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Is Fault Tree Analysis Deductive?
Posted in Engineering & Applied Physics, Probability and Risk, Risk Management on December 2, 2013
An odd myth persists in systems engineering and risk analysis circles. Fault tree analysis (FTA), and sometimes fault trees themselves, are said to be deductive. FMEAs are called inductive. How can this be?
By fault trees I mean Boolean logic modeling of unwanted system states by logical decomposition of equipment fault states into combinations of failure states of more basic components. You can read more on fault tree analysis and its deductive nature at Wikipedia. By FMEA (Failure Mode & Effects Analysis) I mean recording all the things that can go wrong with the components of a system. Writers who find fault trees deductive also find FMEAs, their complement, to be inductive. I’ll argue here that building fault trees is not a deductive process, and that there is possible harm in saying so. Secondarily, I’ll offer that while FMEA creation involves inductive reasoning, the point carries little weight, since the rest of engineering is inductive reasoning too.
Word meanings can vary with context; but use of the term deductive is consistent across math, science, law, and philosophy. Deduction is the process of drawing a logically certain conclusion about a particular instance from a rule or premise about the general. Assuming all men are mortal, if Socrates is a man, then he is mortal. This is true regardless of the meaning of the word mortal. It’s truth is certain, even if Socrates never existed, and even if you take mortal to mean living forever.
Example from a software development website:
FMECA is an inductive analysis of system failure, starting with the presumed failure of a component and analyzing its effect on system stability: “What will happen if valve A sticks open?” In contrast, FTA is a deductive analysis, starting with potential or actual failures and deducing what might have caused them: “What could cause a deadlock in the application?”
The well-intended writer says we deduce the causes of the effects in question. Deduction is not up to that task. When we infer causes from observed effects, we are using induction, not deduction.
How did the odd claims that fault trees and FTAs are deductive arise? It might trace to William Vesely, NASA’s original fault tree proponent. Vesely sometimes used the term deductive in his introductions to fault trees. If he meant that the process of reducing fault trees into cut sets (sets of basic events or initiators) is deductive, he was obviously correct. But calculation isn’t the critical aspect of fault trees; constructing them is where the effort and need for diligence lie. Fault tree software does the math. If Vesely saw the critical process of constructing fault trees and supplying them with numerical data (often arduous, regardless of software) as deductive – which I doubt – he was certainly wrong.
Inductive reasoning, as used in science, logic and philosophy, means inferring general rules or laws from observations of particular instances. The special use of the term math induction actually refers to deduction, as mathematicians are well aware. Math induction is deductive reasoning with a confusing title. Induction in science and engineering stems from our need to predict future events. We form theories about how things will behave in the future based on observations of how similar things behaved in the past. As I discussed regarding Bacon vs. Descartes, science is forced into the realm of induction because deduction never makes contact with the physical world – it lives in the mind.
Inductive reasoning is exactly what goes on when you construct a fault tree. You are making inferences about future conditions based on modeling and historical data – a purely inductive process. The fact that you use math to solve fault trees does not make fault trees any more deductive than the presence of math in lab experiments makes empirical science deductive.
Does this matter?
It’s easy enough to fix this technical point in descriptions fault tree analysis. We should do so, if merely to avoid confusing students. But more importantly, quantitative risk analysis – including FTA – has its enemies. They range from several top consultancies selling subjective, risk-score matrix methodologies dressed up in fancy clothes (see Tony Cox’s SIRA presentation on this topic) to some of NASA’s top management – those flogged by Richard Feynman in his minority report on the Challenger disaster. The various criticisms of fault tree analysis say it is too analytical and correlates poorly with the real world. Sound familiar? It echoes a feud between the heirs of Bacon (induction) and the heirs of Descartes (deduction). Some of fault trees’ foes find them overly deductive. They then imply that errors found in past quantitative analyses impugn objectivity itself, preferring subjective analyses based on expert opinion. This curious conclusion would not follow, even if fault tree analyses were deductive, which they are not.
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Science is the belief in the ignorance of experts. – Richard Feynman
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Great Innovative Minds: A Discord on Method
Posted in Innovation management, Multidisciplinarians on November 19, 2013
Great minds do not think alike. Cognitive diversity has served us well. That’s not news to those who study innovation; but I think you’ll find this to be a different take on the topic, one that gets at its roots.
The two main figures credited with setting the scientific revolution in motion did not agree at all on what the scientific method actually was. It’s not that they differed on the finer points; they disagreed on the most basic aspect of what it meant to do science – though they didn’t yet use that term. At the time of Francis Bacon and Rene Descartes, there were no scientists. There were natural philosophers. This distinction is important for showing just how radical and progressive Descartes and Bacon were.
In Discourse on Method, Descartes argued that philosophers, over thousands of years of study, had achieved absolutely nothing. They pursued knowledge, but they had searched in vain. Descartes shared some views with Aristotle, but denied Aristotelian natural philosophy, which had been woven into Christian beliefs about nature. For Aristotle, rocks fell to earth because the natural order is for rocks to be on the earth, not above it – the Christian version of which was that it was God’s plan. In medieval Europe truths about nature were revealed by divinity or authority, not discovered. Descartes and Bacon were both devout Christians, but believed that Aristotelian philosophy of nature had to go. Observing that there is no real body of knowledge that can be claimed by philosophy, Descartes chose to base his approach to the study of nature on mathematics and reason. A mere 400 years after Descartes, we have trouble grasping just how radical this notion was. Descartes believed that the use of reason could give us knowledge of nature, and thus give us control over nature. His approach was innovative, in the broad sense of that term, which I’ll discuss below. Observation and experience, however, in Descartes’ view, could be deceptive. They had to be subdued by pure reason. His approach can be called rationalism. He sensed that we could use rationalism to develop theories – predictive models – with immense power, which would liberate mankind. He was right.
Francis Bacon, Descartes slightly older counterpart in the scientific revolution, was a British philosopher and statesman who became attorney general in 1613 under James I. He is now credited with being the father of empiricism, the hands-on, experimental basis for modern science, engineering, and technology. Bacon believed that acquiring knowledge of nature had to be rooted in observation and sensory experience alone. Do experiments and then decide what it means. Infer conclusions from the facts. Bacon argued that we must quiet the mind and apply a humble, mechanistic approach to studying nature and developing theories. Reason biases observation, he said. In this sense, the theory-building models of Bacon and Descartes were almost completely opposite. I’ll return to Bacon after a clarification of terms needed to make a point about him.
Innovation has many meanings. Cicero said he regarded it with great suspicion. He saw innovation as the haphazard application of untested methods to important matters. For Cicero, innovators were prone to understating the risks and overstating the potential gains to the public, while the innovators themselves had a more favorable risk/reward quotient. If innovation meant dictatorship for life for Julius Caesar after 500 years of self-governance by the Roman people, Cicero’s position might be understandable.
Today, innovation usually applies specifically to big changes in commercial products and services, involving better consumer value, whether by new features, reduced prices, reduced operator skill level, or breaking into a new market. Peter Drucker, Clayton Christensen and the tech press use innovation in roughly this sense. It is closely tied to markets, and is differentiated from invention (which may not have market impact), improvement (may be merely marginal), and discovery.
That business-oriented definition of innovation is clear and useful, but it leaves me with no word for what earlier generations meant by innovation. In a broader sense, it seems fair that innovation also applies to what vanishing point perspective brought to art during the renaissance. John Locke, a follower of both Bacon and Descartes, and later Thomas Jefferson and crew, conceived of the radical idea that a nation could govern itself by the application of reason. Discovery, invention and improvement don’t seem to capture the work of Locke and Jefferson either. Innovation seems the best fit. So for discussion purposes, I’ll call this innovation in the broader sense as opposed to the narrower sense, where it’s tied directly to markets.
In the broader sense, Descartes was the innovator of his century. But in the narrow sense (the business and markets sense), Francis Bacon can rightly be called the father of innovation – and it’s first vocal advocate. Bacon envisioned a future where natural philosophy (later called science) could fuel industry, prosperity and human progress. Again, it’s hard to grasp how radical this was; but in those days the dominant view was that mankind had reached its prime in ancient times, and was on a downhill trajectory. Bacon’s vision was a real departure from the reigning view that philosophy, including natural philosophy, was stuff of the mind and the library, not a call to action or a route to improving life. Historian William Hepworth Dixon wrote in 1862 that everyone who rides in a train, sends a telegram or undergoes a painless surgery owes something to Bacon. In 1620, Bacon made, in The Great Instauration, an unprecedented claim in the post-classical world:
“The explanation of which things, and of the true relation between the nature of things and the nature of the mind … may spring helps to man, and a line and race of inventions that may in some degree subdue and overcome the necessities and miseries of humanity.”
In Bacon’s view, such explanations would stem from a mechanistic approach to investigation; and it must steer clear of four dogmas, which he called idols. Idols of the tribe are the set of ambient cultural prejudices. He cites our tendency to respond more strongly to positive evidence than to negative evidence, even if they are equally present; we leap to conclusions. Idols of the cave are one’s individual preconceptions that must be overcome. Idols of the theater refer to dogmatic academic beliefs and outmoded philosophies; and idols of the marketplace are those prejudices stemming from social interactions, specifically semantic equivocation and terminological disputes.
Descartes realized that if you were to strictly follow Bacon’s method of fact collecting, you’d never get anything done. Without reasoning out some initial theoretical model, you could collect unrelated facts forever with little chance of developing a usable theory. Descartes also saw Bacon’s flaw in logic to be fatal. Bacon’s method (pure empiricism) commits the logical sin of affirming the consequent. That is, the hypothesis, if A then B, is not made true by any number of observations of B. This is because C, D or E (and infinitely more letters) might also cause B, in the absence of A. This logical fallacy had been well documented by the ancient Greeks, whom Bacon and Descartes had both studied. Descartes pressed on with rationalism, developing tools like analytic geometry and symbolic logic along the way.
Interestingly, both Bacon and Descartes were, from our perspective, rather miserable scientists. Bacon denied Copernicanism, refused to accept Kepler’s conclusion that planet orbits were elliptical, and argued against William Harvey’s conclusion that the heart pumped blood to the brain through a circulatory system. Likewise, by avoiding empiricism, Descartes reached some very wrong conclusions about space, matter, souls and biology, even arguing that non-human animals must be considered machines, not organisms. But their failings were all corrected by time and the approaches to investigation they inaugurated. The tension between their approaches didn’t go unnoticed by their successors. Isaac Newton took a lot from Bacon and a little from Descartes; his rival Gottfried Leibniz took a lot from Descartes and a little from Bacon. Both were wildly successful. Science made the best of it, striving for deductive logic where possible, but accepting the problems of Baconian empiricism. Despite reliance on affirming the consequent, inductive science seems to work rather well, especially if theories remain open to revision.
Bacon’s idols seem to be as relevant to the boardroom as they were to the court of James I. Seekers of innovation, whether in the classroom or in the enterprise, might do well to consider the approaches and virtues of Bacon and Descartes, of contrasting and fusing rationalism and observation. Bacon and Descartes envisioned a brighter future through creative problem-solving. They broke the bonds of dogma and showed that a new route forward was possible. Let’s keep moving, with a diversity of perspectives, interpretations, and predictive models.
Feynman’s Minority Report and Top-Down Design
Posted in Aerospace, Engineering & Applied Physics, Probability and Risk, Systems Engineering on November 11, 2013
On reading my praise of Richard Feynman, a fellow systems engineer and INCOSE member (International Council on Systems Engineering) suggested that I read Feynman’s Minority Report to the Space Shuttle Challenger Enquiry. He said I might not like it. I read it, and I don’t like it, not from the perspective of a systems engineer.
 Challenger explosion, Jan. 28, 1986 |
I should be clear on what I mean by systems engineering. I know of three uses of the term: first, the engineering of embedded systems, i.e., firmware (not relevant here); second, an organizational management approach (relevant, but secondary); third, a discipline aimed at design of assemblies of components to achieve a function that is greater than those of its constituents (bingo). Definitions given by others are useful toward examining Feynman’s minority report on the Challenger.
Simon Ramo, the “R” in TRW and inventor of the ICBM, put it like this: “Systems engineering is a discipline that concentrates on the design and application of the whole (system) as distinct from the parts. It involves looking at a problem in its entirety, taking into account all the facets and all the variables and relating the social to the technical aspect.”
Howard Eisner of GWU says, “Systems engineering is an iterative process of top-down synthesis, development, and operation of a real-world system that satisfies, in a near optimal manner, the full range of requirements for the system.”
INCOSE’s definition is pragmatic (pleasantly, as their guide tends a bit toward strategic-management jargon): “Systems engineering is an interdisciplinary approach and means to enable the realization of successful systems.”
Feynman reaches several sound conclusions about root causes of the flight 51-L Challenger disaster. He observes that NASA’s safety culture had critical flaws and that its management seemed to indulge in fantasy, ignoring the conclusions, advice and warnings of diligent systems and component engineers. He gives specific examples of how NASA management grossly exaggerated the reliability of many systems and components in the shuttle. On this point he concludes, “reality must take precedence over public relations, for nature cannot be fooled.” He describes a belief by management that because an anomaly was without consequence in a previous mission, it is therefore safe. Most importantly, he cites the erroneous use of the concept of factor of safety around the O-ring seals between the two lower segments of the solid rocket motors by NASA management (the Rogers Commission also agrees that failure of these O-rings was the root cause of the disaster). An NASA report on seal erosion in an earlier mission (flight 51-C) had assigned a safety factor of three, based on the seals having eroded only one third of the amount thought to be critical. Feynman replies that the O-rings were not designed to erode, and hence the factor-of-safety concept did not apply. Seal erosion was a failure of the design, catastrophic or not; there was no safety factor at all. “Erosion was a clue that something was wrong; not something from which safety could be inferred.”
But later Feynman incorrectly states that establishing a hypothetical propulsion system failure rate of 1 in 100,000 missions would require an inordinate number of tests to determine with confidence. Here he seems not to grasp both the exponential impact of redundancy on reliability, and that fault tree analysis could confidently calculate low system failure rates based on historical failure rates of large populations of constituent components, combined with the output of FMEAs (failure mode effects analyses) on those components in the relevant systems. This error does not impact Feynman’s conclusions about the root cause of the Challenger disaster. I mention it here because Feynman might be viewed as an authoritative source on systems engineering, but is here doing a poor job of systems engineering.
Discussing the liquid fuel engines, Feynman then introduces the concept of top-down design, which he criticizes. It isn’t clear exactly what he means by top-down. The most charitable reading would be a critique of NASA top management’s overruling the judgments of engineering management and engineers; but, on closer reading, it’s clear this cannot be his meaning:
The usual way that such engines are designed (for military or civilian aircraft) may be called the component system, or bottom-up design. First it is necessary to thoroughly understand the properties and limitations of the materials to be used (for turbine blades, for example), and tests are begun in experimental rigs to determine those. With this knowledge larger component parts (such as bearings) are designed and tested individually…
The Space Shuttle Main Engine was handled in a different manner, top down, we might say. The engine was designed and put together all at once with relatively little detailed preliminary study of the material and components. Then when troubles are found in the bearings, turbine blades, coolant pipes, etc., it is more expensive and difficult to discover the causes and make changes.
All mechanical-system design is necessarily top-down, in the sense of top-down used by Eisner, above. This use of the term is metaphor for progressive functional decomposition from mission requirements down to component requirements. Engineers cannot, for example, size a shuttle’s fuel pumps based on the functional requirement of having five men and two women orbit the earth to deploy a communications satellite. The fuel pump’s performance requirements ultimately emerge from successive derivations of requirements for subsystem design candidates. This design process is top-down, whether the various layers of subsystem design candidates are themselves newly conceived systems or ones that are already mature products (“off the shelf”). Wikipedia’s article and several software methodology sites incorrectly refer to design using off-the-shelf components as bottom-up – not involving functional decomposition. They err by failing to consider that piecing together existing subsystems toward a grander purpose still first requires functional decomposition of that grander purpose into lower-level requirements that serve as a basis for selecting existing subsystems. Simply put, you’ve got to know what you want a thing to do, even if you build that thing from available parts – software or hardware – in order to select those parts. Using off-the-shelf software subsystems still requires functional decomposition of the desired grander system.
F-117 frontal view
Off-the-shelf is a common strategy in aerospace, primarily for cost and schedule reasons. The Lockheed F-117, despite its unique design, used avionics taken from the C-130 and the F-16, brakes from the F-15, landing gear from the T-38, and other parts from commercial and military aircraft. This was for expediency. For the F-117, these off-the-shelf components still had to go through the necessary requirements validation, functional and stress testing, certification, and approval by all of the “ilities” (reliability, maintainability, supportability, durability, etc) required to justify their use in the vehicle – just as if they were newly designed. Likewise for the Challenger, the choice of new design vs. off-the-shelf should have had no impact on safety or reliability if proper systems engineering occurred. Whether its constituents were new designs or off-the-shelf, the shuttle’s propulsion system is necessarily – and desirably – the result of top-down design. Feynman may simply mean that the design and testing phases were rushed, that omissions were made, and that testing was incomplete. Other evidence suggests this; but these omissions are not a negative consequence of top-down design, which is the only sound process for the design of aircraft and other systems of systems.
It is difficult to imagine any sound basis for Feynman’s use of – and defense of – bottom-up design other than the selection of off-the-shelf components, which, as mentioned above, still entails functional decomposition (top-down design). Other uses of the term appear in discussions of software methodologies. I also found a handful of academic papers that incorrectly – incoherently, in my view – equate top-down with analysis and deduction, and bottom-up with synthesis and induction. The erroneous equation of analysis with deductive reasoning pops up in Design Thinking and social science literature (e.g., at socialresearchmethods.net). It fails to realize that analysis as a means of inferring cause from observed result (i.e., what made this happen?) always entails inductive reasoning. Geometry is deduction; science and engineering are inherently inductive.
The use of bottom-up shows up in software circles in a disparaging sense. It describes a state of system growth that happens with no conscious design beyond that of an original seed. It is non-design, in a sense. Such “organic growth” happens in enterprise software when new features, not envisioned during the original design, are later bolted-on. This can stem from naïve mismanagement by those unaware of the damage done to maintainability and further extensibility of the software system, or through necessity in a merger/acquisition scenario where the system’s owners are aware of the consequences but have no other alternatives. This scenario obviously does not apply to the hardware or software of the Challenger; and if it did, such bottom-up “design” would be a defect of the system, not a virtue.
 Hydro-mechanical system components in 737 gear bay |
Aerospace has in its legacy an attitude – as opposed to a design method – sometimes called a bottom-up mindset. I’ve encountered this as a form of resistance to methodological system-design-for-safety and the application of redundancy. In my experience it came from expert designers of electro-hydro-mechanical subsystems. A legendary aerospace systems designer once told me with a straight face, “I don’t believe in probability.” You can trace this type of thinking back to the rough and ready pioneers of manned flight. Charles Lindbergh, for example, said something along the lines of, “give me one good engine and one good pilot.” Implicit in this mentality is the notion that safety emerges from component quality rather than from system design. The failure rates of the best aerospace components tend to vary from those of average components by factors of two or ten, whereas redundancy has an exponential effect. Feynman’s criticism of top-down and endorsement of bottom-up – whatever he meant by it – could unfortunately be seen as support for this harmful and oddly persistent notion of bottom-up.
Toward the end of Feynman’s report, he reveals another misunderstanding about design of life-critical systems. In the section on avionics, he faults NASA for using 15-year-old software and hardware designs, concluding that the electronics are obsolete. He claims that modern chip sets are more reliable and of higher quality. This criticism runs contrary to his complaint about top-down design of the main engines, and it misses a key point. The improvements in reliability of newer chips would contribute only negligibly toward improved availability of the quad-redundant system containing them. More importantly, older designs of electronic components are often used in avionics precisely because they are old, mature designs. Accelerated-life testing of electronics is known to be tricky business. We use old-design chips because there is enough historical usage data to determine their failure rates without relying on accelerated-life testing. Long ago at McDonnell Douglas I oversaw use of the Intel 87C196 chip for a system on the C-17 aircraft. The Intel rep told me that this was the first use of the Intel 8086-derivative chip in a military aircraft. We defended its use, over the traditional but less capable Motorola chips, on the basis that the then 10+ year history of 8086’s in similar environments was finally sufficient to establish a statistical failure rate usable in our system availability calculations. Interestingly, at that time NASA had already been using 8086 chips in the shuttle for years.
Feynman’s minority report on the Challenger contains misunderstandings and technical errors from the perspective of a systems engineer. While these errors may have little impact on his findings, they should be called out because of the possible influence they may have on future generations of engineers. The tyranny of pedigree, as we saw with Galileo, can extend a wrong idea’s life for generations.
That said, Feynman makes several key points about the psychology of engineering management that deserve much more attention than they get in engineering circles. First among these in my mind is the fallacy of induction from near-misses viewed as successes, thereby producing undue confidence about future missions.
“His legs were weary, but his mind was at ease, free from the presentiment of change. The sense of security more frequently springs from habit than from conviction, and for this reason it often subsists after such a change in the conditions as might have been expected to suggest alarm. The lapse of time during which a given event has not happened is, in the logic of habit, constantly alleged as a reason why the event should never happen, even when the lapse of time is precisely the added condition which makes the event imminent. A man will tell you that he has worked in a mine for forty years unhurt by an accident, as a reason why he should apprehend no danger, though the roof is beginning to sink; and it is often observable that the older a man gets, the more difficult it is to retain a believing conception of his own death.”
– from Silas Marner, by George Eliot (Mary Ann Evans Cross), 1861
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Text and aircraft photos copyright 2013 by William Storage. NASA shuttle photos public domain.
You’re So Wrong, Richard Feynman
Posted in Innovation management, Multidisciplinarians on November 2, 2013
“Philosophy of science is about as useful to scientists as ornithology is to birds”
This post is more thoughts on the minds of interesting folk who can think from a variety of perspectives, inspired by Bruce Vojak’s Epistemology of Innovation articles. This is loosely related to systems thinking, design thinking, or – more from my perspective – the consequence of learning a few seemingly unrelated disciplines that end up being related in some surprising and useful way.
Richard Feynman ranks high on my hero list. When I was a teenager I heard a segment of an interview with him where he talked about being a young boy with a ball in a wagon. He noticed that when he abruptly pulled the wagon forward, the ball moved to the back of the wagon, and when he stopped the wagon, the ball moved forward. He asked his dad why it did that. His dad, who was a uniform salesman, put a slightly finer point on the matter. He explained that the ball didn’t really move backward; it moved forward, just not as fast as the wagon was moving. Feynman’s dad told young Richard that no one knows why a ball behaves like that. But we call it inertia. I found both points wonderfully illuminating. On the ball’s motion, there’s more than one way of looking at things. Mel Feynman’s explanation of the ball’s motion had gentle but beautiful precision, calling up thoughts about relativity in the simplest sense – motion relative to the wagon versus relative to the ground. And his statement, “we call it inertia,” got me thinking quite a lot about the difference between knowledge about a thing and the name of a thing. It also recalls Newton vs. the Cartesians in my recent post. The name of a thing holds no knowledge at all.
Feynman was almost everything a hero should be – nothing like the stereotypical nerd scientist. He cussed, pulled gags, picked locks, played drums, and hung out in bars. His thoughts on philosophy of science come to mind because of some of the philosophy-of-science issues I touched on in previous posts on Newton and Galileo. Unlike Newton, Feynman was famously hostile to philosophy of science. The ornithology quote above is attributed to him, though no one seems to have a source for it. If not his, it could be. He regularly attacked philosophy of science in equally harsh tones. “Philosophers are always on the outside making stupid remarks,“ he is quoted as saying in his biography by James Gleick.
My initial thoughts were that I can admire Feynman’s amazing work and curious mind while thinking he was terribly misinformed and hypocritical about philosophy. I’ll offer a slightly different opinion at the end of this. Feynman actually engaged in philosophy quite often. You’d think he’d at least try do a good job of it. Instead he seems pretty reckless. I’ll give some examples.
Feynman, along with the rest of science, was assaulted by the wave of postmodernism that swept university circles in the ’60s. On its front line were Vietnam protesters who thought science was a tool of evil corporations, feminists who thought science was a male power play, and Foucault-inspired “intellectuals” who denied that science had any special epistemic status. Feynman dismissed all this as a lot of baloney. Most of it was, of course. But some postmodern criticism of science was a reaction – though a gross overreaction – to a genuine issue that Kuhn elucidated – one that had been around since Socrates debated the sophists. Here’s my best Readers Digest version.
All empirical science relies on affirming the consequent, something seen as a flaw in deductive reasoning. Science is inductive, and there is no deductive justification for induction (nor is there any inductive justification for induction – a topic way too deep for a blog post). Justification actually rests on a leap of inductive faith and consensus among peers. But it certainly seems reasonable for scientists to make claims of causation using what philosophers call inference to the best explanation. It certainly seems that way to me. However, defending that reasoning – that absolute foundation for science – is a matter of philosophy, not one of science.
This issue edges us toward a much more practical one, something Feynman dealt with often. What’s the difference between science and pseudoscience (the demarcation question)? Feynman had a lot of room for Darwin but no room at all for the likes of Freud or Marx. All claimed to be scientists. All had theories. Further, all had theories that explained observations. Freud and Marx’s theories actually had more predictive success than did those of Darwin. So how can we (or Feynman) call Darwin a scientist but Freud and Marx pseudoscientists without resorting to the epistemologically unsatisfying argument made famous by Supreme Court Justice Potter Stewart: “I can’t define pornography but I know it when I see it”? Neither Feynman nor anyone else can solve the demarcation issue in any convincing way, merely by using science. Science doesn’t work for that task.
It took Karl Popper, a philosopher, to come up with the counterintuitive notion that neither predictive success nor confirming observations can qualify something as science. In Popper’s view, falsifiability is the sole criterion for demarcation. For reasons that take a good philosopher to lay out, Popper can be shown to give this criterion a bit too much weight, but it has real merit. When Einstein predicted that the light from distant stars actually bends around the sun, he made a bold and solidly falsifiable claim. He staked his whole relativity claim on it. If, in an experiment during the next solar eclipse, light from stars behind the sun didn’t curve around it, he’d admit defeat. Current knowledge of physics could not support Einstein’s prediction. But they did they experiment (the Eddington expedition) and Einstein was right. In Popper’s view, this didn’t prove that Einstein’s gravitation theory was true, but it failed to prove it wrong. And because the theory was so bold and counterintuitive, it got special status. We’ll assume it true until it is proved wrong.
Marx and Freud failed this test. While they made a lot of correct predictions, they also made a lot of wrong ones. Predictions are cheap. That is, Marx and Freud could explain too many results (e.g., aggressive personality, shy personality or comedian) with the same cause (e.g., abusive mother). Worse, they were quick to tweak their theories in the face of counterevidence, resulting in their theories being immune to possible falsification. Thus Popper demoted them to pseudoscience. Feynman cites the falsification criterion often. He never names Popper.
The demarcation question has great practical importance. Should creationism be taught in public schools? Should Karmic reading be covered by your medical insurance? Should the American Parapsychological Association be admitted to the American Association for the Advancement of Science (it was in 1969)? Should cold fusion research be funded? Feynman cared deeply about such things. Science can’t decide these issues. That takes philosophy of science, something Feynman thought was useless. He was so wrong.
Finally, perhaps most importantly, there’s the matter of what activity Feynman was actually engaged in. Is quantum electrodynamics a science or is it philosophy? Why should we believe in gluons and quarks more than angels? Many of the particles and concepts of Feynman’s science are neither observable nor falsifiable. Feynman opines that there will never be any practical use for knowledge of quarks, so he can’t appeal to utility as a basis for the scientific status of quarks. So shouldn’t quantum electrodynamics (at least with level of observability it had when Feynman gave this opinion) be classified as metaphysics, i.e., philosophy, rather than science? By Feynman’s demarcation criteria, his work should be called philosophy. I think his work actually is science, but the basis for that subtle distinction is in philosophy of science, not science itself.
While degrading philosophy, Feynman practices quite a bit of it, perhaps unconsciously, often badly. Not Dawkins-bad, but still pretty bad. His 1966 speech to the National Science Teacher’s Association entitled “What Is Science?” is a case in point. He hints at the issue of whether science is explanatory or merely descriptive, but wanders rather aimlessly. I was ready to offer that he was a great scientist and a bad accidental philosopher when I stumbled on a talk where Feynman shows a different side, his 1956 address to the Engineering and Science college at the California Institute of Technology, entitled, “The Relation of Science and Religion.”
He opens with an appeal to the multidisciplinarian:
“In this age of specialization men who thoroughly know one field are often incompetent to discuss another. The great problems of the relations between one and another aspect of human activity have for this reason been discussed less and less in public. When we look at the past great debates on these subjects we feel jealous of those times, for we should have liked the excitement of such argument.”
Feynman explores the topic through epistemology, metaphysics, and ethics. He talks about degrees of belief and claims of certainty, and the difference between Christian ethics and Christian dogma. He handles all this delicately and compassionately, with charity and grace. He might have delivered this address with more force and efficiency, had he cited Nietzsche, Hume, and Tillich, whom he seems to unknowingly parallel at times. But this talk was a whole different Feynman. It seems that when formally called on to do philosophy, Feynman could indeed do a respectable job of it.
I think Richard Feynman, great man that he was, could have benefited from Philosophy of Science 101; and I think all scientists and engineers could. In my engineering schooling, I took five courses in calculus, one in linear algebra, one non-Euclidean geometry, and two in differential equations. Substituting a philosophy class for one of those Dif EQ courses would make better engineers. A philosophy class of the quantum electrodynamics variety might suffice.
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“It is a great adventure to contemplate the universe beyond man, to think of what it means without man – as it was for the great part of its long history, and as it is in the great majority of places. When this objective view is finally attained, and the mystery and majesty of matter are appreciated, to then turn the objective eye back on man viewed as matter, to see life as part of the universal mystery of greatest depth, is to sense an experience which is rarely described. It usually ends in laughter, delight in the futility of trying to understand.” – Richard Feynman, The Relation of Science and Religion
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Just a Moment, Galileo
Posted in Engineering & Applied Physics, Innovation management on October 29, 2013
Bruce Vojak’s wonderful piece on innovation and the minds of Newton and Goethe got me thinking about another 17th century innovator. Like Newton, Galileo was a superstar in his day – a status he still holds. He was the consummate innovator and iconoclast. I want to take a quick look at two of Galileo’s errors, one technical and one ethical, not to try to knock the great man down a peg, but to see what lessons they can bring to the innovation, engineering and business of this era.
Less well known than his work with telescopes and astronomy was Galileo’s work in mechanics of solids. He seems to have been the first to explicitly identify that the tensile strength of a beam is proportional to its cross-sectional area, but his theory of bending stress was way off the mark. He applied similar logic to cantilever beam loading, getting very incorrect results. Galileo’s bending stress illustration is shown below (you can skip over the physics details, but they’re not all that heavy).
For bending, Galileo concluded that the whole cross section was subjected to tension at the time of failure. He judged that point B in the diagram at right served as a hinge point, and that everything above it along the line A-B was uniformly in horizontal tension. Thus he missed what would be elementary to any mechanical engineering sophomore; this view of the situation’s physics results in an unresolved moment (tendency to twist, in engineer-speak). Since the cantilever is at rest and not spinning, we know that this model of reality cannot be right. In Galileo’s defense, Newton’s 3rd law (equal and opposite reaction) had not yet been formulated; Newton was born a year after Galileo died. But Newton’s law was an assumption derived from common sense, not from testing.
It took more than a hundred years (see Bernoulli and Euler) to finally get the full model of beam bending right. But laboratory testing in Galileo’s day could have shown his theory of bending stress to make grossly conservative predictions. And long before Bernuolli and Euler, Edme Mariotte published an article in which he got the bending stress distribution mostly right, identifying that the neutral axis should be down the center of the beam, from top to bottom. A few decades later Antoine Parent polished up Mariotte’s work, arriving at a modern conception of bending stress.
But Mariotte and Parent weren’t superstars. Manuals of structural design continued to publish Galileo’s equation, and trusting builders continued to use them. Beams broke and people died. Deference to Galileo’s authority, universally across his domain of study, not only led to needless deaths but also to the endless but fruitless pursuit of other causes for reality’s disagreement with theory.
So the problem with Galileo’s error in beam bending was not so much the fact that he made this error, but the fact that for a century it was missed largely for social reasons. The second fault I find with Galileo’s method is intimately tied to his large ego, but that too has a social component. This fault is evident in Galileo’s writing of Dialogue on the Two Chief World Systems, the book that got him condemned for heresy.
Galileo did not invent the sun-centered model of our solar system; Copernicus did. Galileo pointed his telescope to the sky, discovered four moons of Jupiter, and named them after influential members of the Medici family, landing himself a job as the world’s highest paid scholar. No problem there; we all need to make a living. He then published Dialogue arguing for Copernican heliocentrism against the earth-centered Ptolemaic model favored by the church. That is, Galileo for the first time claimed that Copernicanism was not only an accurate predictive model, but was true. This was tough for 17th century Italians to swallow, not only their clergy.
For heliocentrism to be true, the earth would have to spin around at about 1000 miles per hour on its surface. Galileo had no good answer for why we don’t all fly off into space. He couldn’t explain why birds aren’t shredded by supersonic winds. He was at a loss to provide rationale for why balls dropped from towers appeared to fall vertically instead of at an angle, as would seem natural if the earth were spinning. And finally, if the earth is in a very different place in June than in December, why do the stars remain in the same pattern year round (why no parallax)? As UC Berkeley philosopher of science Paul Feyerabend so provocatively stated, “The church at the time of Galileo was much more faithful to reason than Galileo himself.”
At that time, Tycho Brahe’s modified geocentric theory of the planetary system (Mercury and Venus go around the sun, which goes around the earth), may have been a better bet given the evidence. Brahe’s theory is empirically indistinguishable from Copernicus’s. Venus goes through phases, like the moon, in Brahe’s model just as it does in Copernicus’s. No experiment or observation of Galileo could refute Brahe.
Here’s the rub. Galileo never mentions Brahe’s model once in Dialogue on the Two Chief World Systems. Galileo knew about Brahe. His title, Two Systems, seems simply a polemic device – at best a rhetorical ploy to eliminate his most worthy opponent by sleight of hand. He’d rather fight Ptolemy than Brahe.
Likewise, Galileo ignored Johannes Kepler in Dialogue. Kepler’s work (Astronomia Nova) was long established at the time Galileo wrote Dialogue. Kepler correctly identified that the planetary orbits were elliptical rather than circular, as Galileo thought. Kepler also modeled the tides correctly where Galileo got them wrong. Kepler wrote congratulatory letters to Galileo; Galileo’s responses were more reserved.
Galileo was probably a better man (or should have been) than his behavior toward Kepler and Brahe reveal. His fans fed his ego liberally, and he got carried away. Galileo, Brahe, Kepler and everyone else would have been better served by less aggrandizing and more humility. The tech press and the venture capital worlds that fuel what Vivek Wadhwa calls the myth of the 20-year old white male genius CEO should take note.
Epistemology of Innovation
Posted in Design Thinking, Innovation management, Philosophy of Science on October 27, 2013
I recently ran across an outstanding blog and series of articles by Bruce A. Vojak, Associate Dean for Administration and an Adjunct Professor in the College of Engineering at the University of Illinois. Vojak deals with the epistemology of innovation. Epistemology is mostly an academic term, not yet usurped by Silicon Valley spin doctors, which basically means the study of knowledge and its justification – in other words, what we know, how we know it, and how we know we know it. So it follows that Vojak’s intent is to challenge readers to reflect on the practice of innovation and on how practitioners come to know what to do today in order to innovate successfully.
Incidentally, Vojak uses the popular term, “breakthrough innovation” – as we all do. I’ve been somewhat skeptical that this term can really carry much epistemic weight. It is popular among innovation advocates, but I’m not sure it has any theoretical – thus predictive – value. Even Judy Estrin, a Silicon Valley visionary for whom I have great respect, differentiates breakthrough from other innovation only in terms of historical marketplace success. Thus it seems to me that breakthrough can only be applied to an innovation in retrospect. In this sense it may be rare that prospective innovators can know whether they are pursuing continuous innovation or the breakthrough variety. Why set your sights low? In any case, Vojak is much more knowledgeable on the topic than I, and I’ll enjoy seeing where he goes with the breakthrough distinction that he develops somewhat in his So, what’s the big idea?. Vojak offers that breakthrough innovators are systems thinkers.
The articles by Vojak that I’m most thrilled with, contrasting the minds of contemporary innovators, are entitled “Patriarchs of Contemporary Innovation.” He’s released two of these this month: Newton & Goethe and Socrates & Hegel. I love these for many reasons including good subjects, concisely covered, flowing logically in a non-academic tone; but especially because they assign a very broad scope to innovation, contrasting the tunnel vision of the tech press.
In Newton & Goethe, Vojak looks at what can be learned from contrasting the two contemporary (with each other) thinkers. The objective Newton used a mathematical description of color, saw color as external to humans, reduced color into components (his famous prism experiment), and was a detached and dispassionate observer of it – the classic empiricist. For the subjective Goethe, color is something that humans do (it’s in our perception). Goethe was attached to color’s beauty; color is an experiential matter. In this sense, Newton is an analyst and Goethe is a design-thinker. Vojak then proposes that one role of an innovator is be able to hold both perspectives and to know when each is appropriate. Contrast this mature perspective with the magic-creative-powers BS peddled by Silicon Valley’s hockers of Design Thinking.
Because of my interest in history of science/philosophy of science, one aspect of Newton & Goethe got me thinking along a bit of tangent, but I think a rather interesting one. Vojak contrasts the romanticism and metaphysics of Goethe with the naturalism and empiricism of Newton, the “mastery of them that know.” But even Newton’s empiricism went only so far. Despite his having revealed what he called “true causes” and “universal truths,” his responses to his peers on what gravity actually was suggest that he never sought justification (in the epistemological sense) for his theories. “Gravity is the finger of God,” said Newton.
Newton was not a scientist, and we should avoid calling him that for reasons beyond the fact that the term did not exist in his day. He was a natural philosopher. When his rival continental natural philosophers – the disciples of Descartes – demanded explanation for force at a distance (how gravity pulls with no rope), Newton replied something along the lines of that gravity means what the equation says. For Newton there was no need to correlate experience with something behind the experience. This attitude seems natural today, with our post-Einstein, post-quantum-mechanics perspective, but certainly was rightly seen by the emerging naturalists of Newton’s day as a theological-holdout basis for denying any interest in understanding reality.
In my view, history shortchanges us a bit by not bothering to mention that only 20% of Newton’s writings were in math and physics, the rest being theology and various forms of spooky knowledge. As presented in modern textbooks, Newton doesn’t seem like the type who would spend years seeking divine secrets revealed in the proportions of biblical structures, yet he did. Newton helped himself to Design Thinking at times.
None of this opposes any of Vojak’s contrast of Newton and Goethe; I just find it fascinating that even in Newton’s day, there was quite a bit of thinking on the opposite side of Newton from Goethe.
I highly recommend Vojak’s very accessible blog and articles on the illinois.edu site to anyone seeking some fresh air on the topic of innovation.
Intuitive Probabilities – Conjunction Malfunction
Posted in Aerospace, Probability and Risk, Systems Engineering on October 15, 2013
In a recent post I wrote about Vic, who might not look like a Christian, but probably is one. The Vic example reminded me of a famous study of unintuitive probabilities done in 1983. Amos Tversky and Daniel Kahneman surveyed students at the University of British Columbia using something similar to my Vic puzzle:
Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.
Which is more probable?
A. Linda is a bank teller.
B. Linda is a bank teller and is active in the feminist movement.
About 90% of students said (B) was more probable. Mathematicians point out that, without needing to know anything about Linda, (A) has to be more probable than (B). Thinking otherwise is the conjunction fallacy. It’s simple arithmetic. The probability of a conjunction, P(A&B), cannot exceed the probabilities of its constituents, P(A) and P(B), because the extension (possibility set) of the conjunction is included in the extension of its constituents. In a coin toss, the probability of heads has to exceed the probability of heads AND that it will rain today.
Putting numbers to Linda, one might guess there’s 1% probability that Linda, based on the description given, is a bank teller, but a 99% probability that she’s a feminist. Even so, 1% is still a bigger number (probability) than 1% AND 99%, which means 1% times 99% – which is a tad less than 1%.
So why does it seem like (B) is more likely? Lots of psychological and semantic reasons have been proposed. For example, in normal communications, we usually obey some unspoken principle of relevance; a sane person would not mention Linda’s marital status, political views and values if they were irrelevant to the question at hand – which somehow seems to have something to do with Linda’s profession. Further, humans learn pattern recognition and apply heuristics. It may be a fair bit of inductive reasoning based on past evidence that women active in the feminist movement are more likely than those who are not to major in philosophy, be single, and be concerned with discrimination. This may be a reasonable inference, or it may just prove you’re a sexist pig for even thinking such a thing. I attended a lecture at UC Berkeley where I was told that any statement by men that connects attributes (physical, ideological or otherwise) to any group (except white men) constituted sexism, racism or some otherism. This made me wonder how feminists are able to recognize other feminists.
In any case, there are reasons that student would not give the mathematically correct answer about Linda beyond the possibility that they are mathematically illiterate. Tversky and Kahneman tried various wordings of the problem, pretty much getting the same results. At some point they came up with this statement of the problem that seems to drive home the point that they were seeking a mathematical interpretation of the problem:
Argument 1: Linda is more likely to be a bank teller than she is to be a feminist bank teller, because every feminist bank teller is a bank teller, but some bank tellers are not feminists, and Linda could be one of them.
Argument 2: Linda is more likely to be a feminists bank teller than she is likely to be a bank teller, because she resembles an active feminist more than she resembles a bank teller.
In this case 65% of students chose the extension argument (2), despite its internal logical flaw. Note that argument 1 explains why the conjunction fallacy is invalid and that argument 2 doesn’t really make much sense.
Whatever the reason we tend to botch such probability challenges, there are cases in engineering that are surprisingly analogous to the Linda problem. For example, when building a fault tree (see fig. 1), your heuristics can make you miss event dependencies and common causes between related failures. For example, if an aircraft hydraulic brake system accumulator fails by exploding instead of by leaking, and in doing so severs a hydraulic line, an “AND” relationship disappears so that what appeared to be P(A&B) becomes simply P(A). Such logic errors can make calculations of probability of catastrophe off by factors of thousands or millions. This is bad, when lives are at stake. Fortunately, engineers apply great skill and discipline to modeling this sort of thing. We who fly owe our lives to good engineers. Linda probably does too.
Fig. 1. Segment of a fault tree for loss of braking in a hypothetical 8-wheeled aircraft using FTA software I authored in 1997. This fault tree addresses only a single Class IV hazard in aircraft braking – uncontrolled departure from the end of the runway due to loss of braking during a rejected takeoff. It calculates the probability of this “top event” as being more remote than the one-per-billion flight hours probability limit specified by the guidelines of FAA Advisory Circular 25.1309-1A, 14CFR/CS 25.1309, and SAE ARP4754. This fault tree, when simplified by standard techniques, results in about 200,000 unique cut sets – combinations of basic events leading to the catastrophic condition.
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Uncertainty is an unavoidable aspect of the human condition- Opening sentence of “Extensional Versus Intuitive Reasoning” by Tversky and Kahneman, Oct. 1983 Psychological Review.
Intuitive Probabilities
Posted in Probability and Risk on October 1, 2013
Meet Vic. Vic enjoys a form of music that features heavily distorted guitars, slow growling vocals, atonality, frequent tempo changes, and what is called “blast beat” drumming in the music business. His favorite death metal bands are Slayer, Leviticus, Dark Tranquility, Arch Enemy, Behemoth, Kreator, Venom, and Necrophagist.
Vic has strong views on theology and cosmology. Which is more likely?
- Vic is a Christian
- Vic is a Satanist
I’ve taught courses on probabilistic risk analysis over the years, and have found that very intelligent engineers, much more experienced than I, often find probability extremely unintuitive. Especially when very large (or very small) numbers are involved. Other aspects of probability and statistics are unintuitive for other interesting reasons. More on those later.
The matter of Vic’s belief system involves several possible biases and unintuitive aspects of statistics. While pondering the issue of Vic’s beliefs, you can enjoy Slayer’s Raining Blood. Then check out my take on judging Vic’s beliefs below the embedded YouTube video – which, by the way, demonstrates all of the attributes of death metal listed above.
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Vic is almost certainly a Christian. Any other conclusion would involve the so-called base-rate fallacy, where the secondary, specific facts (affinity for death metal) somehow obscure the primary, base-rate relative frequency of Christians versus Satanists. The Vatican claims over one billion Catholics, and most US Christians are not Catholic. Even with papal exaggeration, we can guess that there are well over a billion Christians on earth. I know hundreds if not thousands of them. I don’t know any Satanists personally, and don’t know of any public figures who are (there is conflicting evidence on Marilyn Manson). A quick Google search suggests a range of numbers of Satanists in the world, the largest of which is under 100,000. Further, I don’t ever remember seeing a single Satanist meeting facility, even in San Francisco. A web search also reveals a good number of conspicuously Christian death metal bands, including Leviticus, named above. Without getting into the details of Bayes Theorem, it is probably obvious that the relative frequencies of Christians against Satanists governs the outcome. And judging Vic by his appearance is likely very unreliable.
South Park Community Presbyterian Church
Fairplay, Colorado
Moral Truths and True Beliefs
Posted in Uncategorized on August 23, 2013
Suppose I’m about to flip a coin. Somehow you’re just certain it will be heads; you strongly believe so. I flip and you’e right. Say you’re right five times in a row. Can you claim rightness in any meaningful way, or did you merely hold a true belief on invalid grounds? What if you held a strong belief about a complex social issue with no personal knowledge of its details, but followed your community’s lead?
Were Democritus and Lucretius right in any meaningful way when they told the ancient Greeks and Romans that all matter was made up of “atoms” held together by forces, or did they merely hold true but unwarranted beliefs? Does George Berkeley deserve credit for getting quantum mechanics right in the 18th century?
It is moral truth that slavery is wrong and that women should not be subjugated, though this was once obvious to very few. Jesus, at least as he appears in the New Testament, misses every opportunity to condemn slavery. He tells us only not to beat them overly hard. And he tells slaves to obey their masters. Women fare only slightly better. Sometime between then and now the moral truth about women’s rights and slavery has been revealed. Has the moral truth about nuclear power been yet revealed? Solar power? GMO foods?
Last weekend while biking in the Marin Headlands I happened upon a group of unusual tourists. An old man with a long white beard wore high-wasted wool pants and a plain flannel shirt. His wife was in plain garb, clearly separating her from modern society, just as intended by Jakob Ammann, the tailor who inspired it. A younger man also wore a long beard, high wool pants and a plain shirt. I asked him if they were visiting and he said yes, from Ohio. I thought so, I said. He told me they were from Holmes County, naming a tiny town I knew from having grown up in Ohio. They were Amish, on tour in San Francisco.
We talked about the bay area’s curious summer weather, the Golden Gate Bridge and so on, I wished them a nice visit and rode out to Conzulman Road, where I stopped to add a jacket for the cold ride downhill. Two spandex clad local riders did the same. I overheard their snide condemnation of the “Mennonite” (they were Amish) religious zealots and their backward attitudes toward women and cosmology. The more I pondered this, the more it irked me. I think the I can explain why. With no more risk of unwarranted inference than that of my fellow San Franciscans about the Amish visitors, I can observe this about these socially-just bikers.
Get off your morally superior San Francisco high horses. The Amish visitors are far less wedded to dogma than you are. They have consciously broken with their clan and its rigid traditions in several visible ways; while you march straight down the party line. If your beliefs are less destructive to the environment, your cosmology more consistent with scientific evidence, and your position on women’s rights more enlightened than theirs, it is merely because of geography. You are fortunate that your community of influences have made more moral progress than theirs have. As it should be. Your community of influencers is larger and more educated. You can take no credit for your proximity to a better set of influencers. You hold your beliefs on purely social grounds, just like they do. But they examined their dogma and boldly took a few steps away from it – a mega-Rumspringa into a place that invites fellowship with lawlessness[1], where separation from the desires of the modern world[2] is not an option.
[1] Do not be unequally yoked together with unbelievers. For what fellowship has righteousness with lawlessness? And what communion has light with darkness? – 2 Corinthians 6:14
[2] And do not be conformed to this world, but be transformed by the renewing of your mind, that you may prove what is that good and acceptable and perfect will of God. – Romans 12:2
Debbie Downer Doesn’t Do Design
Posted in Engineering & Applied Physics, Innovation management on November 23, 2012
Are you a real engineer – you know, the kind who actually knows the underlying mechanics of how the natural world works? Have you ever been evicted from an innovation workshop by some smug hipster with an art degree who your firm engaged to teach you how to think creatively? Has a self-proclaimed design guru called you a Debbie Downer because you categorically reject all spacecraft designs that include the note, “Insert warp drive here”?
Do you wear the 3rd Law of Thermodynamics on your sleeve? Do you recoil at greentech entrepreneurs who convince investors and politicians that with innovative design, photovoltaic conversion efficiency can breach the Shockley-Queisser limit or even the Carnot limit?
Do many TED talks make you want to hurl? You know the ones. Take the second most popular TED talk of all time, where Dr. Jill Bolte Taylor relates her “stroke of insight” – an incredible tale of the transcendent peace she experienced after a complete lateral stroke. She drags out the ever-popular (false – but don’t be a Negative Nancy) left-brain/right-brain stuff as an explanation for her mystical experiences. The high-rolling TED audience swoons. Taylor then dredges up an old TED staple, stating, “the left hemisphere is linear thinking.”
Ah – linear thinking. One TED speaker shows a graph of a straight line and another of an exponential curve. He explains the magic nature of exponentiality to the spellbound audience who has apparently forgotten their high school math class on compound interest and then releases his pearl: imagine how productive we can be if we employ nonlinear thinking instead of the linear variety. He equivocates discontinuous non-linearity with exponential nonlinearity and not a soul notices. Critical thinking is for left-brained losers.
Do you groan when Jane McGonigal declares an epic win with her assertion that behaviors learned in World of Warcraft can translate into solutions to real problems if we just swallow the right dose of newthink? McGonigal reports that humans have spent 5.93 million years playing World of Warcraft. She means, of course, 5.93 million man-years (or Doritos-stained-fingers, pear-shaped-kid-years). She adds that 5.93 million years ago is when primates became bipedal (TED video at 6:05). She then addresses the evolutionary value of video games, noting that we’ve played WoW as long as we’ve walked on our hind legs. I’m not making this up. “This is true; I believe this,” as McGonigal likes to say.
If you’ve ever wanted to choke a perpetual-motion hocking idea-man, The Onion has an antidote:
The Multidisciplinarian 