Here’s a great excerpt from Michael Mauboussin’s book – Think Twice: Harnessing the Power of Counterintuition, in which Mauboussin is discussing what he calls the expert squeeze, and when is the best time to utilize man versus machine, saying:
As networks harness the wisdom of crowds and computing power grows, the ability of experts to add value in their predictions is steadily declining. I call this the expert squeeze, and evidence for it is mounting. Despite this trend, we still pine for experts—individuals with special skill or know-how—believing that many forms of knowledge are technical and specialized. We openly defer to people in white lab coats or pinstripe suits, believing they hold the answers, and we harbor misgivings about computer generated outcomes or the collective opinion of a bunch of tyros.
The expert squeeze means that people stuck in old habits of thinking are failing to use new means to gain insight into the problems they face. Knowing when to look beyond experts requires a totally fresh point of view, and one that does not come naturally. To be sure, the future for experts is not all bleak. Experts retain an advantage in some crucial areas. The challenge is to know when and how to use them.
So how do you, as a decision maker, manage the expert squeeze? The first step: carefully consider the problem you face. Figure 3-1 helps to guide this process. The second column from the left covers problems that have rules-based solutions with limited possible outcomes. Here, someone can investigate the problem based on past patterns and write down rules to guide decisions.
Experts do well with these tasks, but once the principles are clear and well defined, computers are cheaper and more reliable. Think of tasks such as credit scoring or simple forms of medical diagnosis. Experts agree about how to approach these problems because the solutions are transparent and for the most part tried and true.
The value of experts
Experts are initially important for these problems because they figure out the rules, or algorithms, that work. Think of Ashenfelter. Yet the underlying order is not always obvious. Sometimes experts must use statistical methods to find structure in the system, but once they do, the silicon can take over.
The experience of Harrah’s Casino in the early 2000s is a good illustration. For years, Harrah’s, like other casinos, fawned over people who played at the high-stakes tables—the high rollers. However, a careful study of customer data revealed it was the middle-aged and senior adults with discretionary time and income who added the most value. So the executives used the data to create even greater loyalty from their best customers, while still managing the high rollers effectively. The conventional wisdom that the expert executives had perpetuated—that high rollers were the highest value customers—was flat wrong, but only revealed by a new look at the data.
Now let’s go to the opposite extreme, the column on the far right that deals with probabilistic fields with a wide range of outcomes. Here are no simple rules. You can only express possible outcomes in probabilities, and the range of outcomes is wide. Examples include economic and political forecasts. The evidence shows that collectives outperform experts in solving these problems.
For instance, economists are extremely poor forecasters of interest rates, often failing to accurately guess the direction of rate moves, much less their correct level. Note, too, that not only are experts poor at predicting actual outcomes, they rarely agree with one another. Two equally credentialed experts may make opposite predictions and, hence, decisions from one another.
One example is the forecasting of oil prices. In one camp are experts like Matthew Simmons, an investment banker and consultant specializing in energy, who argues that the world has reached its peak of oil extraction and that oil prices are likely to rise as a consequence. In the other camp are experts including Daniel Yergin, an economic researcher, who argues that technology will make it possible to find new sources of oil and to extract them profitably. Both camps have smart and persuasive experts but come to opposite conclusions about the direction of future prices.
The middle two columns are the remaining province for experts. Experts do well with rules-based problems with a wide range of outcomes because they are better than computers at eliminating bad choices and making creative connections between bits of information. Eric Bonabeau, a physicist who now consults with businesses, has developed programs that combine computers and experts to find solutions for packaging design. Bonabeau uses the computer to generate alternatives using the principles of evolution (recombination and mutation) and has experts select the best designs for the next generation (selection). The computers are effective at creating the design alternatives but have no taste. Large consumer product companies including Procter & Gamble and Pepsi-Cola have successfully used this technique to make their products stand out.
Still, computers will continue to make inroads into this column as their performance improves. Consider that until relatively recently, no computer could beat the world chess champion. But Deep Blue, IBM’s chess-playing supercomputer, beat Garry Kasparov, the world champion from 1985 to 2000, in a six-game match in 1999. Yet humans still dominate computer programs in the game of Go, which has simple rules but allows many more position combinations than chess because of its larger nineteen-by-nineteen board. Here it is only a matter of time. As computing power becomes greater and cheaper, silicon will win this battle as well. Table 3-1 shows how computers stack up versus humans in various games.
For problems that are probabilistic with a limited range of outcomes, the verdict for experts is mixed. Computers and crowds fare poorly if they lack domain-specific knowledge. For instance, an expert coach will probably create a better game plan than a computer because he can draw on the unique knowledge of his team and the competition. Similarly, an executive may be able to better shape strategy for her corporation.
Source: Matthew L. Ginsberg, “Computers, Games and the Real World,” Scientific American Presents: Exploring Intelligence 9, no. 4 (1998): 84–89.
Man versus machine: Where is the advantage?
Once you have properly classified a problem, turn to the best method for solving it. As we will see, computers and collectives remain underutilized guides for decision making across a host of realms including medicine, business, and sports. That said, experts remain vital in three capacities. First, experts must create the very systems that replace them. Severts helped design the prediction market that outperforms Best Buy’s in-house forecasters. Until Ashenfelter came along, evaluating Bordeaux’s red wines was in large part subjective. Of course, the experts must stay on top of these systems, improving the market or equation as need be.
Next, we need experts for strategy. I mean strategy broadly, including not only day-to-day tactics but also the ability to troubleshoot by recognizing interconnections as well as the creative process of innovation, which involves combining ideas in novel ways. Decisions about how best to challenge a competitor, which rules to enforce, or how to recombine existing building blocks to create novel products or experiences are jobs for experts.
Finally, we need people to deal with people. A lot of decision making involves psychology as much as it does statistics. A leader must understand others, make good decisions, and encourage others to buy in to the decision.
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