by Gary Klein, The MIT Press, 2/26/1999, 978-0262611466
Klein runs a research group (Klein Associates, klein-inc.com) which
focuses on on decision-centered solutions. They are a human factors
company, and they use an anthropological approach to cognitive
science. The thesis of the book is that experts decide not based on
"analysis" but on a theory called recognition-primed decision (RPD)
making. The cummulative experience allows experts to perform
similarity analysis on the fly. That's how chess grand masters can
play speed chess with the same relative accuracy as they do non-speed
games, whereas novices make many more mistakes under pressure.
The book is filled with annecdotes from interviews from a variety of
situations including Klein's personal experience with RPD. The
annecdotes liven up the book, and also support his theories. However,
there's no "proof", and he clearly admits this. His argument is that
it has worked for his business, and it may work for yours.
I'd guess that Kent Beck read this book. Extreme Programming takes
many elements from Klein's book including: stories, metaphors, and
team knowledge. And, most importantly, his focus on the value of
code (experience) over plans (analysis).
[p17] Our results turned out to be fairly clear. It was not that the
commanders were refusing to compare options; rather, they did not
have to compare options. I had been so fixated on what they were not
doing that I had missed the real finding: that the commanders could
come up with a good course of action from the start. That was what the
stories were telling us. Even when faced with a complex situation, the
commanders could see it as familiar and know how to react.
The commanders' secret was that their experience let them see a
situation, even a nonroutine one, as an example of a prototype, so
they knew the typical course of action right away. Their experience
let them identify a reasonable reaction as the first one they
considered, so they did not bother thinking of others. They were not
being perverse. They were being skillful. We now call this strategy
recognition-primed decision making.
[p34] Intuition is not infallible. Our experience will sometimes
mislead us, and we will make mistakes that add to our experience
[p42] The part of intuition that involves pattern matching and
recognition of familiar and typical cases can be trained. If you want
people to size up situations quickly and accurately, you need to
expand their experience base.
[p68] I do not count it as a weakness of mental simulations that they
are sometimes wrong. My estimate is that most of the time they are
fairly accurate. Besides, they are a means of generating explanations,
not for generating proofs.
I do count it as a weakness of mental simulations that we become too
confident in the ones we construct. One reason for problems such as de
minimus explanations that discard disconfirming evidence is that once
we have built a mental simulation, we tend to fall in love with
it. Whether we use it as an explanation or for prediction, once it is
completed, we may give it more credibility than it deserves,
especially if we are not highly experienced in the area and do not
have a good sense of typicality. This "overconfidence" effect has been
shown in the laboratoty by Hirr and Sherman (1985).
[p69] Marvin Cohen (1997) believes that mental simulation is usually
self-correcting through a process he has called snap-back. Mental
simulation can explain away disconfirming evidence, but Cohen has
concluded that it is often wise to explain away mild discrepancies
since the evidence itself might not be trustworthy. However, there is
a point when we have explained away so much that the mental simulation
becomes very complicated. At this point we begin to lose fairh in the
mental simulation and reexamine it. We look at all of the new evidence
that had been explained away to see if maybe there is not another
simulation that makes more sense. Cohen believes that until we have an
alternate mental simulation, we will keep patching the original
one. We will not be motivated to assemble an alternate simulation
until there is too much to be explained away. The strategy makes
sense. The problem is that we lose track of how much contrary evidence
we have explained away so the usual alarms do not go off.
[p103] One application of the RPD model is to be skeptical of courses or
books about powerful methods for making effective decisions, thirty
days guaranteed or your money back. I doubt whether such methods
A second application of this chapter is to suggest that analytical
methods may be helpful for people who lack experience. [...]
A third application is to consider which decisions are worth
making. When options are very close together in value, we can call
this a zone of indifference: the closer together the advantages and
disadvantages of competing options, the harder it will be to make a
decision but the less it will matter. For these situations, it is
probably a waste of time to try to make the best decision. If we can
sense that we are within this zone of indifference, we should make the
choice any way we can and move on to other matters.
[p104] A fourth application is not to teach someone to use the RPD
model. There is no reason to teach someone to follow the RPD model,
since the model is descriptive. It shows what experienced decision
makers already do.
A fifth application is to improve decision skills. Because the key to
effective decision making is to build up expertise, one temptation is
to develop training to teach people to think like experts. But in most
settings, this can be too time-consuming and expensive. However, if we
cannot teach people to think like experts, perhaps we can teachthem to
learn like experts. After reviewing the literature, I identified a
number of ways that experts in different fields learn Klein (1997):
They engage in deliberate practice, so that each opportunity for
practice has a goal and evaluation criteria.
They compile an extensive experience bank.
They obtain feedback that is accurate, diagnostic, and reasonably
They enrich their experiences by reviewing prior experiences to
derive new insights and lessons from mistakes.
The first strategy is to engage in deliberate practice. In order to do
this, people must articulate goals and identify the types of judgment
and decision skills they need to improve.
The strategy of compiling an extensive experience bank appears
important. But the mere accumulation of experiences may not be
sufficient. The experiences need to include feedback that is accurate,
diagnostic, and timely. In domains were it is possible to obtain such
feedback (e.g., weather forecasting), decision-making expertise
develops. In domains that are not marked by opportunities for
effective feedback (e.g., clinical psychology), mere accumulation of
experience does not appear to result in growth of expertise.
[p154] One aspect of being able to improvise that was not discussed in
chapter 8 is the ability of experts to generate counterfactuals:
explanations and predictions that are inconsistent with the
data. Perhaps they have this ability because they have learned not to
place too heavy a reliance on data. Novices, in contrast, have
difficulty imagining a world different from the one they are seeing.
[p154] Skilled decision makers may be able to seek information more
effectively than novices. This skill in information seeking would
result in a more efficient search for data that clarify the status of
[p156] The ability to see the past and the future rests on an
understanding of the primary causes in a domain and the ability to
apply these causes to run mental simulations. This is one way to
distinguish true experts people who pretend to be experts. The
pretenders have mastered many procedures and tricks of the trade;
their actions are smooth. They show many of the characteristics of
expertise. However, if they are pushed outside the standard patterns,
they cannot improvise. They lack a sense of the dynamics of the
situation. They have trouble explaining how the current state of
affairs came about and how it will play out. They also have trouble
manetallly simulating how a different future state from the one they
predicted might evolve.
[p225] Considerations in Communicating Intent
In observing teams and reviewing their attempts to communicate goals,
I have identified a few types of information that are important for
describing intent (Klein 1994). There are seven types of information
that a person could present to help the people receiving the request
to understand what to do:
1. The purpose of the task (the higher-level goals).
2. The objective of the task (an image of the desired outcome).
3. The sequence of steps in the plan.
4. The rationale for the plan.
5. The key decisions that may have to be made.
6. Antigoals (unwanted outcomes).
7. Constraints and other considerations.
[p226] All seven rypes of information are not always
necessary. Instead, this list can be used as a checklist, to determine
if there are any more details to add. In my company, whenever we begin
a new project, we go through the relevant items in the checklist. We
try to make sure that everyone working on the project has the same
understanding of what we are after.
[p283] One way to improve performance is to be more careful in
considering alternate explanations and diagnoses for a situation. The
de minimus [p284] error may arise from using mental simulation to
explain away cues that are early warnings of a problem. One exercise
to correct this tendency is to use the crystal ball technique
discussed in chapter 5. The idea is that you can look at the
situation, pretend that a crystal ball has shown that your explanation
is wrong, and try to come up with a different explanation. Each time
you stretch for a new explanation, you are likely to consider more
factors, more nuances. This should reduce fixation on a single
explanation. The crystal ball method is not well suited for
time-pressured conditions. By practicing with it when we have rhe
time, we may learn what it feels like to fixate on a hypothesis. This
judgment may help us in situations of time pressure.
A second application is to accept all errors as inevitable. In complex
situations, no amount of effort is going to be able to prevent any
errors. Jens Rasmussen (1974) came to this conclusion in his work with
nuclear power plants, which is one of the industries most preoccupied
with safery. He pointed out that the rypical method for handling error
is to erect defenses that make the errors less and less likely: add
warnings, safeguards, autOmatic shut-offs, and all kinds of other
defenses. These do reduce the number of errors, but at a cost, and
errors will continue to be made, and accidents will continue to
happen. In a massively defended system, if an accident sneaks through
all the defenses, the operators will find it far more difficult to
diagnose and correct it. That is because they must deal with all of
the defenses, along with the accident itself. Recall example 13.7, the
flight mismanagement system. A unit designed to reduce small errors
helped to create a large one.
Since defenses in depth do not seem to work, Rasmussen suggests a
different approach: instead of erecting defenses, accept malfunctions
and errors, and make their existence more visible. We can try to
design better human-system interfaces that let the system operators
quickly notice that something is going wrong and form- diagnoses and
reactions. Instead of trusting the systems (and, by extension, the
cleverness of the design engineers) we can trust the competence of the
operators and make sure they have the tools to maintain situation
awareness throughout the incident.