Robust decisions in economic models

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Abstract

The minimax principle and a semiparametric characterization of the random states of nature are utilized here in the models of efficiency measurement by data envelopment analysis. These methods assume inadequate knowledge of the decision-maker about the random states of nature and develop a cautious optimal policy by testing if the chosen strategy or solution is very sensitive to the worst contingency that may arise. Some empirical applications to educational production functions show the usefulness of these operational methods.

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    Jati K. Sengupta is Professor of Economics and Operations Research at the University of California, Santa Barbara. He received his doctoral degree from Iowa State University and has held important managerial positions, including the Directorship of the Indian Institute of Management at Calcutta. He has done extensive theoretical and empirical research on stochastic systems and models and has had consulting experience with the World Bank, Ford Foundation and other agencies. His publications include 6 research monographs and over 200 research papers in such journals as Management Science, Journal of Optimization Theory and Applications, Operations Research, Econometrica, International Journal of Systems Science and Journal of Mathematical Analysis and Applications. His latest research monograph “Efficiency Analysis by Production Frontiers: the Nonparametric Approach” (1989) provides an up-to-date survey of research on data envelopment analysis which is used in this paper to illustrate the robustness of optimal decision rules.

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