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From Statistical Decision Theory to Robust Optimization: A Maximin Perspective on Robust Decision-Making

In: Robustness Analysis in Decision Aiding, Optimization, and Analytics

Author

Listed:
  • Moshe Sniedovich

    (University of Melbourne)

Abstract

As attested by the prevalence of worst-case-based robustness analysis in many fields, Wald’s maximin paradigm (circa 1940) plays a central role in the broad area of decision-making under uncertainty. The objective of this chapter is therefore twofold. First, to examine the basic conceptual and modeling aspects of this ostensibly intuitive, yet controversial paradigm, so as to clarify some of the issues involved in its deployment in decision-making in the face of a non-probabilistic uncertainty. Second, to elucidate the differences between this paradigm and other maximin paradigms, such as those used in error analysis and game theory. We thereby chart the journey of this paradigm from the field of statistical decision theory to that of modern robust optimization, highlighting its use in the latter, as a tool for dealing with both local and global robustness. We also look briefly at the relationship between probabilistic and worst-case-based robustness analysis.

Suggested Citation

  • Moshe Sniedovich, 2016. "From Statistical Decision Theory to Robust Optimization: A Maximin Perspective on Robust Decision-Making," International Series in Operations Research & Management Science, in: Michael Doumpos & Constantin Zopounidis & Evangelos Grigoroudis (ed.), Robustness Analysis in Decision Aiding, Optimization, and Analytics, chapter 0, pages 59-87, Springer.
    • Handle: RePEc:spr:isochp:978-3-319-33121-8_4
    DOI: 10.1007/978-3-319-33121-8_4
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