But a company’s fate doesn’t hinge only on the big strategic bets of the top brass. More commonly, it depends upon the myriad day-to-day decisions of managers at multiple levels. Do we invest in a new manufacturing technology? What price should we accept for a long-range contract with a major customer? Do we introduce a new product in a new market segment? If enough of these routine decisions go awry — and they easily can — a company will eventually falter. Managers, although rational, still possess the human biases, frailties, and emotions that can cloud effective decision making.
To counteract the hazard of human error in risk assessment and decision making, businesses for decades have employed rigorous analytical techniques (such as decision trees, simulation models, and probabilistic reasoning) drawn from a discipline known as decision analysis. Yet, despite several decades of exposure to these techniques, human intuition and emotion still upend the best-laid plans of CEOs.
Defenders of existing methods of decision analysis argue for better training to overcome these weaknesses. But rather than fight human behavior, decision analysis can em-brace intuition. Plausibility Theory is a promising new approach that accepts the rationality of intuitive decision making and offers business leaders a path forward.
The analytic underpinnings — as well as the weaknesses — of conventional decision analysis lie in Bayesian statistics, named for Thomas Bayes, an 18th-century English Presbyterian minister who developed rules for weighing the likelihood of different events and their expected outcomes. In the 1960s, Harvard Business School Professor Howard Raiffa popularized the application of Bayesian analysis in a business context. Managers influenced by Bayesian theory make decisions based on a rigorous calculation of the probabilities of all the possible outcomes. By weighting the value of each outcome by the probability and summing the totals, Bayesian analysis calculates an “expected value” for any given decision. The technique teaches managers to accept decisions with positive expected values and avoid those with negative ones.
The Gambling Instinct
Unfortunately, making decisions on the basis of an expected value is not very intuitive for most people. Consider a coin toss. You are offered a bet by which you’ll receive $100,000 if the coin lands on heads, but you must pay $50,000 if it lands on tails. Although the expected value of this bet is a positive $25,000 ([50% x $100,000] – [50% x $50,000]), few people would rush to take the wager. The potential downside — losing $50,000 — is simply too great.
However, many decision makers who would reject the high-stakes gamble on the single flip of a coin might accept a situation that redefines the gamble, based upon the results of 100 flips of the same coin. Although the expected value of each individual flip remains $25,000, the chance of a major loss is now extremely low.
Even those with limited mathematical training recognize that the acceptance of 100 independent coin tosses lowers risk; it’s the logic that underlies diversified stock portfolios. But despite the appeal of portfolio betting, Bayesian decision analysis faults the intuition behind it as the “fallacy of large numbers,” since the single bet always has the same expected value of $25,000, whether it is part of a portfolio or not. Paul Samuelson, the 1970 Nobel laureate in economics, showed that even though our intuition tells us to reject the one-shot bet and to accept the portfolio of bets, it is logically inconsistent to do so.