Black-and-white thinking about black-and-white thinking

A key concept in cognitive behavioural therapy (CBT) is that people may suffer from cognitive distortions—maladaptive patterns of thinking. For example people may jump to conclusions about what someone else is thinking (mind reading) or about the future (fortune telling). Another cognitive distortion is black-and-white thinking—also known as all-or-nothing thinking or dichotomous reasoning—where people see situations in terms of false dilemmas such as “always” or “never”.

Clearly black-and-white thinking can be harmful. It may obliterate nuance, polarize discussions, and preclude compromise. Black-and-white thinking can also go along with absolutism. When arguments get heated, thinking can often become black and white, opinions can become polarized, and positions can harden. Many challenging situations involve shades of gray. Altering the metaphor a little, it has been argued that such situations require  full-colour thinking. (Incidentally, the term full colour certainly sounds better than black and white, evoking images of colour photographs and television. But this hardly constitutes an argument in favour of full-colour thinking.) It is often suggested that when we look for solutions, instead of the dichotomy of “either/or” we should focus on the spectrum of possibilities evoked by “both, and”.

And yet, dichotomous thinking is not intrinsically invalid. Some situations are naturally dichotomous. It is not possible to both buy a new car and not buy one. Death is notoriously dichotomous. Other situations may indeed involve a continuum, and yet it may still be useful to consider whether some threshold has been crossed. Consider blood pressure: although it is inherently continuous, when it is high enough doctors get concerned and label it hypertension. In such cases the question is always where the cut-off should be, not whether there should be a cut-off. Dichotomous thinking often involves absolutes, but that is not always a bad thing. When a company bids on a contract, they are either successful or not. A bid may be good, but if it is not accepted then that is irrelevant. Dichotomous thinking has several inherent advantages. First, with binary propositions logic is particularly straightforward. Indeed classical logic focuses on such cases. One of the basic principles of logic is known as the law of the excluded middle which asserts that either a proposition is true or its negation is true. Aristotle expressed it in his principle of non-contradiction, in which he asserted that if you have two contradictory propositions, one must be true and the other false. The application of logic to non-binary situations is far more complex. In the early 20th century, Jan Łukasiewicz and Alfred Tarski developed a “many-valued” logic, a version of which is today known as fuzzy logic. While this has various applications, it is certainly not as straightforward as classical logic. A second advantage of dichotomous thinking is that by focusing on clearly defined situations it has the potential to shed light on a situation. Either I catch the train or I don’t. What should I do if I don’t catch it? Either my grandfather gets admitted to hospital or he doesn’t. If he doesn’t get admitted, what arrangements do I need to make? True, such thinking does not cover all aspects of the situations we face, but it can quickly focus on some crucial issues.

Consider the issue of texting while driving. A black-and-white approach is to simply declare that one should never send text messages while driving. If we’re already wanting to engage in this practice, full-colour thinking might suggest that it is sometimes acceptable to do this (perhaps if we only send short messages). However, evidence shows that texting while driving is exceedingly dangerous. In this case, black-and-white thinking will likely lead to the better decision.

The problem then is not with black-and-white thinking per se, but with its inappropriate use. One way this can happen is when black-and-white thinking becomes a habit. Rather than choosing to apply black-and-white thinking in a selective fashion according to the situation, we apply it reflexively.

One difficulty that arises when we review our patterns of thinking is in the way we often use language. Common expressions can make it sound like we are using black-and-white thinking. For example, “Did you like the movie?”, “Are you happy with your job?” Ordinarily we know to interpret expressions like these in a more nuanced way—”How well did you like the movie?”, “How happy are you with your job?”. It is sometimes suggested that communication can be improved by avoiding black-and-white language. That may be a useful approach, particularly when relations are strained and communication is breaking down. But it would be a mistake to suppose that the patterns and difficulties of communication and thinking reflect one and the same issue.

Black-and-white thinking has been widely criticized—sometimes in a way that can itself be best described as black-and-white. For example, one article, with apparent unawareness of the irony of the situation, promises to help you “Get Rid of Black and White Thinking Once and for All”.

The challenge is not to foreswear black-and-white thinking, but rather to use it appropriately. The best approach may be to flexibly select between black-and-white and full-colour thinking, acknowledging the limitations in both case. Thinking can be distorted by any number of biases, cognitive short-cuts, and fallacies. When there is a lot at stake, one should always scrutinize one’s thought processes and conclusions, and even when no defects are immediately apparent, a degree of humility is called for.


Distinct kicking motion

I am inclined to believe that every sport must have at least one peculiar rule. Perhaps it’s related to the complexity necessary to make the game interesting. In American football, taking the knee is odd—and distasteful to some fans. I look forward to learning about other such curiosities. But here I’d like to mention a rule I find particularly intriguing: the distinct kicking motion rule of the National Hockey League (NHL).

The rule is that if the puck bounces off an offensive player’s skate straight into the other side’s net then it counts as a goal unless the player made a “distinct kicking motion”. What does that mean? According to an article in The Hockey News:

Until this season, the rulebook contained the definition that a distinct kicking motion “is one which, with a pendulum motion, the player propels the puck with his skate into the net.” Now that passage in Rule 34.4 38.4 (iv) is gone, replaced by a motion in “which the player propels the puck with his skate into the net,” although if that motion was made by the player while he turned his skate to stop, the goal is allowed.

The motivation for this rule is clear enough: deliberately kicking the puck threatens to turn hockey into soccer. And yet the puck will sometimes unintentionally bounce off a player’s skate. Indeed this was just what I saw on TV in a recent NHL game. My untrained opinion (after seeing probably five replays) was that there was no distinct kicking motion—which also happened to be the referees’ ruling.

What I find curious, however, is this. Given that the puck bounced off the player’s skate into the net, is the lack of a kicking motion evidence that the player had no intention of using his skate to direct the puck? On the contrary: if the player wanted to score using his skate, then in this case not kicking the puck was just the right thing to do!

I see a philosophical dimension here. Consideration of counterfactuals (had the player instead kicked the puck, perhaps it would have missed the net!) suggests that using observed actions to draw conclusions about intentions may be exceedingly challenging.

Newton’s loss of motion

When talk turns to philosophy of science, it’s common for people to bring up cutting edge topics like quantum mechanics, the Higgs boson, and superstring theory. But as it happens, some fascinating issues arise when we consider much a less esoteric example: Newton’s laws of motion.

Newton’s theory of mechanics, famously expounded in the Principia, was a landmark achievement and a key development in the scientific revolution. But over the years a number of thinkers have noted that lurking at the core of Newton’s laws are some remarkable logical and philosophical inconsistencies. In a 1985 article in the American Journal of Physics, Robert Brehme [1] wrote:

A physical theory should be precise, economical, and logical. Ideally it is expressed as a mathematical law involving entities whose definitions and measures lie outside the law. Most physical theories conform to these criteria. One serious exception, however, is the circular logic that seems to occur in the connection made between Newton’s first two laws of motion, the inertial frame, and force. Simply put, the circularity is that Newton’s laws are said to hold only in an inertial frame, while an inertial frame is defined as any frame in which Newton’s laws hold.

Consider Newton’s second law, which can be expressed using the famous equation F=ma, where F represents force, m represents mass, and a represents acceleration. If this is indeed a scientific law then it can be empirically verified. But here a problem arises, as noted by Leonard Eisenbud in 1958 [2]:

Implicit in his statement of the [second] law is the assumption of the prior existence of a quantitative definition of “force”; unfortunately Newton nowhere gives such a definition.

The same issue applies to mass. As Brehme writes:

… if Newton’s second law is to be a true law and not merely used to define force or mass, a means must exist for determining force and mass independent of one another and independent of the second law.

Eisenbud notes that during the second half of the nineteenth century, Newton’s laws were criticized by physicists, philosophers, and mathematicians. He cites Hertz (1894) [3]:

It is exceedingly difficult to expound to thoughtful hearers the very introduction to mechanics without being occasionally embarrassed, without feeling tempted now and again to apologize, without wishing to get as quickly as possible over the rudiments and on to examples which speak for themselves. I fancy that Newton himself must have felt this embarrassment.

As the theory of relativity was being developed at the beginning of the twentieth century classical mechanics was subjected to further scrutiny. Part of the problem was a lack of clarity. Writing in 1902, Henri Poincaré [4] pointed out that “treatises on mechanics do not clearly distinguish between what is experiment, what is mathematical reasoning, what is convention, and what is hypothesis.”

It might be asked why such considerations should be of concern to us. Could this just be philosophical nit-picking? Indeed Eisenbud has noted that “For almost all practical purposes … [Newton’s] considerations are entirely adequate.” But the critical evaluation of Newton’s laws is important for at least three reasons.

First, note that just three years after Poincaré’s book was published, Einstein published his theory of special relativity. This huge step forward was enabled by the critical examination of Newton’s laws that took place over the preceding decades. In particular, a careful consideration of inertial frames of references was a key element in Einstein’s reasoning.

Second, from the perspective of science teaching, I believe that we do a disservice to inquiring students when we gloss over the logical inconsistencies in Newton’s laws. Science is not a collection of facts to be memorized (unless we’re preparing for a trivia game). Instead, science is a complex interplay of observation, experiment, intuition, deduction, hypothesis, and modeling. Students need to be empowered to question the received wisdom–it may not turn out to be so wise.

Finally, from the perspective of the history and philosophy of science, a critical examination of Newton’s laws raises some profound questions. What is a scientific law compared to a definition? How do sets of definitions interrelate? When and why is a definition useful? How do such definitions arise, and how did Newton arrive at such a profoundly useful and important set of definitions? To note that F=ma is a definition is not to say that it is arbitrary. To what extent is the choice of definition constrained, and how? That these and other such questions arise from consideration of a theory of mechanics originating over 300 years ago and still widely used may give us pause. While the latest theories of physics can raise intriguing issues in philosophy of science, even well-worn theories can provide rich food for thought.


  1. Brehme RW, 1985. On force and the inertial frame. American Journal of Physics 53 , 952.

  2. Eisenbud L, 1958. On the classical laws of motion. American Journal of Physics 26 , 144.
  3. Hertz H, 1889. Principles of Mechanics, translated by D.E. Johnes and J.T. Walley. The Macmillan Company, New York.
  4. Poincaré H, 1902. Science and Hypothesis. Translated by WJ Greenstreet, 1905. The Walter Scott Publishing Company. New York. [Full text pdf available through Project Gutenberg.]

Rethinking data

[Reposted from Logbase2 June 2012]

“Data! Data! Data!” he cried impatiently. “I can’t make bricks without clay.” — Sherlock Holmes in The Adventure of the Copper Beeches.

Data may be the preeminent obsession of our age. We marvel at the ever-growing quantity of data on the Internet, and fortunes are made when Google sells shares for the first time on the stock market. We worry about how corporations and governments collect, protect, and share our personal information. A beloved character on a television science fiction show is named Data. We spend billions of dollars to convert the entire human genome into digital data, and having completed that, barely pause for breath before launching similar and even larger bioinformatic endeavours. All this attention being paid to data reflects a real societal transformation as ubiquitous computing and the Internet refashion our economy and, in some respects, our lives. However, as with other important transformations—think of Darwin’s theory of natural selection, and the revolutionary advances in genetics and neuroscience—misinterpretation, misapplication, hype, and fads can develop. In this post, I’d like to examine the current excitement about data and where we may be going astray.

Big Data

Writing in the New York Times, Steve Lohr points out that larger and larger quantities of data are being collected—a phenomenon that has been called “Big Data”:

In field after field, computing and the Web are creating new realms of data to explore — sensor signals, surveillance tapes, social network chatter, public records and more. And the digital data surge only promises to accelerate, rising fivefold by 2012, according to a projection by IDC, a research firm.

Widespread excitement is being generated by the prospect of corporations, governments, and scientists mining these immense data sets for insights. In 2008, a special issue of the journal Nature was devoted to Big Data. Microsoft Research’s 2009 book, The Fourth Paradigm: Data-Intensive Scientific Discovery, includes these reflections by Craig Mundie (p.223):

Computing technology, with its pervasive connectivity via the Internet, already underpins almost all scientific study. We are amassing previously unimaginable amounts of data in digital form—data that will help bring about a profound transformation of scientific research and insight. 

The enthusiasm in the lay press is even less restrained. Last November, Popular Science had a special issue all about data. It has a slightly breathless feel—one of the articles is titled “The Glory of Big Data”—which is perhaps not so surprising in a magazine whose slogan is “The Future Now”.

Data Science

Along with the growth in data, there has been a tremendous growth in analytical and computational tools for drawing inferences from large data sets. Most prominently, techniques from computer sciencein particular data mining and machine learninghave frequently been applied to big data. These approaches can often be applied automatically—which is to say, without the need to make much in the way of assumptions, and without explicitly specifying models. What is more, they tend to be scalable—it is feasible (in terms of computing resources and time) to apply them to enormous data sets. Such approaches are sometimes seen as black boxes in that the link between the inputs and the outputs is not entirely clear. To some extent these characteristics stand in contrast with statistical techniques, which have been less optimized for use with very large data sets and which make more explicit assumptions based on the nature of the data and the way they were collected. Fitted statistical models are interpretable, if sometimes rather technical.

In an article on big data, Sameer Chopra suggests that organizations should “embrace traditional statistical modeling and machine learning approaches”. Some have argued that a new discipline is forming dubbed data sciencewhich combines these and other techniques for working with data. In 2010, Mike Loukides at O’Reilly Media wrote a good summary of data science, except for this odd claim:

Using data effectively requires something different from traditional statistics, where actuaries in business suits perform arcane but fairly well-defined kinds of analysis.

Leaving aside the confusion between statistics and actuarial science (not to mention stereotyped notions of typical attire), what is curious is the suggestion that “traditional statistics” has little role to play in the effective use of data. Chopra is more diplomatic: machine learning “lends itself better to the road ahead”. Now, in many cases, a fast and automatic method may indeed be just what’s needed. Consider the recommendations we have come to expect from online stores. They may not be perfect, but they can be quite convenient. Unfortunately, the successes of computing-intensive approaches for some applications has encouraged some grandiose visions. In an emphatic piece titled “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete“, Chris Anderson, the editor in chief of Wired magazine, writes:

This is a world where massive amounts of data and applied mathematics replace every other tool that might be brought to bear. Out with every theory of human behavior, from linguistics to sociology. Forget taxonomy, ontology, and psychology. Who knows why people do what they do? The point is they do it, and we can track and measure it with unprecedented fidelity.


We can stop looking for models. We can analyze the data without hypotheses about what it might show. We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.

Anderson proposes that instead of taking a scientific approach, we can just “throw the numbers” into a machine and through computational alchemy transform data into knowledge. (Similar thinking shows up in commonplace references to “crunching” numbers, a metaphor I havepreviously criticized.) The suggestion that we should “forget” the theory developed by experts in the relevant field seems particularly unwise. Theory and expert opinion are always imperfect, but that doesn’t mean they should be casually discarded.

Anderson’s faith in big data and blind computing power can be challenged on several grounds. Take selection bias, which can play havoc with predictions. As an example, consider the political poll conducted by The Literary Digest magazine, just before the 1936 presidential election. The magazine sent out 10 million postcard questionnaires to its subscribers, and received about 2.3 million back. In 1936, that was big data. The results clearly pointed to a victory by the republican challenger, Alf Landon. In fact, Franklin Delano Roosevelt won by a landslide. The likely explanation for this colossal failure: for one thing, subscribers to The Literary Digest were not representative of the voting population of the United States; for another, the 23% who responded to the questionnaire were likely quite different from those who did not. This double dose of selection bias resulted in a very unreliable prediction. Today, national opinion polls typically survey between 500 and 3000 people, but those people are selected randomly and great efforts are expended to avoid bias. The moral of this story is that, contrary to the hype, bigger data is not necessarily better data. Carefully designed data collection can trump sheer volume of data. Of course it all depends on the situation.

Selection biases can also be induced during data analysis when cases with missing data are excluded, since the pattern of missingness often carries information. More generally, bias can creep into results in any number of ways, and extensive lists of biases have been compiled. One important source of bias is the well-known principle of Garbage In Garbage Out. Anderson refers to measurements taken with “unprecedented fidelity”. It is true that in some areas, impressive technical improvements in certain measurement have been made, but data quality issues are much broader and are usually problematic. Data quality issues can never be ignored, and can sometimes completely derail an analysis.

Another limitation of Anderson’s vision concerns the goals of data analysis. When the goal is prediction, it may be quite sufficient to algorithmically sift through correlations between variables. Notwithstanding the previously noted hazards of prediction, such an approach can be very effective. But data analysis is not always about prediction. Sometimes we wish to draw conclusions about the causes of phenomena. Such causal inference is best achieved through experimentation, but here a problem arises: big data is mostly observational. Anderson tries to sidestep this by claiming that with enough data “Correlation is enough”:

Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all.

But on the contrary, investigations of cause and effect (mechanistic explanations) are central to both natural and social science. And in applied fields such as government policy, it is often of fundamental importance to understand the likely effect of interventions. Correlations alone don’t answer such questions. Suppose, for example, there is a correlation between A and B. Does A affect B? Does B affect A? Does some third factor C affect both A and B? This last situation is known as confounding (for a good introduction, see this article [pdf]). A classic example concerns a positive correlation between the number of drownings each month and ice cream sales. Of course this is not a causal relationship. The confounding factor here is the season: during the warmer periods of the year when people consume more ice cream, there are far more water activities and hence drownings. When a confounding factor is not taken into account, estimates of the effect of one factor on another may be biased. Worse, this bias does not go away as the quantity of data increases—big data can’t help us here. Finally, confounding cannot be handled automatically; expert input is indispensable in any kind of causal analysis. We can’t do without theory.

Big data affords many new possibilities. But just being big does not eliminate the problems that have plagued the analysis of much smaller data sets. Appropriate use of data still requires careful thought—about both the content area of interest and the best tool for the job.

Thinking about Data

It is also useful to think more broadly about the concept of data. Let’s start with an examination of the word data itself, to see what baggage it carries.

We are inconsistent in how we talk about data. The words data and information are often used synonymously (think of “data processing” and “information processing”). Notions of an information hierarchy have been around for a long time. One model goes by the acronym DIKW, representing an ordered progression from Data to Information to Knowledge and eventually Wisdom. Ultimately, these are epistemological questions, and easy answers are illusory.

Nevertheless, if what we mean by data is the kind of thing stored on a memory stick, then data can be meaningless noise, the draft of a novel, a pop song, the genome of a virus, a blurry photo taken by a cellphone, or a business’s sales records. Each of these types of information and an endless variety of others can be stored in digital memory: on one level all data are equivalent. Indeed the mathematical field of information theory sets aside the meaning or content of data, and focuses entirely on questions about encoding and communicating information. In the same spirit, Chris Anderson argues that we need “to view data mathematically first and establish a context for it later.”

But when we consider the use of data, it makes no sense to think of all data as equivalent. The complete lyrics of all of the songs by the Beatles is not the same as a CT scan. Data are of use to us when they are “about” something. In philosophy this is the concept of intentionality, which is an aspect of consciousness. By themselves, the data on my memory stick have no meaning. A human consciousness must engage with the data for them to be meaningful. When this takes place, a complex web of contextual elements come into play. Depending on who is reading them, the Beatles’ lyrics may call to mind the music, the cultural references, the history of rock and roll, and diverse personal associations. A radiologist who examines a CT scan will recognize various anatomical features and perhaps concerning signs of pathology. Judgements of quality may also arise, whether in mistranscribed lyrics or a poorly performed CT scan.

The word data is the plural of the Latin word datum, meaning “something given”. So the data are the “givens” in a problem. But in many cases, it might be helpful to think of data as takenrather than given. For example, when you take a photograph, you have a purpose in mind, you actively choose a scene, include some features and exclude others, adjust the settings of the camera. The quality of the resulting image depends on how steady your hand is, how knowledgeable you are of the principles of photography. Even when a photograph is literally given to you by someone else, it was still taken by somebody. The camera never lies, but the photograph may be misunderstood or misrepresented.

When a gift is given to you, it is easy to default to the passive role of recipient. The details of how the gift was selected and acquired may be entirely unknown to you. A dealer in fine art would carefully investigate a newly acquired work to determine its provenance and authenticity. Similarly, when you receive data from an outside source, it is important to take an active role. At the very least, you should ask questions. Chris Anderson claims that “With enough data, the numbers speak for themselves.” But on their own, the numbers never speak for themselves, any more than a painting stolen during WWII will whisper the secret of its rightful ownership. One common source of received data today is administrative data, that is, data collected as part of an organization’s routine operations. Rather than taking such data at face value, it is important to investigate the underlying processes and context.

It is also possible to make use of received data to design a study. For example, to investigate the effect of a certain exposure, cases of a rare outcome may be selected from a data set and matched with controls, that is individuals who are similar except that they did not experience that outcome. (This is a matched case-control study.) Appropriate care must be taken in how the cases and controls are selected, and in ensuring that any selection effects in the original database do not translate into bias in the analysis. Tools for the valid and efficient analysis of suchobservational studies have been investigated by epidemiologists and statisticians for over 50 years.

When we collect the data ourselves, we have an opportunity to take an active role from the start. In an experiment, we manipulate independent variables and measure the resulting values of dependent variables. Careful experimental design lets us accurately and efficiently obtain results. In many cases, however, true experiments are not possible. Instead, observational studies, where there is no manipulation of independent variables, are used. Numerous designs for observational studies exist, including case-control (as mentioned above), cohort, and cross-sectional. Again, careful design is vital to avoid bias, and to efficiently obtain results.


Excitement over a new developmentbe it a discovery, a trend, or a way of thinkingcan sometimes spill over, like popcorn jumping from a popper. This may give rise to related, but nevertheless distinct ideas. In the heat of the excitement (and not infrequently a good deal of hype), it’s important to evaluate the quality of the ideas. Exaggerated claims may not be hard to identify, but they are also frequently pardoned as merely an excess of enthusiasm.

Still, the underlying bad idea may, in subtler form, gradually gain acceptance. The costs may only be appreciated much later. Today it is easy to see how damaging ideas like social Darwinismthe malignant offspring of a very good ideaproved to be. But at the time, it may have seemed like a plausible extrapolation from a brilliant new theory.

The role of data in our societies and our own lives is becoming increasingly central. We live in a world where the quantity of data is exploding and truly gargantuan data sets are being generated and analyzed. But it is important that we not become hypnotized by their immensity. It is all too easy to see data as somehow magical, and to imagine that big data combined with computational brute force will overcome all obstacles.

Let’s enjoy the popcornbut turn down the heat a little.

Be the change?

[Reposted from Logbase2 Nov 2014]

“Be the change you wish to see in the world”–It’s no wonder this saying (let’s call it BTC) has become so popular. From its sense of immediacy to its spiritual turn of phrase, BTC hits all the right notes. It doesn’t hurt that it is commonly attributed to Gandhi, even though, as writer Brian Morton has noted, the closest Gandhi came to BTC was a passage including these words: “If we could change ourselves, the tendencies in the world would also change.”

Although BTC echoes some of Gandhi’s themes, its phrasing and emphasis are notably different. What is clear is that its concise form delivers a potent message about the potential for transformation–and this provides us with a window into contemporary values.

BTC suggests that if, for example, you wish for more patience in the world, you should be more patient yourself. Presumably if you succeed in becoming more patient, then you have increased the global level of patience. Furthermore, your example may encourage others. This appears plausible, and in cases like this BTC seems to provide good guidance.

What if you seek a reduction in greenhouse gas emissions? You can’t “be” less greenhouse gas emission, but following the spirit of BTC, you should aim to produce less emissions yourself. But suppose you want an improvement in the human rights situation in Burma? How can you “be” such a change? Here, there isn’t much guidance. BTC is sometimes interpreted to mean “If you want to see change in the world, then do something.” But this is too broad. BTC is more than an encouragement to take action–it’s about personal change as a vehicle for transforming the world.

Like many sayings, BTC is a mixed bag. To its credit, it encourages each of us to examine the role we play in larger-scale problems, and it calls us to take action. But troublingly, BTC hints that any problem can be solved by changing individual behaviours. Thus, the problem of greenhouse gas emissions could be solved if we carpooled and used public transportation, used less energy to heat and cool our homes, and so forth. But while individual lifestyle choices are clearly important, the problem is much more extensive and complex than this. Greenhouse gas emissions can be attributed not only to individual choices but also to large-scale industrial and agricultural operations, not to mention military activities. It might be argued that such factors can ultimately be traced to individual lifestyle choices: companies only produce what customers demand, governments simply respond to the public will. But this is simplistic: companies may be driven by the market, but they also affect the market through advertising and political influence; governments respond not only to the public will, but also to powerful people and corporations.

Regardless, we still face the question of how best to effect change. BTC encourages each of us to change ourselves. But even in problems where individual behaviour plays a central role, broader issues are often involved. For example, automobiles are a major producer of greenhouse gases. But our reliance on them is part of an intricate web of social and economic factors, such as urban sprawl, public transit options, tax policies, and government regulations. If complex problems are seen largely as personal lifestyle issues, structural factors may not get the attention they deserve. At its worst, a focus on personal lifestyle can become a fetish, and broader issues may be ignored.

During the American civil rights movement, many wished to see an end to racial discrimination. BTC would suggest that those people should have worked to end their own discriminatory behaviour. But discrimination was more than an individual behaviour, it was an entrenched institutional problem. The civil rights movement used political action such as protest marches to press for structural changes in American society, such as desegregation of schools and the outlawing of discriminatory employment practices. Of course, in many cases personal transformation and political change go hand in hand. However BTC tends to encourage–and reflect–a belief that personal transformation is sufficient.

Another interesting aspect of BTC is the distinctly spiritual tone in the words “be the change”, echoing the transformative themes so common in religious traditions. The notion that transcendent meaning may be found in personal change need not stand in opposition to an understanding of the mechanisms by which broader change can be effected. But BTC conflates personal change with change in the world, hinting that in some mystical sense they are the same thing.

Equating personal and societal change obscures the mechanisms by which change is actually brought about. To understand these mechanisms, we need to pay closer attention to the messy connections between our efforts and their outcomes, including the role of other contributing factors, barriers to change, possible synergies, and the unanticipated consequences that our actions may produce. A commitment to change requires considering the pros and cons of various choices in the face of inevitable uncertainty. Unfortunately, BTC may cut this process short.

The popularity of BTC reflects a concern about our role in the problems of the world and a desire to bring about change, but it also reflects our society’s preoccupation with self-improvement, which sometime veers into self absorption. BTC deftly substitutes personal transformation for global change. The risk is that even as it empowers us to transform ourselves, BTC threatens to further disengage us from the world.