Although an explicit calculation of downside risk is still rare outside the field of financial services, the concept could clearly be applied more broadly. Consider a business looking to build a plant in China. The managers might analyze the decision by comparing the cost of the investment to the risk of complete failure. The analysis would examine a range of scenarios — for example, a rapid growth in consumer affluence coupled with favorable exchange rates, versus continued poverty-level existence for the vast majority of citizens coupled with protectionist government tariffs. Although the “expected value” across all of the scenarios may be large because of some very high returns in the most favorable scenarios, the “downside risk” of the worst scenarios may be beyond the risk threshold for the company.
Rigorous application of Plausibility Theory’s new math could change the way many strategic decisions are made. No longer forced to choose between their gut instincts and “rational” analysis, managers can now apply rigorous analysis in a far more instinctive way. Plausibility Theory embraces rather than challenges the rationality of intuitive decision making. Its use of risk thresholds offers an approach to decision analysis that is much easier for managers to accept than the Bayesian expected value. Plausibility Theory offers a comprehensive set of consistent rules for decision making. It draws upon the hypothesis-testing logic of classical statistical methodology while avoiding some of the “paradoxes” created by the Bayesian method.
Further work remains to be done, of course, before the new theory can be established in the world of statistical analysis. The current Bayesian paradigms draw upon more than a century of testing and refinement by several generations of mathematicians, whereas the basic logic of Plausibility Theory has emerged only in the last five years. Nonetheless, many signs within the world of business suggest that the time is ripe for a fundamental rethinking of our definitions of “rational” thought.
The greatest resistance to this new theory as a method for strategic decision making may come from within the community of academics, economists, and statisticians committed to the Bayesian view. As one senior scholar commented, “I hope I die before this takes over. I’ve invested too much effort learning the traditional model to switch at this point.” But businesspeople tend to follow a more practical approach: If it works, use it.
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Tim Laseter (firstname.lastname@example.org) is the author of Balanced Sourcing: Cooperation and Competition in Supplier Relationships (Jossey-Bass, 1998) and serves on the operations faculty at the Darden Graduate School of Business Administration at the University of Virginia. Formerly a vice president with Booz Allen Hamilton, he has 20 years of experience in supply chain management and operations strategy.
Matthias Hild (email@example.com) is an assistant professor of business with the Darden Graduate School of Business Administration at the University of Virginia. His forthcoming book, The Inference Machine: On the First Principles of Inductive Reasoning (Cambridge University Press, 2004) synthesizes his latest research on decision making, statistics, and risk management.