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Fighting Uncertainty with Uncertainty

Authors: Ravi Kashyap;

Fighting Uncertainty with Uncertainty

Abstract

We can overcome uncertainty with uncertainty. Using randomness in our choices and in what we control and hence in the decision making process could potentially offset the uncertainty inherent in the environment and yield better outcomes. This methodology is suitable for the social sciences since the primary source of uncertainty are the members of the system themselves and presently, no methods are known to fully determine the outcomes in such an environment, which perhaps would require being able to read the minds of everyone involved and to anticipate their actions continuously. Admittedly, we are not qualified to recommend whether such an approach is conducive for the natural sciences, unless perhaps, bounds can be established on the levels of uncertainty in a system and it is shown conclusively that a better understanding of the system and hence improved decision making will not alter the outcomes. We consider a number of examples and develop both the theoretical framework and empirical tests where such an approach might be helpful, with the common prescription, “Don't Simply Optimize, Also Randomize, perhaps best described by the term - Randoptimization”.1. Newsvendor Inventory Management Problem.2. School Admissions.3. Journal Submissions.4. Job Candidate Selection.5. Stock Picking.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
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