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-primeddecisionmaking.
[p34] Intuition is not infallible. Our experience will sometimes mislead us, and we will make mistakes that add to our experience base.
[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 exist. […]
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 timely.
- 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 the situation.
[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.
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:
- The purpose of the task (the higher-level goals).
- The objective of the task (an image of the desired outcome).
- The sequence of steps in the plan.
- The rationale for the plan.
- The key decisions that may have to be made.
- Antigoals (unwanted outcomes).
- 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.