How to Win

From a 2004 paper by Gaba et al:

If a contestant has the opportunity to modify the distribution of her performance, what strategy is advantageous? When the proportion of winners is less than one-half, a riskier performance distribution is preferred; when this proportion is greater than one-half, it is better to choose a less risky distribution. Using a multinormal model, we consider modifications in the variability of the distribution and in correlations with the performance of other contestants. Increasing variability and decreasing correlations lead to improved chances of winning when the proportion of winners is less than one-half, and the opposite directions should be taken for proportions greater than one-half. Thus, it is better to take chances and to attempt to distance oneself from the other contestants (i.e., to break away from the herd) when there are few winners; a more conservative, herding strategy makes sense when there are many winners.

Applications to academia:

For example, if a school wants to be more innovative and nurture high-risk, high-payoff “big ideas,” it should decrease p (of tenure) for junior faculty…
There are also implications regarding the type of individual who might join the organization. For example, consider a new Ph.D. entering academia with a choice between a school with moderate research expectations and reasonably high p (of tenure) and a top research school with low p but greater rewards associated with winning the tenure contest. An organization wanting to minimize the chance of very low performance and/or to attract people who prefer to stay on well-trodden paths should set p high, whereas an organization wanting to increase the chance of especially high performance (at the cost of an increased chance of especially low performance) and/or to attract people who are competitive and like the challenge of striking off in new directions should set p low.

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