The Harvard Business Review’s October 2016 Issue includes a deep look at decision making by authors Kahneman, Rosenfield, Ghandhi, and Blaser. Their conclusion: “noise”, left unchecked, renders decision making highly inconsistent.
At a global financial services firm we worked with, a longtime customer accidentally submitted the same application file to two offices. Though the employees who reviewed the file were supposed to follow the same guidelines—and thus arrive at similar outcomes—the separate offices returned very different quotes. Taken aback, the customer gave the business to a competitor. From the point of view of the firm, employees in the same role should have been interchangeable, but in this case they were not. Unfortunately, this is a common problem.
Professionals in many organizations are assigned arbitrarily to cases: appraisers in credit-rating agencies, physicians in emergency rooms, underwriters of loans and insurance, and others. Organizations expect consistency from these professionals: Identical cases should be treated similarly, if not identically. The problem is that humans are unreliable decision makers; their judgments are strongly influenced by irrelevant factors, such as their current mood, the time since their last meal, and the weather. We call the chance variability of judgments noise. It is an invisible tax on the bottom line of many companies.
Some jobs are noise-free. Clerks at a bank or a post office perform complex tasks, but they must follow strict rules that limit subjective judgment and guarantee, by design, that identical cases will be treated identically. In contrast, medical professionals, loan officers, project managers, judges, and executives all make judgment calls, which are guided by informal experience and general principles rather than by rigid rules. And if they don’t reach precisely the same answer that every other person in their role would, that’s acceptable; this is what we mean when we say that a decision is “a matter of judgment.” A firm whose employees exercise judgment does not expect decisions to be entirely free of noise. But often noise is far above the level that executives would consider tolerable—and they are completely unaware of it.
The prevalence of noise has been demonstrated in several studies. Academic researchers have repeatedly confirmed that professionals often contradict their own prior judgments when given the same data on different occasions. For instance, when software developers were asked on two separate days to estimate the completion time for a given task, the hours they projected differed by 71%, on average. When pathologists made two assessments of the severity of biopsy results, the correlation between their ratings was only .61 (out of a perfect 1.0), indicating that they made inconsistent diagnoses quite frequently. Judgments made by different people are even more likely to diverge. Research has confirmed that in many tasks, experts’ decisions are highly variable: valuing stocks, appraising real estate, sentencing criminals, evaluating job performance, auditing financial statements, and more. The unavoidable conclusion is that professionals often make decisions that deviate significantly from those of their peers, from their own prior decisions, and from rules that they themselves claim to follow.
My emphasis added. Among the author’s proposed solutions to the “noise” problem was the was following:
The most radical solution to the noise problem is to replace human judgment with formal rules—known as algorithms—that use the data about a case to produce a prediction or a decision. People have competed against algorithms in several hundred contests of accuracy over the past 60 years, in tasks ranging from predicting the life expectancy of cancer patients to predicting the success of graduate students. Algorithms were more accurate than human professionals in about half the studies, and approximately tied with the humans in the others. The ties should also count as victories for the algorithms, which are more cost-effective.
This will sound very similar to advice that Dorsey Wright has been giving for many years: Embrace models! Try as we might to be consistent, without the framework of a systematic investment model, our own subjective decision making will be all over the place. Then, how can we tell if our investment success or failure is the result of skill or just good or bad luck? Of course, you can’t simply blindly adhere to just any systematic investment model. The decision rules upon which the model has been built must stack the odds in your favor. Extensive testing, as is detailed here, has give us the necessary input to build systematic relative strength strategies that “dial down the noise” and allow us to focus on execution of a well-designed investment process.
Focus on the process and the results will take care of themselves.
The relative strength strategy is NOT a guarantee. There may be times where all investments and strategies are unfavorable and depreciate in value.