Gary Klein’s Sources of Power: How People Make Decisions


Jason Collins


January 17, 2019

Summary: An important book describing how many experts make decisions, but with a lingering question mark about how good these decisions actually are.

Gary Klein’s Sources of Power: How People Make Decisions is somewhat of a classic, with the version I read being a 20th anniversary edition issued by MIT Press. Klein’s work on expert decision making has reached a broad audience through Malcolm Gladwell’s Blink, and Klein’s adversarial collaboration with Daniel Kahneman (pdf) has given his work additional academic credibility.

However, throughout the growing application of behavioural science in public policy and the private sphere, I have rarely seen Klein’s work practically applied to improve decision making. The rare occasions where I see it referenced typically involve an argument that the conditions for the development of expertise do not exist in a particular domain.

This lack of application partly reflects the target of Klein’s research. Sources of Power is an exploration of what Klein calls naturalistic decision making. Rather than studying novices performing artificial tasks in the laboratory, naturalistic decision making involves the study of experienced decision makers performing realistic tasks. Klein’s aim is to document the strengths and capabilities of decision makers in natural environments with high stakes, such as lost lives or millions of dollars down the drain. It often involves uncertainty or missing information. The goals may be unclear. Klein’s focus is therefore in the field and the decisions of people such as firefighters, nurses, pilots and military personnel. They are typically people who have had many years of experience. They are “experts”.

The exploration of naturalistic decision making contrasts with the heuristics and biases program, which typically focuses on the limitations of decision makers and is the staple fodder of applied behavioural scientists. Using the findings of experimental outputs from the heuristics and biases program to tweak decision environments and measure the response across many decision makers (typically through a randomised controlled trial) is more tractable than exploring, modifying and measuring the effect of interventions to improve the rare, high-stakes decisions of experts in environments where the goal itself might not even be clear.

Is Klein’s work “science”?

The evidence that shapes Sources of Power was typically obtained through case interviews with decision makers and by observing these decision makers in action. There are no experiments, with the data obtained through interviews. The interviews are coded for analysis to attempt to find patterns in the approaches of the decision makers.

Klein is cognisant of the limitations of this approach. He notes that he gives detailed descriptions of each study so that we can judge the weaknesses of his approach ourselves. This brings his approach closer to what he considers to be a scientific piece of research. Klein writes:

What are the criteria for doing a scientific piece of research? Simply, that the data are collected so that others can repeat the study and that the inquiry depends on evidence and data rather than argument. For work such as ours, replication means that others could collect data the way we have and could also analyze and code the results as we have done.

The primary “weakness” of his approach is the reliance on observational data, not experiments. As Klein suggests, there are plenty of other sciences that have this feature. His approach is closer to anthropology that psychology. But obviously, an approach constrained to the laboratory has its own limitations:

Both the laboratory methods and the field studies have to contend with shortcomings in their research programs. People who study naturalistic decision making must worry about their inability to control many of the conditions in their research. People who use well-controlled laboratory paradigms must worry about whether their findings generalize outside the laboratory.

Klein has a faith in stories (the subject of one of the chapters) serving as natural experiments linking a network of causes to their effects. It is a fair point that stories can be used to communicate subtle points of expertise, but using them to reliably identify cause-effect relationships seems a step too far.

Recognition-primed decision making

Klein’s “sources of power” for decision-making by experts are intuition, mental simulation, metaphor and storytelling. This is in contrast to what might be considered a more typical decisions-making toolkit (the one you are more likely to be taught) of logical thinking, probabilistic analysis and statistics.

Klein’s workhorse model integrating these sources of power is recognition-primed decision making. This is a two stage process, involving an intuitive recognition of what response is required, followed by mental simulation of the response to see if it will work. Metaphors and storytelling are mental simulation tools. The recognition-primed model involves a blend of intuition and analysis, so is not just sourced from gut feelings.

From the perspective of the decision maker, someone using this model might not consider that they are making a decision. They are not generating options and then evaluating them to determine the best choice.

Instead, they would see their situation as a prototype for which they know the typical course of action right away. As their experience allowed them to generate a reasonable response at the first instance, they do not need to think of others. They simply evaluate the first option, and if suitable, execute. A decision was made in that alternative courses of action were available and could have been chosen. But there was no explicit examination across options.

Klein calls this process singular evaluation, as opposed to comparative evaluation. Singular evaluation may involve moving through multiple options, but each is considered on its own merits sequentially until a suitable option is found, with the search stopping at that point.

The result of this process is “satisficing”, a term coming from Herbert Simon. These experts do not optimise. They pick the first option that works.

Klein’s examination of various experts found that the recognition-primed decision model was the dominant mode of decision making, despite his initial expectation of comparative evaluation. For instance, fireground commanders used recognition-primed decision making for around 80% of the decisions that Klein’s team examined. Klein also points to similar evidence of decision making by chess grandmasters, who spend little time comparing the strengths and weaknesses of one move to another. Most of their time involves simulating the consequences and rejecting moves.

Mental simulation

Mental simulation involves the expert imagining the situation and transforming the situation until can they picture it in a different way from the start. Mental simulations are typically not overly elaborate, and generally rely on just a few factors (rarely more than three). The expert runs the simulation and assesses: can it pass an internal evaluation? Sometimes mental simulation can be wrong, but Klein considers them to be fairly accurate.

Klein’s examples of mental simulation were not always convincing. For example, he describes an economist who mentally simulated what the Polish economy would do following interventions to reduce inflation. It is hard to take seriously single examples of such mental simulation hitting the mark when I am aware of so many backfires in this space. And how would expertise in such economic simulations develop? (More on developing expertise below.)

One strength of simulations is that they can be used where traditional decision analytic strategies do not apply. You can use simulations (or stories) if you cannot otherwise remember every piece of information. Klein points to evidence that this is how juries absorb evidence.

One direct use of simulation is the premortem strategy. Imagine in the future plan has failed and you have to understand why. You can also do simulation through decision scenarios.

Novices versus experts

Expertise has many advantages. Klein notes experts can see the world differently, have more procedures to apply, notice problems more quickly, generate richer mental simulations and have more analogies to draw on. Experts can see things that novices can’t. They can see anomalies, violations of expectancies, the big picture, how things work, additional opportunities and improvisations, future events, small differences, and their own limitations.

Interestingly, while experts tend not to carefully deliberate about the merits of different courses of action, novices need to compare different approaches. Novices are effectively thinking through the problem from scratch. The rational choice method helps us when we lack the expertise to assess a situation.

Another contrast is where effort is expended. Experts spend most of their effort on situation assessment - this gives the answers. Novices spend more time on determining the course of action.

One interesting thread concerned what happened when time pressure was put on chess players. Time constraints barely degraded the performance of masters, while it destroyed that of novices. The masters often came up with their best move first, so there is no need for the time to test a lot of options.

Developing good decision making

Given the differences between novices and experts, how should novices develop good decision making? Klein suggests this should not be done through training in formal methods of analysis. In fact, this could get in the way of developing expertise. There is also no need to teach the recognition-primed model as it is descriptive: it shows what good decision makers already do. We shouldn’t teach people to think like experts.

Rather, we should teach people to learn like experts. They should engage in deliberate practice, obtain feedback that is accurate and timely, and enrich learning by reviewing prior experience and examining mistakes. The intuition that drives recognition grows out of experience.

Recognition versus analytical methods

Klein argues that recognition strategies are not a substitute for analytical methods, but an improvement. Analytical methods are the fallback for those without experience.

Klein sees a range of environments where recognition strategies will be the superior options. These include the presence of time pressure, when the decision maker is experienced in the domain, when conditions are dynamic (meaning effort can be rendered useless if conditions shift), and when the goals ill-defend (making it hard to develop evaluation criteria). Comparative evaluation is more useful where people have to justify choice, where it is required for conflict resolution, where you are trying to optimise (as opposed to finding just workable option), and where the decision is computationally complex (e.g. investment portfolio).

From this, it is hard to use a rigorous analytical approach in many natural settings. Rational, linear approaches run into problems when the goal is shifting or ill-defined.

Diagnosing poor decisions

I previously posted some of Klein’s views on the heuristics and biases approach to assessing decision quality. Needless to say, Klein is sceptical that poor decisions are largely due to faulty reasoning. More effort should be expended in finding the sources of poor decisions, rather than blaming the operator.

Klein describes a review a sample of 25 decisions with poor outcomes (from 600 he had available) to assess what went wrong. Sixteen outcomes were due to lack of experience, such as someone not realising that construction of the building on fire was problematic. The second most common issue was lack of information. The third most common involved noticing but explaining away problems during mental simulation - possibly involving bias.

Conditions for expertise

The conditions for developing the expertise for effective recognition-primed decision making is delved into in depth in Klein’s article with Daniel Kahneman, Conditions for Intuitive Exertise: A Failure to Disagree (pdf). However, Klein does examine this area to some degree in Sources of Power.

Klein notes that it is one thing to gain experience, and another to turn that into expertise.  It is often difficult to see cause and effect relationships. There is typically delay between the two. It is difficult to disentangle luck and skill. Drawing on work by Jim Shanteau, Klein also notes that expertise was hard to develop when the domain is dynamic, we need to predict human behaviour, there is less chance for feedback, there is not enough repetition to get sense of typicality or there are fewer trials. Funnily enough, this description seems to align somewhat with many of the naturalistic decision making environments.

Despite these barriers, Klein believes that it is possible to get expertise in some areas, such as fighting fires, caring for hospitalised infants or flying planes. Less convincingly (given some research in the area), he also references the fine discrimination of wine tasters (e.g.).

Possibly my biggest criticism of Klein’s book relates to this final point, as he provides little evidence for the conversion of experience into expertise beyond the observation that in many of these domains novices are completely lost. Is the best benchmark a comparison with a novice who has no idea, or is it better to look at, say, a simple algorithm, statistical rule, or someone with basic training?