Multiattribute Choice for the Lazy Decision Maker: Let the Alternatives Decide!

In this article we present a method of multi-attribute choice, based on an application of linear programming called Data Envelopment Analysis (DEA). The first part of the method, which is straightforward DEA, can be thought of as an idealized process of self-evaluation in which each alternative weights the attributes in order to maximize its own desirability relative to the other alternatives. These weights are taken as defining the preferences of a fragment of the market. The second step is to use each alternative′s optimal weights to reconstruct the entire market and thus to infer the preferences of an average decision maker (DM) who needs to choose from among these alternatives. We show how this process is equivalent to an idealized peer evaluation; each alternative applies its own DEA-derived best weights to each of the other alternatives (alternative cross-evaluation, or AXE), then the average of the cross-evaluations that get placed on an alternative is taken as an index of its overall desirability (i.e., we are "letting the alternatives decide"). We use a large data set to examine the workings of the method and to compare our results with the published results of the data set′s compiler. AXE is also able to make use of partial information about the ordering of importance of the attributes specific to a particular DM. Taking an ecological perspective we argue that the method is sensitive to cues already in the data and uses them to amplify helpful bias and attenuate unhelpful bias. We also show how different kinds sensitivity analysis may be performed to check the robustness of the results