The Transfer Challenge to Expertise

Consider this relatively commonsensical view of problem solving:

“We absorb information in some common generic way and then apply our individual talents to using that information to solve problems.”

As straightforward and intuitive as this description sounds, it contains a counter-productive assumption about knowledge, confusing it with information. We tend to assume that the things we experience are experienced in the same way by other people. We might all look at the same world, but we make different observations and draw different conclusions from it. The knowledge base we each build over our lifetime is not just a straightforward result of the information presented to us; it is also in part a result of how we organize that information, which can be very different from one person to another. This is the foundation of the expertise model.

Organization Matters

It is not enough to have the right information to solve a problem, the information must also be organized in a way that lets us think about it in the right way.

Expertise is a key factor in effective problem solving because it permits us to organize information about a domain, recognize patterns in that domain, apply domain knowledge to new kinds of problems, and incorporate new information about that domain.

The expertise model gives us several key insights into the problem solving process that the commonsensical view above misses:

It tells us that the way information is organized is critical to how that information can be used for problem solving. Many problem solving principles will deal with how information is best organized to solve problems.
It tells us that in certain key areas, deep accurate understanding is important and not just superficial familiarity. Both intuitive decision making and more formal methods rely on deep accurate understanding acquired from experience.
It tells us that a great deal of deliberate practice with good feedback is needed to acquire deep understanding.

It tells us that more skillful problem solving emphasizes principles, while less skillful problem solving relies on procedures.
In spite of the tremendous power of the expertise model, it has its own limitations as well. Expertise lets us detect meaningful patterns of information in particular domains because of the way we organize our own knowledge.

The organization of domain-specific knowledge in our mind has important implications. It means that experts have differently organized knowledge for thinking in different domains. The fact that the expertise model is so thoroughly domain-specific forces us to now confront the most serious challenge of all to the expertise model: the challenge of transfer. If it takes so much deliberate practice to become good in a given domain, how does anyone manage to become good at more than a very narrow range of activities? How do our narrowly cultivated abilities support other activities? Or do we have important abilities that are not domain-specific as well? What does it take to apply our hard-won expertise to problems different than the ones we specifically practiced for?

The Failure of Mental Exercise

At one time, it was widely believed that people could develop their mind by doing mental work such as solving logic puzzles, learning mathematics, reading classics of literature, and learning to speak Latin. A long history of sometimes large scale research on this approach to mental ability revealed it to apparently have very little promise.[1] Literacy in general, while valuable for its own sake, simply does not have much effect on other thinking abilities.[2] Our ability to solve puzzles doesn’t tend to generalize very well unless the training specifically teaches the underlying patterns and provides us with a way of remembering them.[3] We don’t automatically apply the lessons of solving one problem to solving a structurally similar but different problem. The different appearance of problems tends to throw us off. When we learn strategies for solving problems, we tend to learn them in a way that is tied to the specific kinds of problems that we used for learning them. Expertise, the research confirms, tends to be very context specific.

How Transfer Does Happen: Two Roads

The problem with this result is that while it seems consistent with the expertise model, it doesn’t quite make sense in terms of our everyday experience. All of us routinely do apply what we know to new kinds of problems. We aren’t equally incompetent in dealing with any sort of novel problem; our existing expertise clearly does sometimes give us an advantage in another domain. We also see negative influence of expertise when our existing abilities interfere with our attempts to perform in a similar domain. Expertise does seem to transfer between domains under some conditions. The question is what those conditions might be.

Research done in the late 1980’s and early 1990’s confirmed that transfer of ability between domains does occur consistently under certain conditions. One cognitive psychologist working with preschool children on simple tasks discovered that the 3 and 4 year olds could use lessons they learned under one set of conditions in a completely different set of conditions, but especially if they were shown how the different problems resembled each other and how the goals were similar, they were familiar with the problem areas, the examples also had rules associated that the children figured out for themselves, and if the learning took place in a social context that specifically encouraged them to spell out the principles, explanations, and justifications to use.[4]

Research such as this led to a general two-pronged theory of transfer, proposed by David Perkins and Gavriel Salomon. The theory is based on the finding that transfer sometimes takes place between similar domains, and sometimes takes place between very dissimilar domains, and that these seem to happen under different conditions.[5]

What the Perkins and Salomon theory calls “low road transfer” happens when situations appear to us to be similar according to simple perceptual cues rather than any deep structural pattern. This seems to be a matter of stimulus triggers. Specific elements in the situation help us recognize and apply skills and knowledge from our memory based on recognizing those elements from our practice. Since low road transfer is pattern-bound, it doesn’t generally lead to transfer to different situations. Practicing under a variety of different conditions however can help is gradually stretch our skills from one context to a similar one to generalize our skills further. Low road transfer is a result of the variety of conditions under which we practice rather than any specific cognitive skills or strategies aside from those specific to the domain. Low road transfer is a perceptual-memory phenomenon.

When a situation bears a superficial similarity to one we’ve trained for, we recognize stimulus patterns and our expertise is evoked via low road transfer. This is how many people manage to drive a truck reasonably well after having learned to drive a car for example. Even though the mechanics are very different, the steering, pedals, and so on are all familiar enough to trigger our learned skills for driving. That is, until we find ourselves in a situation where the fit isn’t so good between our skills and the ones that are needed.

What the Perkins and Salomon theory calls “high road transfer” seems to be a completely different matter. High road transfer involves the deliberate and mindful abstraction of principles during practice and using more general cognitive skills and strategies to apply them to completely different situations. In high road transfer, the learner actively seeks connections between different situations in which to apply the principles they’ve learned. High road transfer is a cognitive phenomenon.

We see that the similarity mechanism of transfer is limited. It only works for relatively similar situations and it works in a very automatic and unthinking way. To apply expertise to a very differently appearing situation with underlying structural similarity (such as we might need for more abstract problem solving) we need high road transfer and we need to use abilities we associate with conscious reflection. This allows us to transfer expertise from deliberate abstraction of principles to entirely different kinds of problems.

The lesson of the transfer challenge to expertise is that expertise does not automatically apply outside of its domain. We have to very deliberately either: (1) work on practicing in widening ranges of situations to facilitate generalization or (2) work on abstracting and applying general principles mindfully from our practice, or both.

Conclusion: Transfer and Expertise

We’ve seen that expertise is a very powerful model that explains in some detail how we organize tacit knowledge for recognizing patterns and solving a particular domain of problems. This appears to explain the lion’s share of differences in human abilities in problem solving. We’ve also seen that expertise can be acquired in such a way that it can be generalized to an increasingly wider range of conditions and in a way that makes it less likely to fail catastrophically under extreme conditions, making expertise a potentially very robust resource for problem solving.

We’ve also seen that the expertise model misses a small but critical aspect of problem solving; it does not tell us how people manage to deal with surprises or with domains that are characterized by surprises. The expertise research consistently shows strong dependence on specific contexts. We do not automatically generalize our skills or strategies to new kinds of problems just by acquiring deep expertise in a domain.

Novelty offers our most serious challenge to the power of expertise. The very concept of expertise implies domain-specificity, and domain-specificity implies that expertise is honed to deal effectively with a particular range of situations. Novelty, both within a domain and outside that domain, creates problems for the standard expertise model that need to be addressed.

Novel but superficially similar situations can be handled through expertise, but only if we specifically widen our practice to deal with a broader range of conditions.

Completely novel situations in other domains that don’t resemble the ones we practice for except in terms of their underlying deep structure can be handled through expertise as well, but only by deliberate attention to learning and applying general principles as well as acquiring domain expertise.

The Story So Far: Going Beyond Expertise

The expertise model tells us how we acquire useful patterns of tacit knowledge from experience through deliberate practice with good feedback. The expertise model explains how we deal effectively with the sorts of situations where we have accumulated extensive practice. Expertise thus acquired becomes part of our intuitive understanding of situations, enhancing, modifying, and extending our existing commonsense intuitions.

The expertise model also challenges us to explain how it is that we are able to deal with extreme yet realistic conditions and novel problems even though expertise tends to be very context-specific. Applying expertise to very different situations requires deliberate mindful work at abstracting principles and applying them through our capacity for reflective thinking. This kind of reflective thinking is not adequately captured by the expertise model. Either we need to expand the expertise model to handle the challenges we have identified, or else we need a more expansive concept to describe our abilities.



[1] (Thorndike & Woodworth, 1901), (Thorndike, The influence of first year Latin upoin the ability to read English, 1923)

[2] (Scribner & Cole, 1981)

[3] (Simon & Hayes, Psychological differences among problem isomorphs, 1977)

[4] (Brown, 1989)

[5] (Perkins & Salomon, 1987), (Perkins & Salomon, Teaching for transfer, 1988), (Salomon & Perkins, 1989)

The Unpredictability Challenge to Expertise

We almost universally recognize the legitimacy of experts in a number of different domains. In many academic fields such as mathematics, sciences, history, literature, and other academic areas, some people know much more and consistently perform much better than others in tests of ability. Similarly for many professional fields and various sports and games, we recognize that there are experts who outperform the majority of us.

A key finding in modern learning and human performance research has been the discovery of how expertise is acquired.[1] This discovery became possible with the advent of cognitive science, allowing us to model the human brain as an organized collection of information rather than just a collection of behavioral patterns. As we learned about the specific differences between novices and experts[2] in each field, we discovered certain general principles that apply to a very wide range of different fields.

A formidable body of this type of research has overturned the intuitive view that novices and experts differ because some people are simply more naturally talented than others. Experts seem to perform so effortlessly that we tend to attribute great natural ability to them rather than a different kind and degree of experience. Contrary to this intuition, expertise via deliberate practice is our best model so far of individual differences in ability in a wide range of activities. Expertise is a result of experience, and not just any experience, but deliberate practice where we meet challenges in that domain, are immersed in purposeful practice, gain knowledge from other people who are already good at it, have good coaches, and benefit from quality feedback for our performance.[3]

At the same time as verifying the legitimacy of expertise in many fields and establishing the central role of deliberate practice, social environment, coaching, and feedback, we have also discovered that there are some fields where our performance doesn’t benefit from these factors.

In spite of the tremendous power of the expertise model, it has its own limitations as well. Expertise lets us detect meaningful patterns of information in particular domains because of the way we organize our own knowledge. This assumes that there are meaningful patterns to detect that human beings are capable of using effectively. This is not always the case.


Where the Experts Fail

In the 1950’s, research into medical diagnosis and prognosis revealed something shocking: presumed experts didn’t seem to predict medical outcomes any better than novices. This line of research continued over time to demonstrate that prognosis and diagnosis in clinical work in medicine was often not improved by experience when experts relied upon informal gathering of data and their trained intuitions.[4]

Professional experience and presumed expertise also seem to make no difference in predicting the outcome of psychotherapy by psychologists, who it turns out also fare no better than less trained individuals.[5]

If there is a skill to predicting medical and psychotherapeutic outcomes in general, it doesn’t seem to be acquired from the standard professional training, or typically through experience with patients, and it isn’t obvious how else it might be acquired. There is perhaps good reason why some experienced doctors seem very reluctant to make predictions about outcomes, and maybe more of them should heed this lesson.

As a result of the difficulty of prediction in areas like this, novices using simple formal statistical methods have often outperformed the experts in tests in spite of the greater experience and training of the experts (or perhaps in some cases partly because of it).

This is not by any means to imply that statistical methods are always superior to expertise, even in a particular field where clinical experience has proven less than optimal. However, it does give us good reason to pause and reflect on the meaning of this finding for the expertise model. In some domains, the best we can do for prediction is provided by a simple statistical method; and the value of expertise in particular reaches a limit fairly early on in the training for those fields.

What Makes the Value of Expertise Vary So Much?

Research into the value of expertise in different domains shows it to vary[6] with:

1.the level of inference[7] required (moderate levels of inference are more conducive to using expertise than high levels of inference),
2.whether experience or training available is adequate to confer expertise,
3.whether the conditions and instruments available allow for the expression of expertise

What this tells us is that even though expertise helps us make sense of complex situations by recognizing patterns, there is also a limit to how well acquired expertise can help us make better judgments in very complex situations. The more specialized knowledge we need in a field just to understand what is going on, the more likely it is that expertise will fail us when the situation requires a great deal of challenging inference. In the most complex fields at least, it may be that intelligence can also play an important role alongside expertise.[8]

Both intelligence and expertise play some role in every field, but each is more important to some fields than others and at different points in the development and expression of ability. The relevance of intelligence in a field seems to depend to a large degree on the role that abstract reasoning plays in success in that field. The relevance of expertise is more general. The role of expertise in a field depends on how well the situation is made comprehensible to the expert through specialized tools, the quality of their training and experience, and the kinds of conditions in which they have to perform.

Even allowing for a role for intelligence in particularly difficult technical fields requiring very high inference levels, there are fields where neither expertise nor intelligence nor any combination of the two seems to predict performance any better than simple methods.

We’ve discovered that in some areas, experts perform significantly better than non-experts and consistently outperform computer models of various kinds because of their rich background of task-relevant skills and knowledge. In .other areas, simple computer models, statistical indexes, and non-experts consistently outperform experts.

Intelligence may play more of a role in ability in highly technical domains where a high level of inference is often required in addition to recognizing important patterns. Domains are apparently not all equal with regard to what it takes to be good at them.

How Surprises Can Negate Expertise

The difference has to do with the varying role of understanding the situation for solving problems in different fields, and the role that surprise plays in each field. Fields involving things that move freely and things that scale wildly rather than behaving according to standard statistical methods[9] tend to produce surprises that can’t be managed primarily by either intelligence or expertise or both. In these areas the requisite intelligence is relatively low and the practical role of expertise is relatively marginal because reasoning doesn’t help much and it is particularly difficult to get the necessary skills even if you can identify them. So in these fields, simple statistical rules can sometimes perform as well as any expert, regardless of their IQ.

For examples of fields more or less dominated by surprises think of stockbrokers, risk management advisors, clinical psychologists, counselors, psychiatrists, admissions officers, court judges, economists, financial advisors, and intelligence analysts. Think in general of all the fields where experts fare poorly compared to non-experts, where overconfidence cancels out the benefits of expertise, or where time spent in formal practice has relatively little impact on effective outcomes.

In these fields, formal domain-specific expertise and general intelligence provide relatively little advantage in producing good outcomes compared to simple algorithms, direct local observation, direct experience, practical skills, and domain general problem solving skills. Formal expertise and intelligence in these fields especially tends to produce overconfidence more than real predictive ability. It’s not impossible that there may be some real experts in these fields who fare better than others, but they are particularly difficult to identify and train with formal methods.

Not all fields are dominated by surprises regardless of intelligence and expertise. Think of fields involving things that stay put or else move within strictly defined ranges according to physical laws or arithmetic or statistical relationships. These are much better suited to intelligence and domain-specific expertise because in those fields a better understanding of identifiable patterns and potentially complex information does tend to lead to better prediction of outcomes. Think of theoretical mathematicians and physicists, astronomers, test pilots, firefighters, livestock, grain, and soil judges, accountants, chess masters, insurance analysts (who deal with Gaussian topics like mortality), competitive athletes, and surgeons. Think in general of the many fields studied by expertise researchers where deliberate formal practice yields measureable improvements in results, and where the critical skills can be identified and trained.

We Don’t Learn Well from History

Part of the problem with expertise in fields where surprise plays an important role is that we don’t learn well from history in general. One of our consistent biases is that we systematically overweight the likelihood of events that actually happened, relative to ones that didn’t happen (but could have). This means that we have a very strong predisposition to describe events that happened as if they were fated to happen that way. This also means that we tend to think of our descriptive stories as if they were also explanations, not just descriptions. The remarkable power of stories becomes a disadvantage for explanation because the narrative content tends to replace our ability to analyze cause and effect.[10]

We become experts by being exposed to similar conditions over and over again and learning from consistent patterns in our experience. When a domain is characterized by events that are relatively uncommon yet influential, our confidence in our ability to predict events in that domain tends to grow way out of proportion to our actual ability to predict or explain the course of events. Our hindsight bias (“I knew it all along”) often kicks in to replace our missing explanatory ability.[11]

The human mind is particularly well suited to remembering and making sense of events after the fact by weaving facts into a plausible narrative, and particularly poorly suited to capturing actual frequencies of events in order to use that information in other judgments. Our common sense excels at generating plausible stories for what happens, our expertise then generates trained intuitions that add to our confidence in our explanations, but in some cases does not also add to our explanatory ability. Then history leaves us with only a single chain of events to explain, the one that actually happened. We infer from all of this that we are explaining why a sequence of events took place in a particular situation, whereas we have often only described the events, not explained them.[12]

Conclusion: Surprises and Expertise

We saw in the previous section that extremes of arousal can negate some kinds of expertise, especially expertise relying on fine motor skills. We also saw that our mindset can determine whether expert performance is retained during high arousal or fails catastrophically. In addition we saw that our ability to flexibly adapt our responses to novelty in the situation is hampered by high arousal.

Now we see that novelty offers a more general and more serious kind of challenge than just our tendency to lock in to central stimuli under high arousal. When relatively uncommon events tend to be influential in a domain, the power of expertise to help us predict and explain events is severely compromised and often even negated entirely. In these cases we have a compelling natural tendency to tell plausible stories and rely on them as explanations, and additional expertise only serves to increase our overconfidence.


[1] A June 2008 review of major trends in expertise research: (Charness & Tuffiash, 2008)

[2] An expert is typically defined for research purposes as someone who consistently performs more than two standard deviations above the mean average performance on representative tasks for their domain, assuming that ability in the field can be represented by measureable tasks and that this ability is normally distributed (Ericsson & Charness, 1994). Experts defined in this way are assumed to be roughly the top 5% of the performers in a field.

[3] The body of research has been variously summarized in popular books by journalists but a far better source for reviewing the evidence directly is the edited technical article collection: The Cambridge Handbook of Expertise and Expert Performance (Ericsson, Charness, Feltovich, & Hoffman, 2006)

[4] Classic early research showing the limits of human judgment from experience was done by Paul E. Meehl. Meehl demonstrated the limits of informal aggregation of data and prognostication by presumed experts in clinical situations such as diagnosing patients and predicting medical outcomes (Meehl, 1954). An influential review of research showing the superiority of actuarial vs. clinical judgment appeared in the journal Science in 1989: (Dawes, Faust, & Meehl, 1989)

[5] (Dawes, 1994)

[6] (Westen & Weinberger, 2005)

[7] The level of inference means the amount of specialized individual knowledge needed to understand what is going on. Situations with low levels of inference are understandable by most people, those with high levels of inference are only accurately understood by experts. Even experts utilize their abilities better in situations of lower levels of inference.

[8] There is a lot of ongoing controversy about various aspects of intelligence measurement and what it can tell us, but one of the things that most theorists agree on regarding individual differences in intelligence measurements is that they seem to correspond in some sense to our capacity to handle complexity. (Neisser, et al., 1996)

[9] This was one of the main points made by Nassim Nicholas Taleb in his entertainingly and ironically sharp book exhorting the importance of epistemological humility in the face of this sort of unpredictability in important domains, The Black Swan (Taleb, 2007)

[10] For examples in technical literature making this argument more clearly, see: (Lombrozo, 2006), and (Lombrozo, 2007). The point is made even more emphatically in (Dawes R. , 1979). Formal techniques for causal analysis take the lure of stories explicitly into account by using various methods to compensate for it and force analysts to think in causal terms rather than relying on our more natural instincts for telling stories about what happened. (Gano, 2008)

[11] A good technical article introducing hindsight bias and the related idea of “creeping determinism” (what happened is what was most likely to happen) is (Fischhoff, 1982)

[12]There is a more detailed discussion of the difference between stories and explanations in the chapter History is a Fickle Teacher in (Watts, 2011, pp. 108-134)

The Arousal Challenge to Expertise

There is a long research tradition studying the gross response of our nervous system to stimuli as a result of a response in the Reticular Activating System of the brainstem. The general response seems to be one of preparing us for activity since it involves increased heart rate and blood pressure and a condition of sensory alertness, mobility and readiness to respond. Still, too much of a good thing apparently is not so good.

The Inverted-U

The venerable Yerkes-Dodson curve[1] (sometimes referred to as “The Inverted-U”) describes performance vs. arousal in a way that shows performance being degraded under extremes of arousal, and this relationship indeed does seem to apply fairly widely.[2]

That’s the high school textbook picture, and it’s accurate for the most part. Still, in real life, upon closer inspection, arousal is a very complex phenomenon involving a number of different neurotransmitter systems in the brain and affecting different kinds of skills in different ways. For one thing, it doesn’t seem to apply equally to different activities. For another thing, it doesn’t seem to apply equally to different people. But on average, it holds up fairly well.

During the Civil War in the United States, it has been estimated that only about 25% of soldiers remembered to fire their muskets in combat. Many muskets were found with up to 5 charges in the barrel, indicating that soldiers kept reloading without firing. Most of us don’t think clearly in a crisis, we rely on simple well-learned habits that might not be what is needed for the situation at hand. A more expert marksman who doesn’t fire their weapon isn’t performing proportionately to their skill. Clearly, extreme arousal can degrade our performance as Yerkes-Dodson predicts.

What’s harder to tell from this picture is what is different about the soldiers who did fire their muskets. Were those the more expert soldiers in some sense? Or were they the more brave? Or were they different in some other way? In other words, does extreme arousal really degrade performance in general and negate differences in skill, or does it actually bring out differences in skill in greater relief, while demonstrating the importance of a different kind of skills, those less vulnerable to degradation?

A dilemma arises with the Yerkes-Dodson curve if we assume that skills break down under pressure. The skills that are preserved under high arousal seem to be the ones that we have overlearned through long practice. Yet these kind of overlearned skills are also among the hallmarks of the expert. So it isn’t obvious that expert performance should necessarily degrade under high arousal, at least not more than less expert skills. Just because experts rely on more finely honed skills doesn’t mean they should be more subject to losing those skills under stress, they may actually be less vulnerable. How do we resolve this dilemma?

Dissecting the Inverted-U: What are the Real Effects?

Perceptual Narrowing

One possibly relevant finding is that intellectually demanding tasks seem to be more degraded by arousal and that tasks requiring persistence are less degraded. The Easterbrook Cue-Utilization theory says that this is in part because an increase in arousal leads to a decrease in number of cues that can be utilized[3], an effect that has been reinforced by other research and has been called perceptual narrowing.[4]

The significance of perceptual narrowing is that it does not seem to be affected by skill level and so it may represent a way of distinguishing the more specific effect of arousal on expert performance. Experts seem to experience this kind of narrowing of the spotlight of their attention under high arousal the same as others do. The question is how it affects their performance.

The most robust effect of high arousal is that our ability to deal with surprises is significantly compromised. High arousal focuses our attention such that we are only aware of a narrow range of predictable central events and we tend to completely ignore unlikely events that would normally get some of our attention.[5] Think about it, this could be good or bad, depending on the role of surprises in the environment. Being unable to respond effectively to a soldier sneaking up on you would be a bad thing in combat. Failing to be distracted by things that don’t affect you would be a positive result.

If my expertise depends on being able to scan the environment widely and respond to novelty, then it seems it will probably be significantly compromised by high arousal. If my expertise depends on being able to focus on a narrow range of stimuli and execute well-learned skills in response to them for an extended period, then high arousal will probably enhance my performance.

Interestingly, the effects of low arousal seem roughly consistent with this model as well. Rather than being blind to things happening at the periphery, at low arousal we seem to be overly distracted by things happening outside the center of our attention, for our attention to wander.

Immediacy

Another effect of high arousal is one seen especially when we feel we are in danger. We tend to not only narrow the spotlight of our attention, but also to rely more on immediate subjective experience and to reject other sources of information that we might ordinarily consider more objective. Under high arousal and threat we tend to resort to our own immediate sensory experience and mistrust all other sources.[6] Again, this seems fairly robust and happens to experts as much as non-experts. Various military programs discovered this effect to their dismay when highly trained personnel have often abandoned their elaborate electronic information systems under combat conditions to depend on their own senses.[7]

Blocking

One more robust effect of high arousal is variability in some kinds of performance, a phenomenon originally called “blocking”[8] when it was discovered. “Blocking” refers to the appearance of occasional “blocks” where information processing for the task at hand is apparently momentarily interrupted, and decision responses are markedly slower during extended cognitive work. Since this only happens after extended work, it has been interpreted as a kind of “mental fatigue.”[9] Some theorists have interpreted this as an indication that our attention is involuntarily shifting to sources irrelevant to the task at hand.[10]

Beyond the Inverted-U: The Role of Interpretation

One way to make sense of varying performance under high arousal is to take our interpretation of the situation into account. Previous research supporting the Yerkes-Dodson law dealt with situations where the range of interpretations was probably relatively narrow. This leaves margin for us to hypothesize that our interpretation of the situation might play an additional role, even one that challenges the very shape of the Yerkes-Dodson curve.

Some recent theorists have indeed suggested that the Yerkes-Dodson curve only applies under certain conditions and that high arousal consistently improves our performance under other conditions, particularly those where we interpret the situation as an exciting challenge rather than a threat and where we perceive that we have the skills to thrive in it.

This potentially changes the relationship between expertise, arousal, and performance in a fundamental way.

According to these theories of positive psychology[11], depending on the degree of challenge we perceive and our skills for the situation, a high arousal situation can either facilitate or degrade our performance. We might experience the same situation and the same arousal level negatively as anxiety or anger on the one hand or positively as challenge and excitement on the other hand. This would determine whether the high arousal makes us perform worse or better.

For example, a more or less neutral interpretation might have an effect on performance resembling the Yerkes-Dodson law. A very negative interpretation of the situation might have a catastrophic effect on performance even worse than the Yerkes-Dodson law predicts. A very positive interpretation of the situation would have a more uniformly positive relationship of arousal and performance. In this way, the positive psychology theory of arousal and performance is thought by its proponents to explain a wide range of results.


Conclusion: Arousal and Expertise

Is arousal a serious challenge to the power of expertise?

From research consistent with Yerkes-Dodson we know that …

Low arousal can degrade performance because of our body is inadequately prepared for rigorous demands:

■Insufficient oxygenation of working muscles,
■Cooling is not functioning optimally,
■Digestion and excretion are using energy,
■Available glucose in the liver hasn’t been released,
■Alertness and readiness to respond quickly are compromised.

High arousal can degrade performance because our body is prepared for rapid, strenuous response but not for finely controlled motor skills, reasoning, strategic planning, or flexible response to changes in the situation:

■Excess muscle tension for fine control
■Some fine coordination impaired
■Perceptual narrowing
■Spontaneous attention shifts prevented
■Intermittent blocking of verbal behavior and decision making with extended effort due to “mental fatigue”

The data we’ve examined so far imply that arousal can very well negate the value of expertise under some conditions. If we’re doing surgery in a combat zone we might well have our skills compromised and a good corpsman with adequate basic skills might be as valuable as or more so than a master surgeon under those conditions. A weaker chess player might well consistently defeat much stronger players in high pressure speed matches if they have less of a tendency to “choke” under the pressure. Objective reasoning and strategic planning are significantly compromised by high arousal, especially if the arousal is negative. Extended performance of some kinds is hampered by “mental fatigue.” In even the best cases, high arousal reduces our ability to respond spontaneously and adaptively to surprises at the periphery of our activity.

This is far from a completely negative assessment of the effect of arousal on expert performance however. Experts can learn to interpret a wider range of situations as positive, possibly preventing the downside of the Yerkes-Dodson curve, can learn to rely on skills that do not require the kind of fine coordination that degrades with high arousal, can learn skills and habits that don’t require planning and reasoning, and can learn skills for managing their own arousal level. In short, in addition to their domain expertise, experts can learn to:

1.Make better use of high arousal
2.Rely on skills that don’t degrade with high arousal
3.Better manage their own arousal level

With this flexibility, arousal is a far less serious challenge to the power of expertise than it might seem from a simplistic application of the Yerkes-Dodson law.



[1] (Yerkes & Dodson, 1908)

[2] For example, see (Hockey, 1986) for a review of the evidence for general degradation of performance under high arousal conditions.

[3] (Easterbrook , 1959)

[4] (Broadbent, 1971), (Kahneman, 1973)

[5] “This is usually thought of as a reduction in the ability to deal effectively with relatively unlikely peripheral events in favor of focusing on more likely central events.” (Schmidt, 1989)

[6] (MacMillan, Entin, & Serfaty, 1994)

[7] I suppose Obi Wan Kenobi would approve since he recommended this to Luke Skywalker when he attacked the Death Star in Star Wars. Fortunately, Luke’s narrowly defined and well learned task was well suited to performance under high arousal. However in a situation where it is imperative to gather and process information more widely rather than focus on a narrow target, trusting our own senses rather than an information panel could easily become a fatal mistake.

[8] (Bills, 1931)

[9] (Bertelson & Joffe, 1963)

[10] (Broadbent, 1958)

[11] For example, see: (Csikszentmihalyi, 1998)

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The Limitations of Expertise

The Limitations of Expertise

Although it covers a very wide range of activities, the large body of expertise research takes place in areas where we can easily identify how good people are based on standards of performance within the field itself, and where, to put it bluntly, skill matters. What about performance in real world, where things are a lot messier, and the more skillful exponent doesn’t always come out on top?

This indeed turns out to be a very real issue. While having a certain amount of skill is always valuable, it isn’t always the case that being more skillful means that we perform even better. A little skill might be good, but more skill might not be better. How can this be true?

Consider these possibilities for why being more skillful might not make us perform better:

1. Extremes of Arousal: The general state of our nervous system in response to a situation can in turn affect the performance of our trained skills, although the reason for this is surprisingly poorly understood theoretically. Picture trying to drive a challenging obstacle course while very sleepy, anxious, or terrified. Extremes of arousal may plausibly affect expertise, and perhaps even negate large differences in expertise, although the effects would probably depend on some interaction of the type or activity and whether it was low or high arousal. And it turns out that the way we interpret the situation can be an important factor as well.

2. Transfer Failure: The situation at hand may resemble the situation we practiced for, but be different enough that our skills matter less. If I learn to drive a car and then manage to drive a truck, I’m transferring my skills. If I crash the truck because I can’t figure out how to operate the different controls properly or because the different response of vehicle confuses me, then we have transfer failure. My expertise doesn’t help me if it doesn’t transfer to the situation I’m in.

3. Domain Unpredictability: Some things seem to be intrinsically difficult to predict, so no amount of experience makes us better at predicting things in those domains. I don’t necessarily get better at predicting earthquakes by living through a few earthquakes, and I don’t necessarily get better at predicting slot machine payoffs by playing more, although I might learn other valuable lessons.

Despite the power of expertise across such a wide range of activities, it’s entirely possible that our performance may depend more on something other than expertise under some conditions. I’m going to examine these challenges to the power of expertise one at a time.

The Power of Expertise

The Power of Expertise

Who Ya Gonna Call?

Let’s say you’re working on your computer and it starts acting strangely. You get errors that don’t understand or it crashes for no apparent reason. If you aren’t sure what to do at first, where will you look for help? You might perform a web search for the symptoms to see if it’s a known problem and other people have solved it before you. You might run some diagnostic program or an antivirus scan because those are the tools you happen to have.

If you can’t fix it easily and you aren’t confident with computers you’ll probably start looking for help from another person at some point. Who? If it were me, I probably wouldn’t head down to the local college and find the top honors student or someone in the local Mensa chapter. I probably wouldn’t look for someone with great SAT scores or someone really good at Sudoku or even a master electrician. I’d look for someone with a lot of experience with computers and a proven track record fixing them. I’d look for an expert, and an expert specifically in that area, not just a smart person or an expert in a related area.

I stacked the deck a little bit with this question, because I picked a problem that is probably going to be technical in nature. That is, it seems like it will require some specialized knowledge to solve because it involves computers which are complicated devices that are a little mysterious to the average person and far less so for someone who has worked extensively with them.

It turns out, though, that my guess is pretty accurate for a wide range of fields, not just highly technical ones. Knowledge about the job turns out to be a far better predictor of performance than how high our IQ is or any other general disposition, not just in certain kinds of jobs but across a wide range from complex technical work to manual labor.[1] Just as I’d rather have a computer expert help me rather than my friend with an astronomical IQ, in most cases I’d prefer someone who has job experience rather than someone very smart but inexperienced. And I can point to research evidence that supports my preference.

Seeing Differently vs. Seeing More

Even in many areas where we would tend to expect pure reasoning ability to play a large role, it turns out that on average experience tends to win out consistently over any more general ability or measurement we have come up with.

The research that inspired the modern study of expertise began with the game of chess. Think about chess for just a moment. Chess is an activity with a small number of relatively simple rules. Yes chess has the reputation for being a difficult game. But that’s not because chess is hard to play. Nearly anyone can learn the game. It’s because we soon discover that differences in individual ability are immense.

The difference between someone who plays chess for fun who doesn’t study the game seriously, and an average tournament player, is like night and day. It doesn’t seem like much of a competition most of the time. The difference between an average tournament player and a strong one is just as large, which is why there is a rating system.

Ratings allow people of similar ability to play relatively evenly, or to estimate handicaps as they do in golf. The difference between a strong player and a master is similarly imposing as is that between the master and a grandmaster, and between the average grandmaster and a world champion.

How can a game with a handful of simple rules end up with people playing at such astronomical differences in ability? This was the question that intrigued early researchers trying to figure out how people solve problems. The obvious answer is that the stronger players must be seeing more on the board. But what are they seeing differently?

When most of us look at the chess board we see a collection of pieces in different places that are allowed to move in particular ways. We know what we have to do to win; we have to trap the king. We also know some ways to accomplish that. For example we can capture the opponent’s pieces so we have a bigger army, and we can harass the opponent’s pieces so that they are forced into a less defensible position, allowing us to attack the king. Everyone who plays the game, even for fun, knows these things. Still most of us pretty much have to guess at how to get from some arbitrary position to that result.

If I move here, I’ll attack this piece, but how do I know that my opponent doesn’t have some better move in response that is even stronger? More insidiously, is that move by my opponent actually setting up a surprise for me later? If so, what are my options? These kinds of considerations quickly lead to the very intuitive notion that being better at chess is really about calculation, about being able to imagine a lot of different moves, and what might happen if we made them, and keeping track of all that imagining. The better player must be seeing more moves on the board, figuring out what the options are more accurately, and then predicting the outcome.

This is indeed how early chess software played the game well. It looked at the possible moves, looked at the possible responses to each move, evaluated the resulting positions, and chose the move that seemed to give the best outcome based on what the opponent was able to do. The trouble was that trying to do this more than a couple of moves ahead turned out to be a very demanding calculation. More demanding than even the most powerful computers could handle. Researchers were curious as to whether seeing more moves in their mind is really what good players were doing.

Maybe the human brain is really that much more powerful at calculation than we thought. Or maybe the brain is doing something else entirely?

In a pioneering study of chess players in the 1940’s[2] a Dutch psychologist found the surprising answer. I say his work was pioneering not just because it was early but because it led to entire fields of research based upon it and validating his basic findings. The most compelling and surprising findings:

...Weaker players examined the same number of moves as stronger players, and equally thoroughly (!)

...Stronger players could recognize an actual game position far better than weaker players.

...Stronger players were just as bad as weaker players at recognizing an arbitrary configuration of pieces.

This may not seem so earthshattering at first, but think about the implications. Experts at chess consistently beat weaker players, but without examining more moves and without examining the outcomes of those moves more thoroughly. They aren’t “looking ahead more” and they aren’t “reasoning better” and they aren’t even remembering more in general. They do remember more about chess in a sense but not because they have a better memory. And looking ahead is important, but not by keeping track of moves. Their ability is a result of their mind being better trained to remember chess configurations in particular and to use that knowledge quickly and efficiently to evaluate moves.

So what are chess experts seeing that the rest of us aren’t? They aren’t seeing more moves ahead, they are seeing the board in terms of chess configurations instead of seeing it in terms of individual pieces. Their mind has been trained to see meaningful configurations of pieces instead of individual moves. They are not seeing more per se, they are seeing differently. They are seeing in terms of larger and more meaningful groupings. Experts with extended experience acquire a larger number of more complex patterns and use these new patterns to store knowledge about which actions should be taken in similar situations.[3]

The result is profound. We have a game where a few simple rules results in an incalculably large number of possible sequences of moves. But we become good at this game of many, many moves not by thinking about more moves but by thinking in terms of larger patterns: patterns of pieces rather than movements by individual pieces.

Through practice, chess masters have trained their mind to recognize the unique meaningful patterns that apply to their game. Further, the ability to learn to recognize new patterns (along with a huge capacity to remember them) seems to be something we all possess, not just chess masters. It is a fundamental principle of learning, at least learning to be a chess expert.

Even more interesting, we don’t recognize this as knowledge, in the sense of things we recognize that we know. I know that I know some things. I know that I know all sorts of facts like the capital of some of the U.S. states and the number of sides in a triangle and Newton’s formula relating force and mass and acceleration. These sorts of things are considered explicit knowledge.[4]

Chess masters can’t write down most of the patterns they know, both because those patterns are so vast and because they use them without thinking about them. The patterns they learn become part of their chess intuition in a manner of speaking. A common technical term for this is tacit knowledge.[5] We use tacit knowledge in our thinking without realizing that we are using it. This is why it took focused research to discover what was going on in the minds of chess masters.

Tacit knowledge becomes part of our perception. Chess masters see the board differently; for example they often immediately see positions as good or bad without having to do the kind of analysis that the rest of us would have to rely upon.[6]

Tacit knowledge is also used automatically in our thinking. When chess masters guess at the best move in a given position, their guess is informed by their vast database of tacit knowledge, so it is very different from the guess made by a weaker player. Experts make better guesses in their area of expertise. This is what I mean by their “chess intuition” above.

Trained Intuition and Better Guesses

You might be wondering at this point why I’ve spent so much time talking about chess experts. Or you may have guessed the answer. The most interesting conclusions from the research on chess masters are by no means limited to chess masters. Very similar or consistent results have been obtained across a staggeringly wide variety of fields from physical pursuits like wrestling and ballet to intellectual subjects like calculus and philosophy to artistic activities like painting and violin playing, to a wide variety of everyday jobs, to oddball activities like picking the winners at the horse races.[7] Even among scientists, where the role of abstract reasoning is particularly central and the subject matter particularly challenging, productivity doesn’t seem to be predicted on the whole by supposed general ability measures such as IQ.[8]

The chess findings are a particularly useful rhetorical device here because chess seems like it should be so dependent on reasoning and analysis. It turns out that experts analyze chess positions with the help of a vast mental database of chess configurations that apply without any recognition that they know them. The resulting perception and memory of the board just seems natural to them as a result of practice. Examined closely, in spite of its natural appearance for some people, the effortlessness of deep expertise seems to be an extreme kind of skill acquisition[9] far more than an expression of talent.

Even if you interpret all of these findings from different fields very conservatively, collectively they still tell us something of tremendous importance about how we become good at things. We modify the way we perceive the activity. In effect, we train our intuition about the activity.

In all of these activities, researchers have found that time spent in the activity lets us acquire a new way of perceiving patterns in that activity that let us transcend the limits of our working memory and sequential reasoning capacity. That’s why expertise consistently outperforms IQ or working memory capacity or other general measures as a predictor of performance in virtually every activity that has been studied so far. And expertise is not just specialized knowledge or skills; it is also more importantly an accumulation of organized tacit knowledge that lets us make better guesses.



[1] (Hunter, 1986)

[2] (de Groot, 1965)

[3] This has been the most common interpretation of the chess research findings amongst expertise researchers, based on the influential theory of Chase and Simon. (Chase & Simon, 1973), (Simon & Chase, 1973)

[4] “Explicit knowledge,” basically just means things we know that can be easily identified and written down. The descriptor declarative is sometimes used as well, meaning that we can declare it.

[5] In contrast to “explicit knowledge,” this is often referred to as “tacit knowledge,” meaning things we know but we can’t easily express, especially things that support action. Tacit knowledge is usually assumed to be useful for doing things more than for taking part in our conscious reasoning processes. The descriptor procedural is sometimes also used for tacit knowledge because we think of it as involving procedures for doing things rather than declarations about things. For this reason, a common rule of thumb is that tacit knowledge refers to “know how” whereas explicit knowledge refers to “know that” (i.e. I know that grass is green). The casual rule of thumb is troublesome because we don’t really know how we do those things we call procedural, the usage of the word “know” in “know how” is very different than the word “know” in “know that.”

[6] Following the pioneering chess research, research into other areas reinforced the same finding: expert performance depends heavily on a large accumulated memory of patterns that give us a different “intuitive perceptual orientation” to tasks. “Experts can ‘see’ what challenges and opportunities a particular situation without affords.” (and without doing any analysis) (Perkins, 1995, p. 82)

[7] One of the leading and best known figures in the study of expertise is K. Anders Ericsson, whose research encompasses a particularly wide range of fields. An excellent and accessible overview of work in diverse areas of expertise research is Ericsson’s edited collection: The Road to Excellence (Ericsson, 1996).

[8] (Taylor, 1975)

[9] (Proctor & Dutta, 1995), (VanLehn, 1996)

Getting the Right Answer: Is it the Right Answer?

What’s the most important thing about problem solving? If you paid attention in school, you probably would respond: getting the right answer!

There’s nothing wrong with wanting to get the right answer. Or is there? I want to raise four related concerns:

1. The problem structuring concern: Problems don’t always arise in a form that has an identifiable single right answer. Often there are different best answers for different sets of possible criteria, with different sets of tradeoffs.



2. The motivated thinking concern: The kind of thinking we do in order to feel we are right, to be seen by others as being right, or to advocate the right answer to others can overwhelm the kind of thinking needed to solve the problem in the best way.



3. The my-side bias concern: We have a natural tendency to look selectively for evidence in favor of the first good guess we make explicit, to ignore evidence for alternatives, and to think in ways that support our favored alternative.



4. The belief overkill concern: The my-side bias is often reinforced in such a way that that certain of our intuitions become treated as aspirations or universal facts of nature and this extends beyond things that can be verified empirically between observers. Compelling intuitions can guide our thinking into limited preferred patterns, reinforced by selective use of evidence and also by social patterns of polarized thinking.

I argue that these concerns, along with various inferences we can reasonably make about how the mind works, necessitates a certain approach to thinking, especially about more difficult problems.

These factors mean that we have to learn to adopt and leverage different perspectives in order to harvest all of the information available and the expertise needed to solve complex problems. This is why when it comes to problem solving worthy of the name, thinking clearly is more important than thinking correctly along predetermined lines.

At this point you probably have your own concerns. You might be wondering whether I am advocating some sort of fluffy relativistic “there’s no right or wrong and all perspectives are valid” sort of approach to thinking.

That’s not the case. I use the term Clear Thinking because I truly believe there is such a thing as identifiably better and worse thinking, leading to better or worse conclusions and that it very often makes a critical difference whether we get it right.

My point is just that all of us (not just other people) assume we are getting it right much more often than we really are getting it right, and that very knowledge about our own thinking processes is a key to Clear Thinking.

This means that when we need to think clearly about complex problems, we need to use our knowledge about and skill at problem solving itself to root out our own shortcuts, make our thinking more explicit, bring alternate perspectives into play, and in general consider more alternatives than would otherwise come to mind.

The Dilemma and Challenges of Exceptional Thinking Abilities

What Makes Some People Better Problem Solvers Than Others?

The Dilemma and Challenges of Exceptional Thinking Abilities

Simply getting the right answer isn’t always the best way to think about solving difficult problems. For some problems outside of the classroom and aside from questions of knowledge from within well established domains, there may not be a single right answer.

There may be additional alternatives to be considered that aren’t known yet or which don’t seem right at first but can be turned into better solutions. Needing to be right (getting the answer that others seem to think is right), or needing to think we’re right, or needing to be seen by others as being right, may mislead our thinking and blind us to better answers.

Thinking Too Much: Defying Common Sense


Wanting to be seen as clever or wanting to be seen as an expert often similarly restricts our thinking. We very often settle for the first guess that seems right or the way other people seem to be thinking. There are sometimes good reasons to stop thinking about a problem and settle for an answer, but most of the time we use shortcuts rather than stopping when we truly have the best answer available to us.

Shortcuts are natural to us and they are an important part of what makes us good thinkers. Shortcuts in thinking are part of our common sense. It doesn’t seem right to sit and reflect on something that has an obvious answer. It can seem like a peculiarity or a symptom of subscribing to some bizarre overly complex view of reality, or maybe even a character flaw.

The trouble is that our common sense that serves us so legitimately well in so many everyday situations turns out ot be poorly suited to many other kinds of complex and counter-intuitive situations. Our natural instincts for reasoning are significantly better adapted to some kinds of problems than others.

The shortcuts that serve us for biological needs like feeding ourselves and mating and getting along with other people in small groups tend to fail us when we think about things like cultures, corporations, markets, and nations or when we’re presented with a completely different kind of problem.

Importantly, the way we learn is not well suited to automatically recognizing which kinds of situations we are thinking poorly in. The shortcuts in our thinking work so well because we rely on them so naturally. We don’t necessarily get an alarm bell in our mind that we are thinking in the wrong way about a problem. We instead get responses from our natural learning systems that we experience as compelling feelings and intuitions that guide our thinking.

It is only by learning about the thinking process itself and how our own mind works that we begin to learn how to make best use of our natural learning systems to think clearly about problems that our natural abilities are not well optimized to solve, situations where our common sense and our intutions fail us. This learning also helps us reason through situations where we have to think across different domains of expertise without a sense of how well we have captured the meaningful patterns in each of those domains.

Sure, when there’s a right answer, we want to be able to figure out what it is. More generally though we want to think clearly about the problem. This means thinking in a way that leads to, if not an ultimate perfect answer, the best solution available, even if that means bringing more expertise and different perspectives into play and challenging our own intuitions.

How do we know when our shortcuts and intuitions are failing us and that the situation requires a different kind of thinking? I think it comes down to making it a priority to learn about our own thinking while we are learning other things. This means being strategic about thinking: knowing as much as possible about our own tools and resources, both their strengths and their weaknesses.

Identifying Our Strengths and Identifying Our Weaknesses


I’ve been a professional problem solver for decades and from time to time I have worked alongside other problem solvers whose abilities truly amazed me. Some people are able to look at a situation and see opportunities and possibilities that others cannot seem to appreciate until after they become real solutions, and sometimes not even then.


Some of those same remarkable problem solvers then even more remarkably sometimes make the worst mistakes in certain situations. They apply their knowledge and skills in ways that just don’t fit the situation, and sometimes the very qualities that often serve them so well in other situations now make them overconfident in their answers. This book is about learning from both their triumphs and their failures, as well as our own triumphs and failures. It’s about learning to think better.

I have devoted many years trying to understand what it is that is different about exceptional problem solvers, and to what degree their abilities can be duplicated and perhaps even improved upon to avoid the worst mistakes that they also tend to make.

This sets out two primary challenges for me:

--> What makes some people so much better problem solvers than others, especially across different kinds of problems?

--> What causes otherwise great problem solvers to make such awful mistakes so often?

Clear Thinking: An Introduction

When a difficult problem is solved deliberately, it is generally because the right expertise was applied to good information through the right tools as part of a reliable process. These are the essential elements of effective human problem solving. The rest is in the details. We often do less than this because we are also very good at guessing well.

The human nervous system is not a logic engine, it evolved to serve human biology. This has profound implications for the way we think and what we must do to improve our thinking. Our explanations are guided by powerful intuitions that often seem to defy the theoretical ideal of rationality.

Expertise refers to the way a mind with natural learning abilities organizes its experience purposefully for action. This is where our guessing ability comes from. Expertise provides our built-in guidance for effective thinking in particular areas.

Information is the fuel for thinking, without which expertise would be an engine with a dry tank.

Tools and processes are the way we leverage our strengths and compensate for our weaknesses.

Exceptional problem solvers make better use of available resources than the rest of us and also gather more of the right resources around themselves. This isn’t magic and it isn’t something we’re born with. Nearly anyone can learn to do these things better. Nearly anyone can learn to make better guesses and also to leverage good guesses into more powerful reasoning.

This book introduces an approach which I call Clear Thinking. The basis of this approach is strategic. Through a realistic and accurate ongoing understanding of the strengths and weaknesses of our own mind, we learn to make best use of our ever changing strengths and minimize or compensate for our ever changing weaknesses. In this way we make increasingly better use of our resources and approach the ideal of clear thinking.

You should understand from the start that this is a lifetime learning process. You can’t learn to radically improve your thinking in a weekend seminar, a critical thinking course you can complete in a semester, or even a degree you can earn in a few years. To become smarter you have to learn the mindset, strategies, processes, skills, tactics, and habits of becoming smarter, and this learning is difficult, rewarding, and lifelong.

Clear Thinking is an approach that you incorporate into your daily decision making and problem solving by learning the associated tools and principles and by coming to embody the intellectual virtues shared by the best problem solvers.

Key Points:

--> Useful human knowledge, skills, and attitudes tend to break down into domains. Among other reasons, this is possibly because the human brain is organized into somewhat discrete learning systems for dealing with different kinds of biological needs.

--> Different subjects we learn have their own domain with their own domain-specific rules and methods of study. Our practical abilities tend to be organized into domains for the most part. The domain-specific elements of thinking are critical to Clear Thinking and also to education in general.

--> Most problem solving is relatively routine and involves dealing with particulars of a situation relevant to a specific domain of activity rather than dealing with abstract principles.

--> Since problem solving so often involves dealing with particulars, individual differences in problem solving ability are largely a result of specialized expertise in particular domains rather than a more generalized reasoning ability.

--> Speciallized domain expertise is the result of systematically acquired experience in which we structure our mind in a way that lets us think efficiently about a specific kind of activity in a particular way.

--> We also have important abilities that apply to multiple domains of knowledge at once or which cross domains. These domain-general elements are the ones emphasized when we try to improve problem solving and decision making through “critical thinking” and similar approaches. I have adopted the term Clear Thinking rather than “critical thinking” only because I think the emphasis on criticism can be misleading.

--> Some people are better individual problem solvers than others because they have learned to make use of their cognitive talents, domain-specific expertise, and domain-specific knowledge, by means of domain-general problem solving knowledge, skills, strategies, and attitudes.

--> One of the most critical things we can do in order to improve our thinking is to distinguish domain-specific from domain-general elements. We acquire and apply these different kinds of elements in very different ways and they have different kinds of influence on our thinking.

--> A great significance of domain-general vs. domain-specific elements is partly that more intelligent and more expert problem solvers often make even worse mistakes than less intelligent and less expert problem solvers due to negative artifacts of their abilities such as overconfidence, overspecialization, and the amplification of natural biases.

--> One way we can avoid the worst mistakes is by learning realistically about the strengths and weaknesses of human abilities in general. This becomes an important aspect of our domain-general problem solving knowledge, skills, strategies, and attitudes.

--> The domain-general emphasis of Clear Thinking is mostly intended to make the thinking process more explicit in order to make better use of our guesses. Making the thought process more explicit is the essence of the ideal of rationality.

--> The domain-general elements are also significant because they help us learn how to shift between different perspectives. Importantly, this is not because different perspectives are somehow all equally valid. It is because a perspective is much like a lens which makes some things easier to see than others. Useful bits of knowledge are sometimes obscured by our current perspective, and shifting perspectives can help additional alternatives become more visible.

--> Some groups are better collective problem solvers than others due to differences in the patterns of their interactions in making use of their individual expertise, knowledge, skills, strategies, and attitudes. This becomes another important domain-general element of human thinking.

Does adding men to a group make the group dumber?

Does adding men to a group make the group dumber?

New preliminary finding reported at the Harvard Business Review:

 Adding more women to a group may make the group smarter.

Previous related finding:

 Collective intelligence of groups is roughly independent of intelligence of individual members

Previous assumption:

 A more diverse group is better than a less diverse group.

Surprising new possible implication:

 Gender could potentially be more important than diversity in collective intelligence of a group, with women adding to group intelligence and men detracting from it.

Limitations:

 This is a preliminary finding not yet a robust one. It has been reported in two studies by the same team under a limited range of conditions.

 Collective intelligence is not the only thing of importance in group problem solving.

 The effect of gender may be through process factors that potentially could be achieved in other ways as well if isolated.

 The effect has not been tested very far yet at the extremes.

 Measured of collective intelligence are not as standardized as measures of individual intelligence, which are themselves of mixed value in actual problem solving.

Additional thoughts:

This is a preliminary finding but it seems plausible to me and if it is borne out robustly then combined with the previous finding that collective intelligence is roughly independent of individual intelligence of group members, this seems to mean that (1) group process is more important to collective intelligence than individual insights, and (2) that group process depends strongly on gender.

The first still amazes me but I think it may be true, and if so, the second one seems even more plausible. My impression is that there is a lot less constructive interaction between men than between women in group processes in general.

My own speculation is that while both experience a mixture of task and relationship tension in groups, women leverage the relationship tension more constructively, whereas men tend to align more quickly when they agree and to stonewall more strongly when they disagree. Men usually seem to be more likely to drift toward a goal of satisficing (coming to the first satisfactory solution) and then disengaging, whereas women seem to interact in a more prolonged way. I'm guessing that this contributes to collective intelligence as it is being measured here in some way.

refs:

Article on MindHacks: http://mindhacks.com/2011/06/24/a-dose-of-female-intelligence/

Interview at HBR: http://hbr.org/2011/06/defend-your-research-what-makes-a-team-smarter-more-women/ar/1

Chart (The Female Factor): http://hbr.org/2011/06/defend-your-research-what-makes-a-team-smarter-more-women/sb1

Book Review: The Alternate Day Diet

Review of ...

The Alternate-Day Diet, by James B, Johnson
Putnam Adult, 2008.

The Alternate Day Diet explains some of the relevant science behind calorie restriction as both a health-conducive regimen and a weight control strategy and offers valuable experience-based practical advice. There is a lot of evidence in favor of strategic intermittent calorie restriction for making us healthier but there are also some pervasive arguments that have been made against it. Johnson directly addresses each of the arguments and concludes, partly from research he cites and partly from his own experience with patients, that alternate day calorie restriction is practical, effective, and healthy for a wide range of people.

Intermittent calorie restriction is a welcome and refreshing strategy in the diet world because it has the distinct potential to help explain some of the benefits of the other approaches and resolve some of their competing claims. Johnson's conclusion, and the evidence he cites in favor of intermittent fasting, is consistent with the evidence of health benefits for other approaches such as "low carb," and "paleo," because of the significant overlap between those strategies and general calorie restriction. For example, calorie restriction, low carb, and paleo approaches all involve minimizing high glycemic intake, although they use different rationales to explain the outcomes.

This book is also welcome because it offers a perspective that seems to me to help dissapate some of the supposed tension over whether "calories in vs. calories out" is more important than "metabolism" in weight control. If strategic fasting is healthy and effective in weight control, then overall calories may very well matter, but so does metabolism. Fasting appears to hit both targets at once, through the effect of calorie restriction on genetic mechanisms of metabolic regulation.

The key idea behind intermittent fasting as presented by Johnson is that even severe calorie restriction for only 24 hours at a time is not only tolerable, but sustainable over time, and in fact does not cause us to hoard body fat by shutting down our metabolism. The alternate day fast, according to Johnson, seems appropriate to nearly anyone without such serious contraindications as insulin dependent diabetes. He also finds no evidence that intermittent fasting further encourages restriction in people predisposed to anorexia (although I would add that it may help them rationalize their pathological restriction).

I'll add that I've been using Johnson's alternate day fasting approach for several weeks (and prior to that had used the recommendations in Brad Pilon's "Eat Stop Eat" intermittent fasting program for several months) and have so far found everything he says to be true from my own experience. It is not at all difficult to get used to eating very little on alternate days, especially if you combine this approach with some knowledge of satiety (see Barbara Rolls books such as Volumetrics), it does not seem to encourage me to eat more on the non-fasting days, it does seem to have a steady body fat reduction effect, and it seems to have positive effects on how I feel both physically and emotionally. So far I have not found alternate day fasting to have any negative impact on my activities, and I think it has had a small positive effect on my energy levels.

I can't say that the approach would work for an elite athlete or bodybuilder who lives on a much larger scale of intake and output, but it seems like this approach has a lot to recommend it for most of us. Being successful with alternate day fasting will take some new habits, and probably a shift in attitude toward meals, but I think it is a viable weapon against obesity for those who can do it (and it seems to me that those who can't succeed with this approach will probably will have problems eventually with any approach) and a valuable tool for better health.

I bought this book on Kindle, and it is well served by that format. I highly recommend it for its reassuring scientific evidence and its practical advice. I would enhance this approach by learning as much as you can about satiety (such as from Barbara Rolls books on Volumetrics) so that you have even more strategies for feeling comfortable with calorie restriction while still getting healthy nutrition from the reduced intake. The alternate day fasting approach does not tell you what to do on your non-fasting days (other than to avoid either fasting or gorging), so it's still up to you to learn how to eat healthy on those days. The author doesn't abandon you on the "up" days, he does offer some standard suggestions for healthy eating, but these are a small part of the book.

Book Review: "The Hypnotist" by Jake Shannon

"The Hypnotist: Healer, Head-Hacker, & Headliner"

by Jake Shannon

https://www.createspace.com/3623853

Jake Shannon is a fascinating multi-talented man with a rich and partly enviable life experience. He has also lived through extraordinary pain and illness which were a big part of his initial inspiration for his deep study of hypnosis. As in the life story Dan Ariely, whose life with severe burns helped shape his fascination with psychology, Jake turned to the study of the mind for healing and pain relief. Hypnosis served Jake, as it has served many, as a way to alter the way they experience pain and discomfort.

If "hypnosis" simply referred to a straightforward technique for altering our perception, and that were the end of the story, then Jake's book would have been much more narrow in scope. The intentional structure of the human mind as we understand it pretty much ensures that any any discussion of perception also overlaps with a discussion of beliefs, behavior, communication, and social interaction. This network of concepts has far reaching implications in areas that fascinate all reflective people.

Topics as deep and diverse as the nature of "free will" and responsibility, the sources of extraordinary (and ordinary) human abilities, the role of social compliance in human life, and the malleability of perception all have some meaningful dependence on the ideas that underlie contemporary theories of hypnosis in terms of things like suggestion and expectancy. That interaction, and those dependences, are where the story gets really interesting, and Jake stands out to me in the broad way he has captured them.

Jake surveys the key concepts from a wide range of hypnosis theories, but he does not stop there. He also explores their use in daily life and their implications for social and political issues. When we extend our scientific theories to less directly empirical questions like the implications for social and political issues, of course we tend to incorporate our own distinctive leanings along the way.

Jake's distinctive leanings are strongly libertarian, and libertarian philosophy does not map neatly onto the usual political spectrum but has its own distinct principles. In his book "Justice," Michael Sandel argues that libertarian philosophy revolves crucially on the idea of self-ownership, and I think this is borne out in Jake's perspective on hypnosis. Jake is starting with the central notion that we own the stories we tell ourselves about the world and our role in it, and that we can and should take responsibility for guiding our own self-talk and for filtering the inevitable influences that impinge on it.

For Jake, the crux of hypnosis is entering into and redirecting the conversation we all have with ourselves in our own thoughts, thus shaping the inner stories we tell ourselves. This emphasis on the language aspects of hypnosis are particularly complementary with the communications theories of hypnosis, such as the ideas derived from the studies of Milton Erickson's hypnotherapeutic methods. Jake wants to tie the concept of hypnosis together with a host of other phenomena of importance to self-ownership, particularly social compliance phenomena, so the emphasis on communications and language seems to be strategic.

Hypnotic phenomena are all about changes in our perception as a result of "suggestion," and it is notoriously difficult to distinguish changes in perception from their related mutual influences on belief, behavior, attitude, and emotional associations. So Jake is working from very fertile ground in adopting the broad view of hypnosis as a class of psychological phenomena with interesting social implications. The question that arises about the role of "hypnosis" (in the broad sense Jake uses the term) in human life is whether the sorts of focused suggestions we associate with hypnotism really have a lasting and systematic effect. This is where the topic has historically gone astry in the public mind, with many people attributing much greater and more mysterious power to hypnosis than it deserves, and others attempting to debunk it out of existence as a distinctive class of phenomena of any importance.

Jake would seemingly focus on hypnotic suggestion as a situation where the someone's existing inner conversation is entered into and guided or redirected by the voice of the hypnotist. In the communications view, the explanation for what is going on in the situation (our self-talk about the situation) seems to be a driver for what will follow. We explain the situation to ourself in a particular way, and as a result we act in the situation as if that explanation were true. I think this primacy of the role of explanation in behavior in Jake's thesis is tied tightly to his committment to libertarian self-ownership. It provides a focus point where we can more clearly see how we can influence our own behavior, and how we influence each other. In psychological terms, it centers around *compliance* rather than the _experience_ we have from hypnosis.

Jake's summary illustrates this focus on compliance very clearly: "The hypnotist accesses the subject’s self-conversation through specific rhetorical techniques and subject similar-body language while leveraging the effects of expectation and authority in the subject’s mind in order to achieve compliance."

Explanation is just one albeit critical aspect of human mind, but it clearly isn't the primary driver of behavior in a wide range of situations. Jake refers to the work of John Bargh and others who have demonstrated this experimentally. Jake seems to give the non-language aspects of hypnotic phenomena a secondary role as influencers on our inner dialog. Importantly, he sees hypnosis as "leveraging the effects ..." of expectancy rather than expectancy being the central distinction of effective suggestion.

A less communications-oriented view might see the same situation as if our expectations were altered first, through various language and non-language means, and then our experience and behavior followed our expectations, followed at last by some explanation. In the expectancy view promoted by Irving Kirsch (for example), the explanation for why we responded to a suggestion is an artifact, not a driver of the behavior. We follow a suggestion, and then explain why we followed it. Experiments have sometimes deliberately imposed a conflict between an explicit suggestion and an implicit expectancy, and Kirsch interprets those as demonstrating that the expectancy (rather than the language per se) is what drives the resulting behavior. Jake sees automaticity as "the ability to do habitual tasks with little active thought." Kirsch, in his writings on automaticity, has gone much farther and described it as the default state for daily life, where active thought arises only under limited conditions. The non-communications views tend to focus more on the _experience_ of hypnosis and especially why **our experience of control** differs from situation to situation.

This is to some extent a chicken and egg problem, since we can find circumstances where behavior is more clearly driven by the (potentially) self-owned stories we tell ourselves, and also circumstances where our explanations for our own behavior are completely at odds with a more compelling cause. The stronger-self-ownership view of the mind will of course tend to emphasize the role that we play in guiding it. The stronger-automaticity view emphasizes the small, critical windows where active guidance actually takes place. These are not mutually exclusive ideas, something that I think Jake does manage to get across in his book. Again, though, there is that important difference in emphasis, and the different role of language in the human mind that this difference in emphasis represents.

It seems plausible that we can reconcile these different views by seeing the mind as having multiple different parallel systems, perhaps one speciallized for explanations, and others speciallized for motivating and guiding behavior as such. This is part of the role that Jake seems to attribute to bicameral theories of mind. As good promoters of natural science, we want to somehow try to map the features of the mind onto the brain, and bicameral theories take a big swing at that. I think the process of mapping mind onto brain is, while laudable, problematic in practice for various reasons perhaps better identified by philosophers than scientists. Also I don't personally step onto the train of thought that views the anatomical division of many of the brain's "higher" structures as also being a fundamental infrastructure of psychology, nor do I think it plays a particularly fundamental role in explanations of hypnosis, but I do think it is likely that it plays at least some secondary role. My difference with Jake here seems to be mostly one of emphasis.

I don't criticize Jake for taking on the language-focused view of hypnosis here, and I understand how compellingly it fits into the bigger picture of ownership for our own thinking and behavior. In various notable attempts to naturallize consciousness, various reflective thinkers such as Daniel C. Dennett have also described the meaning-making function of the mind largely in terms of stories we tell ourselves, and this does seem to imply a huge role for language as well.

The remaining question is the relationship between language and experience, and in the case of Jake's thesis in this book, especially the experience of *control* and its relationship to compliance. Understanding the phenomena of hypnosis (broadly defined), Jake empahsizes, can help us gain better control of our own mind, and better understand the influences that others are having. I would say that compliance and self-ownership of our mind is a larger topic than is hypnosis, but that Jake is right on target in pointing out the crucial importance of understanding how our mind is shaped and influenced, and how much we can learn about this from the phenomena of hypnosis.

I would actually go farther and say that I think the phenomena of hypnosis have potentially much more to tell us about the nature of the mind than just about the issues pertaining to compliance and influence. I wouldn't entirely define hypnosis in terms of compliance or language, I would define it more primarily in terms of expectancies, and in revisioning the workings of the mind in terms of expectancies I would expand Jake's thesis to include even broader implications of our shifting sense of control for law, medicine, philosophy, economics, and so on. Still, this is a great start and a very worthwhile research effort with both a depth and breadth of ideas you won't easily find elsewhere and which most people will find surprising, fascinating, often troubling, and most importantly, useful in understanding ourselves. I urge you to learn from Jake's experiences and reflections.

Book Review - Apollo Root Cause Analysis: A New Way of Thinking (?)

Book Review - Apollo Root Cause Analyis: A New Way of Thinking Book by Dean Gano, Apollonian Publications, 2008. Review by Todd I. Stark Link to Amazon review.

Very useful principles for preventing bad things from happening, April 11, 2011 By
Todd I. Stark "Cellular Wetware plus Books"

This review is from: Apollo Root Cause Analysis: A New Way of Thinking (Paperback)

How can we prevent bad things from happening, and how can a formal method help us in this quest? That's the topic of this book.

"By understanding the cause and effect principle and creating a RealityChart, your understanding of what constitutes reality will be changed forever ... allow you to see a reality that was previously beyond your comprehension." (Apollo Root Cause Analysis, p. 3)

"... the problem of a linear language in a nonlinear world while accommodating the simple human mind has been a challenge for the ages. I believe this challenge has been met with Apollo Root Cause Analysis." (ARCA, p. 176)

The importance of the topic covered by this book is immense, so for my purposes, I'll forgive the author his amusing enthusiasm for this own method as seen in the quotes above and try to determine what may actually be truly useful in it rather than dwell on what may or may not be unique about it. I will be referring to this book and to the Apollo method in this review. The method is also implemented with associated RealityCharting(tm) software which is outside the scope of this review, except where explicitly mentioned.

Problem solving well is one of if not the most critical factor for human success across a wide range of activities. One big part of problem solving is explaining *why* something unexpected happened. I you think about it, anytime something unexpected happens, we generally want to know why. We do this naturally, automatically, and effortlessly. We can't stop ourselves from doing it to at least some extent. Sometimes the explanation seems obvious and sometimes it seems very elusive. Even when the explanation seems obvious, there is often more going on of importance than we realized. We do this seemingly because especially if it's a bad thing that happened, understanding why it happened potentially helps us predict whether it will happen again and perhaps prevent it from happening again. That's important to us as individuals as well as to organizations. It's so important that we already do it all the time. We also tend to assume that we're very good at it, I think. My experience is in agreement with the author of this book, people are in general not nearly as good at solving problems as we think we are, at least when the problem become complex and involve multiple people and organizations.

We have a powerful natural ability to make sense of events that happen around us by identifying people, places, things, and weaving a story around how they interact with each other. (1) The power of storytelling to make sense of events is also its tragic downfall when we need to understand how things happen in great detail. Our temptation to tell stories actually gets in the way of understanding what specific things need to happen in order for other things to happen, and that's the sort of understanding that is needed in root cause analysis. Getting past our natural but distracting abilities to use the right tools effectively is the greatest value of an effective formal method. We often look to formal methods to systematize our thinking in general, but problem solving outside the academic realm of worked problems is often not amendable to being constrained by formal methods. Instead, formal methods provide their real benefit in forcing us to look past our own blind spots at key points during problem solving. To his credit, the author of this book seems to have grasped this important point very well and applied it effectively.

The most problematic blind spots in our thinking are those that aren't avoided by intelligence, education, or domain-specific expertise, skills or knowledge. The most problematic blind spots are actually natural abilities that serve us well most of the time. One of the most important examples is that we tend to use storytelling to construct a plausible sequence of events leading to an outcome, putting people into the center of the action and making the events meaningful to us. There are a number of reasons why this natural and compelling process is a bad idea in problem solving.

First, since a story starts with the putative "root cause" and then proceeds to its effects, it tends to assume a single cause. Events don't have a single cause. If our goal is to prevent bad things from happening, we'd rather identify as many relevant causes as possible, and potentially address each one as makes sense.

Second, storytelling tends to center around characters, their motives, and the things they do. The motives and behaviors of people are often the things we have the least reliable control over in problem solving. We'd prefer to find things we can control better if at all possible, and save human choices and our interpretation of human motives as a last resort.

Third, we can usually tell multiple different stories about the same events, depending on what information we emphasize and how we emphasize it, which in turn depends on our motivations and perspective. This means that the choice of which "root cause" we focus on tends to be more political and subjective than a result of careful analysis.

Our storytelling is natural and compelling but it tends to be more to make sense of events than to understand the details of what causes what.

So effective Root Cause Analysis tools and methods should discourage storytelling and instead search the prerequisite conditions and sequences of events that lead to the results we want to prevent. That's the message of this book and its method. So how well does it accomplish this?

Why a formal process?

Causal analysis (or what is sometimes more specifically referred to as "root cause analysis") is the more formal process of doing what we tend to do naturally, try to figure out why events diverged from what we expected. Why do we need a formal process for doing this, if we already do it naturally? Because often we don't do it very well. The very fact that we do it naturally means that we will tend to make *confident guesses* about why things happened. Often more confident than accurate. This far, the author is on solid ground. The author doesn't refer to it explicitly, but there is a very large literature on the cognitive science of decision making that supports the author's claim that people tend to jump confidently to unwarranted conclusions when they first try to explain causation. (2, 3, 6)

One very important reason for a formal process is to systematize the data gathering and analysis process in order to help compensate for our natural biases that lead us to make confident but inaccurate guesses. The value of formal vs. informal processes is debated widely in the decision science literature, since much of our innate intelligence is the result of automatic non-conscious processes that are opaque to us in our own thinking. (5) However in general I think it is a pretty safe conclusion that a good formal process often helps us focus attention on things that we would not otherwise have seen to see past some of our own blind spots. That's the first reasonable rationale for causal analysis methods like Apollo Root Cause Analysis (ARCA) and its primary tool, RealityCharting(tm). Simply having a process to focus us on causes rather than stories is useful in itself.

Why a formal group process?

There are various reasons for using a formal process in organizations. Unexpected events often affect more than one person, often more than one person is part of the problem, and often more than one person has information or perspective needed to figure out what happened. So causal analysis very often becomes a group process. In addition to the reasons for using a formal process in general (to get around our individual blind spots), it is also important in groups in order to help avoid "group blind spots" such as the principles of behavior in groups studied by psychologists. We are often tempted us to go along with the consensus, to protect our own ideas, to react initially negatively to new ideas, and other tendencies that can negatively impact the problem solving process in groups. (6) A good formal group process can help get around our group blind spots just as it can help us get around our individual blind spots. In spite of the well-established problems created by "groupthink" and other group dynamics studied in social psychological, under optimal conditions, groups can often perform significantly better than individuals on some kinds of problems. (7)

The author makes a key point about group processes that he says defies conventional wisdom (p. 9). He implies essentially that the principles of good problem solving are simple and domain-general so should be taught to everyone, whereas domain expertise is deeper and necessarily differs more between people, so "subject matter experts" should also be at hand. The conventional wisdom he says is that problem solving is entirely "inherent to the subject at hand," or what I would call domain-specific, ignoring the value of domain-general principles in problem solving. I don't know how much this really defies conventional wisdom, but I agree with him completely and I think this is an important principle. It is a major part of the rationale for involving more people in a collaborative group problem solving process rather than just pulling in a few experts. If the process is good, and this principle is valid, then it has deep implications for improving problem solving in organizations by broadening involvement and investment in the process itself. That brings us to the real question at hand, whether the author's method accomplishes this objective.

Why Apollo?

So far my description here is I think pretty much in line with the author's rationale. Now we come to the real meat. How good is this particular method at getting around our individual and group blind spots, compared to other methods? That's the significance the author claims for this book and for his method, so that's what we really need to know to evaluate this book. The book covers a lot of ideas that are very important to know, but many are common knowledge to experienced problem solvers, so I am not going to focus on those. What I will focus on is what is supposed to be special about ARCA and RealityCharting(tm) in particular.

According to the author, the key distinction between his formal method and all others is that all others are ways of categorizing causes and schemes for voting on the best way to categorize them, while his method discerns (and RealityCharting displays) the actual relationship between different causes, along with the evidence for each cause. (p. 193)

All viable methods of "root cause analysis" in groups involve taking chains of events that would be too numerous and related in too complex a manner to envision in a common way by everyone involved in the process of they were simply described in words. Visual representations of trees of causes play a key role in all formal methods of "root cause analysis," including ARCA.

The trick to understanding the underlying message of this book and grasping the uniqueness claimed for the method is to understand exactly what he means by the *relationships between causes* and how the method forces you to think about evidence for causes. I don't personally favor the author's explanation for why his method is unique. His rules of causality seem awkward to me and I think there is a slightly different and clearer way to think of how his method works.

First, every effect is also a cause. There is no distinction between causes and effects except at the endpoints. Feedback loops of causes that have effects that ultimately lead back to the original cause are common in nature as well as human design and this method has no problem handling them. We start with the effect we are trying to explain and end with causes that either do not need to be explained or cannot yet be explained. This seems fairly straightforward to me for a causal chain, I'm not sure why the author feels it is unique to his method. I suppose it may be that commonly used methods tend to de-emphasize this aspect of causality.

Second, the method forces you to identify both actions and conditions that had to be in place for the action to cause the effect. This isn't useful for causal modeling as much as it is a useful trick for helping to identify all of the causes that might be relevant to solving the problem. Breaking the "Why?" question into an action and a set of conditions is seemingly somewhat unique to this method and helps avoid thinking solely in terms of actions or solely in terms of things that were in place at the time or characteristics of the situation. The theory is that it is usually *easier* to see actions that happened and less obvious what conditions had to be in place as well, but it is often *more useful* to identify the conditions because they tend to be more predictable and more controllable.

Third, the method forces you to have evidence for each cause, and takes an unusual and perhaps mildly questionable empiricist slant here. It distinguishes "sensed" from "inferred" evidence. "Sensed" of course refers to direct observation by someone, with as little interpretation as possible. "Inferred" refers to anything else. Assumptions and opinions represent doubts and must be investigated further, and just about anything that someone doesn't observe directly themselves has some doubt associated with it, whereas direct observation is automatically given very high credibility by the rules of the methodology.

This is a slightly odd sort of slant from my perspective because the credibility of eyewitness testimony in general is often in doubt (8), the reliability of inference is often not obvious, and because in my experience, hypothesis testing is particularly useful and important problem solving tool. Storytelling, much maligned and minimized in the ARCA method for good reason, is built on inferences from observations. However so do scientific theories and mathematical models arise from inferences. (9) The main difference is that science and math involve systematic linking and pragmatic testing of inferences rather than just weaving meaningful narratives. The author frequently asserts in different ways that storytelling negatively impacts problem solving, and defers to direct observation. I would instead argue that both are in doubt, and that their relative strength is not absolute based on source, but depends upon how inferences and observations support each other. One contemporary philosopher usefully compares the relationship to that of a crossword puzzle. (10) It is notable that the ARCA method and reality charting tool have no trouble accommodating different conceptions of the reliability of evidence.

Strengths

1. The philosophy behind the method is extremely inclusive organizationally. The idea is not to find the putative "true causes" but to gather enough information to produce effective solutions. So the method encourages as many people as possible to participate as fully as possible, rather than to argue over the right causes to focus on. That is perhaps the greatest strength of this method, its formal incorporation of diverse perspectives, allowing everyone to be heard and every perspective considered while discouraging the usual drama of competing narratives. Different definitions of the problem are managed by starting from different primary causes and creating separate charts. Since the framing of the problem often guides or constrains the search for a solution, I consider this a strength of this method.

2. ARCA explicitly includes the source and specifics of evidence for each cause, helping to identify and prioritize information gathering tasks to further validate assumptions and opinions, or reproduce observations. You are strongly encouraged to provide evidence for every cause, a process that I think can lead to a far more comprehensive yet focused data gathering effort than other methods.

3. ARCA allows for the explicit representation of causal feedback loops, which can be important factors in understanding what is happening. Many causal charts make this important relationship very difficult.

4. The technique of treating each effect as a cause and subjecting each to requirements for evidence, and explicit follow-up for finding further causes, intermediate causes, information required, or explicit reasons for stopping is a very powerful aid to causal thinking and to me is the core of both the method and the associated software.

Limitations and Criticisms

1. Potential overreliance on unreliable eyewitness testimony vs. discouragement of useful inferences. The process of hypothesis generation is a particular kind of inference where we consider more alternatives than we suggest, and as a result we understand that they are explicitly hypotheses. There is experimental evidence that generating hypotheses ourselves leads to a more realistic appraisal of their strength and less false confidence than inferences provided from other people. (cf. Derek Koehler) Other research supports the notion that our causal judgments are themselves inferences that often result from mental simulation. [This is a comment regarding the method in the book, which downplays the role of "inference" vs. "direct observation." It is a minor point in practice because there is nothing in the RealityCharting tool that forces you to evaluate sources of evidence in a particular way.]

2. Overemphasis on uniqueness of the method. This mostly refers to the tone in which the book is written, which I often found distracting and annoying, wherein it often makes the book sound like a sales presentation for the method and the tool rather than a treatise on effective problem solving. This point goes along with the scarcity of credit to other sources that for me would have greatly enriched my appreciation of the rationale for the method. The author relies too heavily on himself as an authority for his rationale to rate this a five star book.

3. Restriction of causal statements to very brief verb-noun and noun-verb format rather than sentences. I think this has some value in fostering clarity if you manage to find a phrase that everyone happens to understand in the same way. Still, in practice I found that it can be time consuming to come up with these compact ways of expressing causes, and that they were easier to misinterpret than full sentences would be. [This is also a potential issue in the tool because the rule checking tries to enforce it. However, it can be disabled if desired.]

4. Does not allow you to visibly categorize causes except into actions vs. conditions. This is an attribute of both the method and the associated software. This "limitation" is the intentional result of the author's core principle that he feels distinguishes his method in particular: categorizing causes is relatively useless compared to relating them. Categorizing causes is suggested in ARCA only if you are stuck finding plausible candidate causes and want to try thinking in terms of categories in order to help find candidates. Since this is intentional, it is a limitation only in the sense that someone familiar with other methods may run into it and have to think differently in order to use the method as intended.

Given that so far every substantive criticism I've had of this method is easily accommodated by minor changes to the method (as well as in the associated tool, with the exception of categorizing causes) I found this to be a particularly versatile method. It captures a number of key principles of problem solving, especially in groups, and provides practical and effective ways of compensating for our most problematic blind spots and biases. For the most part, I think understanding the principles of ARCA and RealityCharting would probably enhance most other methods as well as the method standing on its own.

A formal method itself is no substitute for improving the reflective intelligence of each problem solver, but I think this method could go a long way in any organization toward getting people to think more effectively, and especially for helping them communicate their best thinking to each other. I found very little to disagree with and much of value in this book and found the method easy to understand and apply.

References

(1) Brian Boyd on art and storytelling as biological adaptations which derive from play.

"On the Origin of Stories: Evolution, Cognition, and Fiction," Brian Boyd, 2009, Belknap Press of Harvard University Press

(2) David Perkins discusses the significance of domain-general principles for effective thinking, and why they can't be replaced completely by intelligence or expertise. A plausible research-based discussion of why we need help getting around our built-in individual blind spots.

"Outsmarting IQ: The Emerging Science of Learnable Intelligence," David Perkins, 1995, Simon and Schuster

(3) The psychological study of how we tend to answer questions of the form "Why ... ?" is called attribution theory. Probably a result of our bias toward storytelling explanations, most attribution theory has attempted to address attributions of people in social situations and sometimes products in the case of consumer research. A much smaller amount of research has been done on the perception of causality in other ways, such as in building causal explanations, and only a tiny amount has made its way into popularly accessible books.

"Causal attribution: from cognitive processes to collective beliefs," Miles Hewstone , Wiley-Blackwell

(4) There are not many modern books that deal relatively broadly with causal modeling from both a philosophical and mathematical perspective. One of the few is Pearl's text.

"Causality: Models, reasoning, and inference," J. Pearl, 2000, Cambridge University Press

(5) The non-conscious processes in realistic problem solving is addressed by a fairly sizeable body of technical literature by Dijksterhuis, Bargh, Gollwitzer, and others, and introduced in an accessible and practical way by Gary Klein.

"Sources of Power: How People Make Decisions," 1999, Gary Klein, MIT Press

(6) The problems exacerbated by group behavior under non-optimal conditions are introduced accessibly and briefly yet very broadly in:

"The Psychology of Judgment and Decision Making," Scott Plous, 1993, Mcgraw-Hill.

(7) The positive potential of groups under optimal conditions is discussed in
"Group Problem Solving" by Patrick Laughlin, 2011, Princeton University Press.

The common belief that direct observation is inherently more reliable than inference is based on two ideas that are at best only partially true: (1) the assumption that reports based on observation does not significantly depend on memory, inference, or explanation, and so is automatically free of distortions, biases, or storytelling, and (2) the assumption that inferences are roughly equivalently reliable under a wide range of conditions.

(8) On the reconstructive nature of memory for events, see:
"Searching for Memory: The Brain, The Mind, and the Past," Daniel L. Schacter (1997), Basic Books

(9) On the nature of inference and especially scientific inference, see:

"Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science," Mayo and Spanos (2010), Cambridge University Press

(10) The useful crossword puzzle metaphor for the relationship of observation and inference is introduced in:

"Evidence and Inquiry: A Pragmatist Reconstruction of Epistemology," Susan Haack (2009), Prometheus Books