BECOMING AN EXPERT
Expertise is commitment coupled with creativity. Specifically, it is the commitment of time, energy, and resources to a relatively narrow field of study, and the creative energy necessary to generate new knowledge in that field. It takes a considerable amount of time and regular exposure to a large number of cases to become an expert.
An individual enters a field of study as a novice. The novice needs to learn the guiding principles and rules of a given task in order to perform that task. Concurrently, the novice must be exposed to specific cases, or instances, that test the boundaries of such principles. Generally, a novice will find a mentor to guide them through the process of acquiring new knowledge. A simple example would be someone learning to play chess. The novice chess player seeks a mentor to teach them the objective of the game, the number of spaces, the names of the pieces, the function of each piece, how each piece is moved, and the necessary conditions for winning or losing.
In time, and with much practice, the novice begins to recognise patterns of behaviour within cases and, thus, becomes a journeyman. With more practice and exposure to increasingly complex cases, the journeyman identifies patterns not only within cases but also between them. More importantly, the journeyman learns that these patterns often repeat themselves over time. The journeyman still maintains regular contact with a mentor to solve specific problems and learn more complex strategies. Returning to the example of the chess player, the individual begins to learn patterns of opening moves, offensive and defensive strategies, and patterns of victory and defeat.
When a journeyman starts to make and test hypotheses about future behaviour based on past experiences, they begin the next transition. Once they creatively generate knowledge, rather than simply matching superficial patterns, they become an expert. At this point, they are confident in their knowledge and no longer need a mentor as a guide—they become responsible for their own learning. In the chess example, once a journeyman begins competing against experts, makes predictions based on patterns, and tests those predictions against actual outcomes, they are generating new knowledge and gaining a deeper understanding of the game. They are creating their own cases rather than relying on the cases of others.
The chess example is a brief description of an apprenticeship model. Apprenticeship may seem like a restrictive 18th-century mode of education, but it remains a standard method of training for many complex tasks. Academic doctoral programmes are based on an apprenticeship model, as are fields like law, music, engineering, and medicine. Graduate students enter fields of study, find mentors, and begin the long process of becoming independent experts and generating new knowledge in their respective domains.
Psychologists and cognitive scientists agree that the time it takes to become an expert depends on the complexity of the task and the number of cases, or patterns, to which an individual is exposed. The more complex the task, the longer it takes to build expertise—or, more accurately, the longer it takes to experience and store a large number of cases or patterns.
THE POWER OF EXPERTISE
An expert perceives meaningful patterns in their domain more effectively than non-experts. Where a novice perceives random or disconnected data points, an expert connects regular patterns within and between cases. This ability to identify patterns is not an innate perceptual skill; rather, it reflects the organisation of knowledge after exposure to and experience with thousands of cases. Experts have a deeper understanding of their domains than novices, and utilise higher-order principles to solve problems. A novice, for example, might group objects together by colour or size, whereas an expert would group the same objects according to their function or utility. Experts comprehend the meaning of data and weigh variables with different criteria within their domains better than novices. Experts recognise variables that have the greatest influence on a particular problem and focus their attention accordingly.
Experts have better domain-specific short-term and long-term memory than novices. Moreover, experts perform tasks in their domains more quickly and commit fewer errors while problem-solving. Interestingly, experts approach problems differently than novices. They spend more time thinking about a problem to fully understand it at the beginning, while novices often attempt to find a solution immediately. Experts use their knowledge of previous cases as a framework to build mental models for solving current problems.
Better at self-monitoring than novices, experts are more aware of instances where they have committed errors or misunderstood a problem. They check their solutions more often and recognise when they lack the necessary information to solve a problem. Experts are aware of the limits of their domain knowledge and apply domain-specific heuristics to solve problems beyond their direct experience.
THE PARADOX OF EXPERTISE
The strengths of expertise can also be weaknesses. Although one might expect experts to be good forecasters, they are not particularly accurate at predicting the future. Since the 1930s, researchers have tested the forecasting ability of experts. The performance of experts has been compared to actuarial tables to determine if they outperform statistical models. After more than 200 experiments across different fields over seventy years, the conclusion is clear: they do not.
If provided with an equal amount of data about a particular case, an actuarial table is as good as—or better than—an expert at making predictions. Even if the expert receives more specific information than the statistical model, they still tend not to outperform the model.
Theorists and researchers differ in explaining why experts are less accurate forecasters. Some argue that experts, like all humans, are inconsistent when using mental models to predict outcomes. Others point to human biases to explain these inaccuracies. Over the past three decades, researchers have categorised and theorised about the cognitive aspects of forecasting. However, despite these efforts, the literature shows little agreement regarding the causes or nature of human bias.