of information models. The agreement between the approximate models and the normative models is shown to be quite good. Next, we cite a portion of the behavioral decision theory literature which establishes that linear models of multiple attributes provide quite robust approximations to individual decision-making processes. We then present more general theoretical and empirical results which support our contention that linear multiple attribute models can provide good approximations to more complex models.