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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 lo
perform that task. Concurrently, the novice needs to be exposed for
specific cases, or instances, that lest the boundaries of such principles.
Generally, a novice will find a mentor to guide her through the process of
acquiring new knowledge. A fairly simple example would he someone learning
lo play chess. The novice chess player seeks a mentor to leach her the
object 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 the game.
In lime, and with much practice, the novice begins to recognise
patterns of behavior within cases and, thus, becomes a journeyman. With
more practice and exposure to increasingly complex cases, The
journeyman finds patterns not only within cases but also between
cases. 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 game-playing, strategies, and patterns of victory and
defeat.
When a journeyman starts to make and test hypotheses about future
behavior based on past experiences, she begins the next transition. Once
she creatively generates knowledge, rather than simply matching, superficial
patterns, she becomes an expert. At this point, she is confident in her
knowledge and no longer needs a mentor as a guide she becomes
responsible for her own knowledge. In the chess example, once a
journeyman begins competing against experts, makes predictions based
on patterns, and tests those predictions against actual behavior, she
is generating new knowledge and a deeper understanding of the game. She
is creating her own case, rather than relying on the cases of others.
The Power of Expertise
An expert perceives meaningful patterns in her domain better 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
thou-sands of cases.
Experts have a deeper understanding of their domains than novices do,
and utilise higher-order principles to solve- problems. A novice, for
example, might group objects together by color 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 belter than novices. Experts recognise
variables that have the largest influence on a particular problem and
focus their attention on those variables.
Experts have better domain-specific short-term and long-term memory
than novices do. Moreover, experts perform tasks in their domains faster
than novices and commit fewer errors while problem solving.
Interestingly, experts go about solving problems differently than
novices. Experts spend more time thinking, about a problem to fully
understand it at the beginning of a task than do novices, who
immediately seek to find a solution, Experts use their knowledge of
previous cases as context tor creating mental models to solve
given problems.
Better at self-monitoring than novices, experts are more aware of
instances where they have committed errors or failed to understand a
problem. Experts check their solution more often than novices and
recognise when they are missing, information necessary for solving a
problem. Experts are aware of the limits of their domain knowledge and
apply their domain’s heuristics to solve problems that fall outside of
their experience base.
The Paradox of Expertise
The strengths of expertise can also be weaknesses. Although one would
expect experts to be good forecasters, they are not particularly good at
making predictions about the future. Since the 1930s, researchers have
been testing, the ability of experts to make forecasts.
The performance of experts has been tested against actuarial tables to
determine if they are better at making predictions than simple
statistical models. Seventy years later, with more than two hundred
experiments in different domains, it is clear that the answer is no. If
sup-plied with an equal amount of data about a particular case, an
actuarial table is as good, or better, than an expert at making, calls
about the future. Even if an expert is given more specific case information
than is available to the statistical model, the expert does not tend
to outperform the actuarial table.
Theorists and researchers differ when trying, to explain why experts
are less accurate fore-casters than statistical models. Some have argued
that experts, like all humans, are inconsistent when using mental models to
make predictions. That is, the model an expert uses for predicting X in one
month is different from the model used for predicting X in a
following, month, although precisely the same case and same data set
are used in both instances.
A number of researchers point to human biases to explain unreliable
expert predictions. During, the last 30 years, researchers have
categorised, experimented, and theorised about the cognitive aspects
of forecasting. Despite such efforts, the literature shows little consensus
regarding the causes or manifestations of human bias.
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