Abstract
Recently Tanguiane and Gruber suggested two methods to construct quadratic objective functions from answers to simple questions about indifference. It is pointed out that objective functions generated with these methods tend to be highly non-robust with respect to small errors in a decision maker’s answers. For definite objective functions which are often considered relevant in macroeconomics, a modified method is proposed which reduces the problem of non-robustness. Simulations yield a ranking of the three approaches with respect to their reliability when answers are not precise. However, even the modified method which requires the lowest level of precision may be too demanding for real-world decision makers.
I am grateful to workshop participants at Sion and to participants of the 3rd International Conference on Econometric Decision Models at Hagen, in particular to F.Ebert, J.Gruber, and A.S.Tanguiane for helpful comments on earlier work which stimulated this research.
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Welzel, P. (1997). Quadratic Objective Functions from Ordinal Data: Towards Reliable Representations of Policy Makers’ Preferences. In: Tangian, A., Gruber, J. (eds) Constructing Scalar-Valued Objective Functions. Lecture Notes in Economics and Mathematical Systems, vol 453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-48773-6_13
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