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 - Randomoptimization" . 1. Newsvendor Inventory Management Problem 2. School Admissions. 3. Journal Submissions. 4. Job Candidate Selection. 5. Stock Picking.
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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