He may not have been able to articulate the patterns or describe their features, but he was relying on the pattern-matching process to let him feel comfortable that he had the situation scoped out.’
A set of painstaking interviews with the nurses in the neonatal unit provided the same insights. In essence, the nurses were relying on their deep knowledge of perceptual cues, each one subtle, but which together signalled an infant in distress. The same mental process is used by pilots, military generals, detectives – you name it. It is also true, as we have seen, of top sportsmen. What they all have in common is long experience and deep knowledge.
For years, knowledge was considered relatively unimportant in decision-making. In experiments, researchers would choose participants with no prior experience of the area under examination in order to study the ‘cognitive processes of learning, reasoning, and problem solving in their purest forms’. The idea was that talent – superb general reasoning abilities and logical prowess – rather than knowledge makes for good decision-makers.
This was the presumption of top business schools and many leading companies, too. They believed they could churn out excellent managers who could be parachuted into virtually any organization and transform it through superior reasoning.
Experience was irrelevant, it was said, so long as you possessed a brilliant mind and the ability to wield the power of logic to solve problems. This approach was seriously misguided. When Jeff Immelt became the chief executive of General Electric in 2001, he commissioned a study of the best-performing companies in the world. What did they have in common? According to Geoff Colvin in Talent Is Overrated, ‘These companies valued “domain expertise” in managers – extensive knowledge of the company’s field. Immelt has now specified “deep domain expertise” as a trait required for getting ahead at GE.’
These insights have not just become central to modern business strategy; they also form the basis of artificial intelligence. In 1957 two computer experts created a programme they called the General Problem Solver, which they billed as a universal problem-solving machine. It did not have any specific knowledge, but possessed a ‘generic solver engine’ (essentially, a set of abstract inference procedures) that could, it was believed, tackle just about any problem.
But it was soon realized that knowledge-free computing – however sophisticated – is impotent. As Bruce Buchanan, Randall Davis, and Edward Feigenbaum, three leading researchers in artificial intelligence, put it: ‘The most important ingredient in any expert system is knowledge. Programmes that are rich in general inference methods – some of which may even have some of the power of mathematical logic – but poor in domain-specific knowledge can behave expertly on almost no tasks.’
Think back to the firefighters. Many young men and women are drawn to the profession because they think they’re good at making decisions under pressure, but they quickly discover they just can’t cut it. When they look at a raging fire, they are drawn to the colour and height of the flames and other perceptually salient features, just like the rest of us. Only after a decade or more of on-the-job training can they place what they are seeing within the context of an interwoven understanding of the patterns of fires.
The essential problem regarding the attainment of excellence is that expert knowledge simply cannot be taught in the classroom over the course of a rainy afternoon, or indeed a thousand rainy afternoons (the firefighters studied by Klein had an average of twenty-three years experience). Sure, you can offer pointers of what to look for and what to avoid, and these can be helpful. But relating the entirety of the information is impossible because the cues being processed by experts – in sport or elsewhere – are so subtle and relate to each other in such complex ways that it would take forever to codify them in their mind-boggling totality. This is known as combinatorial explosion, a concept that will help to nail down many of the insights of this chapter.
The best way to get a sense of the strange power of combinatorial explosion is to imagine folding a piece of paper in two, making the paper twice as thick. Now repeat the process a hundred times. How thick is the paper now? Most people tend to guess in the range of a few inches to a few yards. In fact the thickness would stretch eight hundred thousand billion times the distance from Earth to the sun.
It is the rapid escalation in the number of variables in many real-life situations – including sport – that makes it impossible to sift the evidence before making a decision: it would take too long. Good decision-making is about compressing the informational load by decoding the meaning of patterns derived from experience. This cannot be taught in a classroom; it is not something you are born with; it must be lived and learned. To put it another way, it emerges through practice.
As Paul Feltovich, a researcher at the Institute for Human and Machine Cognition at the University of West Florida, has explained: ‘Although it is tempting to believe that upon knowing how the expert does something, one might be able to teach this to novices directly, this has not been the case. Expertise is a long-term developmental process, resulting from rich instrumental experiences in the world and extensive practice. These cannot simply be handed to someone.’
All of which hints at the decisive advantage held by Kasparov over his machine opponent. Deep Blue had all the ‘talent’: the ability to search moves at a rate measured in tens of millions per second. But Kasparov, although limited to a derisory three moves per second, had the knowledge. A deep, fertile, and endlessly elaborate knowledge of chess: the configurations of real games, how they can be translated into successful outcomes, the structure of defensive and offensive positions, and the overall construction of competitive chess. Kasparov could look at the board and see what to do in the same way an experienced firefighter can confront a blazing building and see what to do. Deep Blue can’t.
It is worth noting something else here. You’ll remember that SF, the person who performed so well on the digit span task, was able to remember more than eighty numbers by relating them to his experiences as a competitive runner. The numbers 9 4 6 2, for example, became 9 minutes, 46.2 seconds – a very good time for running two miles. SF’s retrieval structure was, in effect, an ad hoc device derived from his life beyond the test.
Kasparov’s memory of chess positions, on the other hand, is embedded in the living, breathing reality of playing chess. When he sees a chessboard, he does not chunk the pattern by relating it to an altogether different experience but by perceiving it immediately as the Sicilian Defence or the Latvian Gambit. His retrieval structure is rooted within the fabric of the game. This is the most powerful type of knowledge, and is precisely the kind possessed by firefighters, top sportsmen, and other experts.
By now it should be obvious why Deep Blue’s gigantic advantage in processing speed was not sufficient to win – combinatorial explosion. Even in a game as simple as chess, the variables rapidly escalate beyond the capacity of any machine to compute. There are around thirty ways to move towards the beginning of a game, and thirty ways in which to respond. That amounts to around 800,000 possible positions after two moves each. A few moves after that, and the number of positions are measured in trillions. Eventually, there are more possible positions than there are atoms in the known universe.
To be successful, a player must cut down on the computational load by ignoring moves unlikely to result in a favourable outcome and concentrating on those with greater promise. Kasparov is able to do this by understanding the meaning of game situations. Deep Blue is not.
As Kasparov put it after winning game two of the six-game match: ‘Had I been playing the same game against a very strong human I would have had to settle for a draw. But I simply understood the essence of the end game in a way the computer did not. Its computational power was not enough to overcome my experience and intuitive appreciation of where the pieces should go.’
Gary Klein, the psychologist who studied the firefighters, wanted to double-check whether
