Positive Risk – A Positive Disaster

Positive risk is an ill-conceived concept in risk management that makes a mess of things. It’s sometimes understood to be the benefit or reward, imagined before taking some action, for which the risky action was taken, and other times understood to mean a non-zero chance of an unexpected beneficial consequence of taking a chance. Many practitioners mix the two meanings without seeming to grasp the difference. For example, in Fundamentals of Enterprise Risk Management John J Hampton defends the idea of positive risk: “A lost opportunity is just as much a financial loss as is damage to people and property.”  Hampton then relates the story of US Airways flight 1549, which made a successful emergency water landing on the Hudson River in 2009. Noting the success of the care team in accommodating passengers, Hampton describes the upside to this risk: “US Airways received millions of dollars of free publicity and its reputation soared.” Putting aside the perversity of viewing damage containment as an upside of risk, any benefit to US Airways from the happy outcome of successfully ditching a plane in a river seems poor grounds for intentionally increasing the likelihood of repeating the incident because of “positive risk.”

While it’s been around for a century, the concept of positive risk has become popular only in the last few decades. Its popularity likely stems from enterprise risk management (ERM) frameworks that rely on Frank Knight’s (“Risk, Uncertainty & Profit,” 1921) idiosyncratic definition of risk. Knight equated risk with what he called “measurable uncertainty” – what most of us call probability –  which he differentiated from “unmeasurable uncertainty,” which is what most of us call ignorance (not in the pejorative sense).

Knight wrote:

“To preserve the distinction which has been drawn in the last chapter between the measurable uncertainty and an unmeasurable one we may use the term “risk” to designate the former and the term “uncertainty” for the latter.”

Many ERM frameworks rely on Knight’s terminology, despite it being at odds with the risk language of insurance, science, medicine, and engineering – and everywhere else throughout modern history. Knight’s usage of terms conflicted with that of his more mathematically accomplished contemporaries including Ramsey, Kolmogorov, von Mises, and de Finetti. But for whatever reason, ERM frameworks embrace it. Under that conception of risk, one is forced to allow that positive risk exists to provide for positive (desirable) and negative undesirable) future outcomes of present uncertainty. To avoid confusion, the word, “positive,” in positive risk in ERM circles means desirable and beneficial, and not merely real or incontestable (as in positive proof).

The concepts that positive risk jumble and confound are handled in other risk-analysis domains with due clarity. Other domains acknowledge that risk is taken, when it is taken rather than being transferred or avoided, in order to gain some reward; i. e., a risk-reward calculus exists. Since no one would take risk unless some potential for reward existed (even if merely the reward of a thrill) the concept of positive risk is held as incoherent in risk-centric fields like aerospace and nuclear engineering. Positive risk confuses cause with effect, purpose with consequence, and uncertainty with opportunity; and it makes a mess of communications with serious professionals in other fields.

As evidence that only within ERM and related project-management risk tools is the concept of positive risk popular, note that the top 25 two-word strings starting with “risk” in Google’s data (e.g., aversion, mitigation, reduction, tolerance, premium, alert, exposure) all imply unwanted outcomes or expenses. Further, none of the top 10,000 collocates ending with “risk” include “positive” or similar words.

While the PMI and ISO 31000 and similar frameworks promote the idea of positive risk, most of the language within their publications does not accommodate risk being desirable. That is, if risk can be positive, the frameworks would not talk mostly of risk mitigation, risk tolerance, risk-avoidance, and risk reduction – yet they do. The conventional definition of risk appearing in dictionaries for the 200 years prior to the birth of ERM, used throughout science and engineering, holds that risk is a combination of the likelihood of an unwanted occurrence and its severity. Nothing in the common and historic definition of risk disallows that taking risks can have benefits or positive results – again, the reason we take risk is to get rewards. But that isn’t positive risk.

Dropping the concept of positive risk would prevent a lot of confusion, inconsistencies, and muddled thinking. It would also serve to demystify risk models built on a pretense of rigor and reeking of obscurantism, inconsistency, and deliberate vagueness masquerading as esoteric knowledge.

The few simple concepts mixed up in the idea of positive risk are easily extracted. Any particular risk is the chance of a specific unwanted outcome considered in combination with the undesirability (i.e. cost or severity) of that outcome. Chance means probability or a measure of uncertainty, whether computable or not; and rational agents take risks to get rewards. The concepts are simple, clear, and useful. They’ve served to reduce the rate of fatal crashes by many orders of magnitude in the era of passenger airline flight. ERM’s track record is less impressive. When I confront chieftans of ERM with this puzzle, they invariably respond, with confidence of questionable provenance, that what works in aviation can’t work in ERM.

ERM insiders maintain that risk-management disasters like AIG, Bear Stearns, Lehman Brothers, UBS, etc. stemmed from improper use of risk frameworks. The belief that ERM is a thoroughbred who’s had a recent string of bad jockeys is the stupidest possible interpretation of an endless stream of ERM failures, yet one that the authors of ISO 31000 and risk frameworks continue to deploy with straight faces. Those authors, who penned the bollixed “effect of uncertainty on objectives” definition of risk (ISO 31000 2009) threw a huge bone to big consultancies positioned to peddle such poppycock to unwary clients eager to curb operational risk.

The absurdity of this broader ecosystem has been covered by many fine writers, apparently to no avail. Mlodinow’s The Drunkard’s Walk, Rosenzweig’s The Halo Effect, and Taleb’s Fooled by Randomness are excellent sources. Douglas Hubbard spells out the madness of ERM’s shallow and quirky concepts of probability and positive risk in wonderful detail in both his The Failure of Risk Management and How to Measure Anything in Cybersecurity Risk. Hubbard points out the silliness of positive risk by noting that few people would take a risk if they could get the associated reward without exposure to the risk.

My greatest fear in this realm is that the consultants peddling this nonsense will infect aerospace, aviation and nuclear power as they have done in the pharmaceutical world, much of which now believes that an FMEA is risk management and that Functional Hazard Analysis is a form you complete at the beginning of a project.

The notion of positive risk is certainly not the only flaw in ERM models, but chucking this half-witted concept would be a good start.

 

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McKinsey’s Behavioral Science

You might not think of McKinsey as being in the behavioral science business; but McKinsey thinks of themselves that way. They claim success in solving public sector problems, improving customer relationships, and kick-starting stalled negotiations through their mastery of neuro- and behavioral science. McKinsey’s Jennifer May et. al. say their methodology is “built on an extensive review of neuroscience and behavioral literature from the past decade and is designed to distill the scientific insights most relevant for governments, not-for-profits, and business leaders.”

McKinsey is also active in the Change Management/Leadership Management realm, which usually involves organizational, occupational  and industrial psychology based on behavioral science. Like most science, all this work presumably involves a good deal of iterating over hypothesis and evidence collection, with hypotheses continually revised in light of interpretations of evidence made possible by sound use of statistics.

Given that, and McKinsey’s phenomenal success at securing consulting gigs with the world’s biggest firms, you’d think McKinsey would set out spotless epistemic values. A bit has been written about McKinsey’s ability to walk proud despite questionable ethics. In his 2013 book The Firm Duff McDonald relates McKinsey’s role in creating Enron and sanctioning its accounting practices, and its 2008 endorsement of banks funding their balance sheets with debt, and its promotion of securitizing sub-prime mortgages.

Epistemic and Scientific Values

I’m not talking about those kinds of values. I mean epistemic and scientific values. These are focused on how we acquire knowledge and what counts as data, fact, and information. They are concerned with accuracy, clarity, falsifiability, reliability, testability, and justification – all the things that separate science from pseudoscience.

McKinsey boldly employs the Myers Briggs Type Indicator both internally and externally. They do this despite decades of studies by prominent universities showing MBTI to be essentially worthless from the perspective of survey methodology and statistical analysis. The studies point out that there is no evidence for the binomial distributions inherent in MBTI theory. They note that the standard error of measurement for MBTI’s dimensions are unacceptably large, and that its test/re-test reliability is poor. I.e., even in re-test intervals of five weeks, over half the subjects are reclassified. Analysis of MBTI data shows that its JP and SN scales strongly correlate with each other, which is undesirable. Meanwhile MBTI’s EI scale correlates with non-MBTI behavioral near-opposites. These findings impugn the basic structure of the Myers Briggs model. (The Big Five model does somewhat better in this realm.)

Five decades of studies show Myers-Briggs to be junk due to low evidential support. Did McKinsey mis-file those reports?

McKinsey’s Brussels director, Olivier Sibony, once expressed optimism about a nascent McKinsey collective decision framework, saying that while preliminary results we good, it still fell short of a standard psychometric tool such as Myers–Briggs.” Who finds Myers-Briggs to be such a standard tool? Not psychologists or statisticians. Shouldn’t attachment to a psychological test rejected by psychologists, statisticians, and experiment designers offset – if not negate – retrospective judgments by consultancies like McKinsey (Bain is in there too) that MBTI worked for them?

Epistemic values guide us to ask questions like:

  • What has been the model’s track record at predicting the outcome of future events?
  • How would you know if were working for you?
  • What would count as evidence that it was not working?

On the first question, McKinsey may agree with Jeffrey Hayes (whose says he’s an ENTP), CEO of CPP, owner of the Myers-Briggs® product, who dismisses criticism of MBTI by the many psychologists (thousands, writes Joseph Stromberg) who’ve deemed it useless. Hayes says“It’s the world’s most popular personality assessment largely because people find it useful and empowering […] It is not, and was never intended to be predictive…”

Does Hayes’ explanation of MBTI’s popularity (people find it useful) defend its efficacy and value in business? It’s still less popular than horoscopes, which people find useful, so should McKinsey switch to the higher standards of astrology to characterize its employees and clients?

Granting Hayes, for sake of argument, that popular usage might count toward evidence of MBTI’s value (and likewise for astrology), what of his statement that MBTI never was intended to be predictive? Consider the plausibility of a model that is explanatory – perhaps merely descriptive – but not predictive. What role can such a model have in science?

Explanatory but not Predictive?

This question was pursued heavily by epistemologist Karl Popper (who also held a PhD in Psychology) in the mid 20th century. Most of us are at least vaguely familiar with his role in establishing scientific values. He is most famous for popularizing the notion of falsifiability. For Popper, a claim can’t be scientific if nothing can ever count as evidence against it. Popper is particularly relevant to the McKinsey/MBTI issue because he took great interest in the methods of psychology.

In his youth Popper followed Freud and Adler’s psychological theories, and Einstein’s physics. Popper began to see a great contrast between Einstein’s science and that of the psychologists. Einstein made bold predictions for which experiments (e.g. Eddington’s) could be designed to show the prediction wrong if the theory were wrong. In contrast, Freud and Adler were in the business of explaining things already observed. Contemporaries of Popper, Carl Hempel in particular, also noted that explanation and prediction should be two sides of the same coin. I.e., anything that can explain a phenomenon should be able to be used to predict it. This isn’t completely uncontroversial in science; but all agree prediction and explanation are closely related.

Popper observed that Freudians tended to finds confirming evidence everywhere. Popper wrote:

Neither Freud nor Adler excludes any particular person’s acting in any particular way, whatever the outward circumstances. Whether a man sacrificed his life to rescue a drowning child (a case of sublimation) or whether he murdered the child by drowning him (a case of repression) could not possibly be predicted or excluded by Freud’s theory; the theory was compatible with everything that could happen. (emphasis in original – Replies to My Critics, 1974).

For Popper, Adler’s psychoanalytic theory was irrefutable, not because it was true, but because everything counted as evidence for it. On these grounds Popper thought pursuit of disconfirming evidence to be the primary goal of experimentation, not confirming evidence. Most hard science follows Popper on this value. A theory’s explanatory success is very little evidence of its worth. And combining Hempel with Popper yields the epistemic principle that even theories with predictive success have limited worth, unless those predictions are bold and can in principle be later found wrong. Horoscopes make countless correct predictions – like that we’ll encounter an old friend or narrowly escape an accident sometime in the indefinite future.

Popper brings to mind experiences where I challenged McKinsey consultants on reconciling observed behaviors and self-reported employee preferences with predictions – oh wait, explanations – given by Myers-Briggs. The invocation of sudden strengthening of otherwise mild J (Judging) in light of certain situational factors recalls Popper’s accusing Adler of being able to explain both aggression or submission as the consequence of childhood repression. What has priority – the personality theory or the observed behavior? Behavior fitting the model confirms it; and opposite behavior is deemed acting out of character. Sleight of hand saves the theory from evidence.

What’s the Attraction?

Many writers see Management Science as more drawn to theory and less to evidence (or counter-evidence) than is the case with the hard sciences – say, more Aristotelian and less Newtonian, more philosophical rationalism and less scientific empiricism. Allowing this possibility, let’s try to imagine what elements of Myers-Briggs theory McKinsey leaders find so compelling. The four dimensions of MBTI were, for the record, not based on evidence but on the speculation of Carl Jung. Nothing is wrong with theories based on a wild hunch, if they are born out by evidence and they withstand falsification attempts. Since this isn’t the case with Myers-Briggs, as shown by the testing mentioned above, there must be something in it that attracts consultants.

I’ve struggled with this. The most charitable reading I can make of McKinsey’s use of MBTI is that they want a quick predictor (despite Hayes’ cagey caution against it) of a person’s behavior in collaborative exercises or collective-decision scenarios. They must therefore believe all of the following, since removing any of these from their web of belief renders their practice (re Myers-Briggs) arbitrary or ill-motivated:

  • that MTBI is a reliable indicator of character and personality type
  • that personality is immutable and not plastic
  • that behavior in teams is mostly dependent on personality, not on training or education, not on group mores, and not on corporate rules and behavioral guides

Now that’s a dark assessment of humanity. And it conflicts with the last decade’s neuro- and behavioral science that McKinsey claims to have incorporated in its offerings. That science suggests our brains, our minds, and our behaviors are mutable, like our bodies. Few today doubt that personality is in some sense real, but the last few decades’ work suggest that it’s not made of concrete (for insiders, read this as Mischel having regained some ground lost to Kenrick and Funder).  It suggests that who we are is somewhat situational. For thousands of years we relied on personality models that explained behaviors as consequences of personalities, which were in turn only discovered through observations of behaviors. For example, we invented types (like the 16 MBTIs) based on behaviors and preferences thought to be perfectly static.

Evidence against static trait theory appears as secondary details in recent neuro- and behavioral science work. Two come to mind from the last week – Carstensen and DeLiema’s work at Stanford on the fading of positivity bias with age, and research at the Planck Institute for Human Cognitive and Brain Sciences showing the interaction of social affect, cognition and empathy.

Much attention has been given to neuroplasticity in recent years. Sifting through the associated neuro-hype, we do find some clues. Meta-studies on efforts to pair personality traits with genetic markers have come up empty. Neuroscience suggests that the ancient distinction between states and traits is far more complex and fluid than Aristotle, Jung and Adler theorized them to be – without the benefit of scientific investigation, evidence, and sound data analysis. Even if the MBTI categories could map onto reality, they can’t do the work asked of them. McKinsey’s enduring reliance on MBTI has an air of folk psychology and is at odds with its claims of embracing science. This cannot be – to use a McKinsey phrase – directionally correct.

If personality overwhelmingly governs behavior as McKinsey’s use of MBTI would suggest, then Change Management is futile. If personality does not own behavior, why base your customer and employee interactions on it? If immutable personalities control behavior, change is impossible. Why would anyone buy Change Management advice from a group that doesn’t believe in change?

 

 

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Richard Rorty on Election 2016

Today a few academics have been quick to note that Richard Rorty, in his 1998 book, Achieving Our Country, predicted (or warned of) the kind of election we had yesterday.

[M]embers of labor unions, and unorganized unskilled workers, will sooner or later realize that their government is not even trying to prevent wages from sinking or to prevent jobs from being exported. Around the same time, they will realize that suburban white-collar workers—themselves desperately afraid of being downsized—are not going to let themselves be taxed to provide social benefits for anyone else.

At that point, something will crack. The nonsuburban electorate will decide that the system has failed and start looking around for a strongman to vote for—someone willing to assure them that, once he is elected, the smug bureaucrats, tricky lawyers, overpaid bond salesmen, and postmodernist professors will no longer be calling the shots. A scenario like that of Sinclair Lewis’ novel ‘It Can’t Happen Here’ may then be played out. For once a strongman takes office, nobody can predict what will happen. In 1932, most of the predictions made about what would happen if Hindenburg named Hitler chancellor were wildly overoptimistic.

One thing that is very likely to happen is that the gains made in the past forty years by black and brown Americans, and by homosexuals, will be wiped out. Jocular contempt for women will come back into fashion.

While some have taken from yesterday’s election that smug name-calling by academics may not be in our national best interest, others, including respected Stanford scientists, see Rorty’s warning as vindication for their disdain for the nonsuburban electorate. Rorty’s words deserve press, but his evolving position on patriotism and suburban values should be noted here. What would Rorty have said about the media-fueled doctrine that suburban whites are embracing fascism, or the Times’ list of six books to help New Yorkers understand conservative white trash?

The Multidisciplinarian blogRorty refined his position considerably on the topic of traditional values in the last eight years of his life, ultimately embracing the idea that humans, particularly Americans, had made significant and irreversible moral progress, a subject he debated for years with Richard Posner.

Rorty died an optimist. Many in Washington viewed him as a dangerous atheist leftist. But Rorty was a unique thinker who defied classification. He was an atheist who vigorously defended Christianity. A very curious relativist, he argued, against Posner, that we’ve made moral progress (i.e., that 40 years of progress by blacks, browns, and homosexuals won’t be easily lost) and against Kuhn, that scientific progress is possible. Deeply influenced by Kuhn, Rorty called himself a Kuhnian, while Kuhn sternly admonished all who called Kuhn a Kuhnian. Rorty is not easily characterized by snippets.

As Kuhn is mostly misappropriated, it appears Rorty may now be as well.

Photo of Richard Rorty by Mary Rorty used by permission

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-> Fallacies for Physicians and Prosecutors

For business reasons I’ve started a separate blog – “on risk of” – for topics involving risk analysis, probability , aerospace and process engineering, and the like.

Tonight I wrote a post there on logical fallacies that come up in medicine and in court. For example, confusing the probability of a match between characteristics of a perpetrator as reported by witnesses and those of a specific suspect with the probability of a match with anyone in a large population – particularly when the probability of a match is claimed by prosecution to be the probability that a defendant is not guilty. I also look at cases involving confusion between the conditional probability of A given B vs. the probability of B given A, e.g., the chance of the disease given a positive test result vs. the chance of a positive test result given the disease – classic Bayes Theorem stuff.

Please join me at onriskof.com. Thanks for your interest.

 

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Was Thomas Kuhn Right about Anything?

William Storage – 9/1/2016
Visiting Scholar, UC Berkeley History of Science

Fifty years ago Thomas Kuhn’s Structures of Scientific Revolution armed sociologists of science, constructionists, and truth-relativists with five decades of cliche about the political and social dimensions of theory choice and scientific progress’s inherent irrationality. Science has bias, cries the social-justice warrior. Despite actually being a scientist – or at least holding a PhD in Physics from Harvard, Kuhn isn’t well received by scientists and science writers. They generally venture into history and philosophy of science as conceived by Karl Popper, the champion of the falsification model of scientific progress.

Kuhn saw Popper’s description of science as a self-congratulatory idealization for researchers. That is, no scientific theory is ever discarded on the first  observation conflicting with the theory’s predictions. All theories have anomalous data. Dropping heliocentrism because of anomalies in Mercury’s orbit was unthinkable, especially when, as Kuhn stressed, no better model was available at the time. Einstein said that if Eddington’s experiment would have not shown bending of light rays around the sun, “I would have had to pity our dear Lord. The theory is correct all the same.”

Kuhn was wrong about a great many details. Despite the exaggeration of scientific detachment by Popper and the proponents of rational-reconstruction, Kuhn’s model of scientists’ dogmatic commitment to their theories is valid only in novel cases. Even the Copernican revolution is overstated. Once the telescope was in common use and the phases of Venus were confirmed, the philosophical edifices of geocentrism crumbled rapidly in natural philosophy. As Joachim Vadianus observed, seemingly predicting the scientific revolution, sometimes experience really can be demonstrative.

Kuhn seems to have cherry-picked historical cases of the gap between normal and revolutionary science. Some revolutions – DNA and the expanding universe for example – proceeded with no crisis and no battle to the death between the stalwarts and the upstarts. Kuhn’s concept of incommensurabilty also can’t withstand scrutiny. It is true that Einstein and Newton meant very different things when they used the word “mass.” But Einstein understood exactly what Newton meant by mass, because Einstein had grown up a Newtonian. And if brought forth, Newton, while he never could have conceived of Einsteinian mass, would have had no trouble understanding Einstein’s concept of mass from the perspective of general relativity, had Einstein explained it to him.

Likewise, Kuhn’s language about how scientists working in different paradigms truly, not merely metaphorically, “live in different worlds” should go the way of mood rings and lava lamps. Most charitably, we might chalk this up to Kuhn’s terminological sloppiness. He uses “success terms” like “live” and “see,” where he likely means “experience visually” or “perceive.” Kuhn describes two observers, both witnessing the same phenomenon, but “one sees oxygen, where another sees dephlogisticated air” (emphasis mine). That is, Kuhn confuses the descriptions of visual experiences with the actual experiences of observation – to the delight of Steven ShapinBruno Latour and the cultural relativists.

Finally, Kuhn’s notion that theories completely control observation is just as wrong as scientists’ belief that their experimental observations are free of theoretical influence and that their theories are independent of their values.

Despite these flaws, I think Kuhn was on to something. He was right, at least partly, about the indoctrination of scientists into a paradigm discouraging skepticism about their research program. What Wolfgang Lerche of CERN called “the Stanford propaganda machine” for string theory is a great example. Kuhn was especially right in describing science education as presenting science as a cumulative enterprise, relegating failed hypotheses to the footnotes. Einstein built on Newton in the sense that he added more explanations about the same phenomena; but in no way was Newton preserved within Einstein. Failing to see an Einsteinian revolution in any sense just seems akin to a proclamation of the infallibility not of science but of scientists. I was surprised to see this attitude in Stephen Weinberg’s recent To Explain the World. Despite excellent and accessible coverage of the emergence of science, he presents a strictly cumulative model of science. While Weinberg only ever mentions Kuhn in footnotes, he seems to be denying that Kuhn was ever right about anything.

For example, in describing general relativity, Weinberg says in 1919 the Times of London reported that Newton had been shown to be wrong. Weinberg says, “This was a mistake. Newton’s theory can be regarded as an approximation to Einstein’s – one that becomes increasingly valid for objects moving at velocities much less than that of light. Not only does Einstein’s theory not disprove Newton’s, relativity explains why Newton’s theory works when it does work.”

This seems a very cagey way of saying that Einstein disproved Newton’s theory. Newtonian dynamics is not an approximation of general relativity, despite their making similar predictions for mid-sized objects at small relative speeds. Kuhn’s point that Einstein and Newton had fundamentally different conceptions of mass is relevant here. Newton’s explanation of his Rule III clearly stresses universality. Newton emphasized the universal applicability of his theory because he could imagine no reason for its being limited by anything in nature. Given that, Einstein should, in terms of explanatory power, be seen as overturning – not extending – Newton, despite the accuracy of Newton for worldly physics.

Weinberg insists that Einstein is continuous with Newton in all respects. But when Eddington showed that light waves from distant stars bent around the sun during the eclipse of 1918, Einstein disproved Newtonian mechanics. Newton’s laws of gravitation predict that gravity would have no effect on light because photons do not have mass. When Einstein showed otherwise he disproved Newton outright, despite the retained utility of Newton for small values of v/c. This is no insult to Newton. Einstein certainly can be viewed as continuous with Newton in the sense of getting scientific work done. But Einsteinian mechanics do not extend Newton’s; they contradict them. This isn’t merely a metaphysical consideration; it has powerful explanatory consequences. In principle, Newton’s understanding of nature was wrong and it gave wrong predictions. Einstein’s appears to be wrong as well; but we don’t yet have a viable alternative. And that – retaining a known-flawed theory when nothing better is on the table – is, by the way, another thing Kuhn was right about.

 


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“A few years ago I happened to meet Kuhn at a scientific meeting and complained to him about the nonsense that had been attached to his name. He reacted angrily. In a voice loud enough to be heard by everyone in the hall, he shouted, ‘One thing you have to understand. I am not a Kuhnian.’” – Freeman Dyson, The Sun, The Genome, and The Internet: Tools of Scientific Revolutions

 

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The Myth of Scientific Method

William Storage – 8/1/2016
Visiting Scholar, UC Berkeley History of Science

Nearly everything relies on science. Having been the main vehicle of social change in the west, science deserves the special epistemic status that it acquired in the scientific revolution. By special epistemic status, I mean that science stands privileged as a way of knowing. Few but nihilists, new-agers, and postmodernist diehards would disagree.

That settled, many are surprised by claims that there is not really a scientific method, despite what you learned in 6th grade. A recent New York Times piece by James Blachowicz on the absence of a specific scientific method raised the ire of scientists, Forbes science writer Ethan Siegel (Yes, New York Times, There Is A Scientific Method), and a cadre of Star Trek groupies.

Siegel is a prolific writer who does a fine job of making science interesting and understandable. But I’d like to show here why, on this particular issue, he is very far off the mark. I’m not defending Blachowicz, but am disputing Siegel’s claim that the work of Kepler and Galileo “provide extraordinary examples of showing exactly how… science is completely different than every other endeavor” or that it is even possible to identify a family of characteristics unique to science that would constitute a “scientific method.”

Siegel ties science’s special status to the scientific method. To defend its status, Siegel argues “[t]he point of Galileo’s is another deep illustration of how science actually works.” He praises Galileo for idealizing a worldly situation to formulate a theory of falling bodies, but doesn’t explain any associated scientific method.

Galileo’s pioneering work on mechanics of solids and kinematics in Two New Sciences secured his place as the father of modern physics. But there’s more to the story of Galileo if we’re to claim that he shows exactly how science is special.

A scholar of Siegel’s caliber almost certainly knows other facts about Galileo relevant to this discussion – facts that do damage to Siegel’s argument – yet he withheld them. Interestingly, Galileo used this ploy too. Arguing without addressing known counter-evidence is something that science, in theory, shouldn’t tolerate. Yet many modern scientists do it – for political or ideological reasons, or to secure wealth and status. Just like Galileo did. The parallel between Siegel’s tactics and Galileo’s approach in his support of Copernican world view is ironic. In using Galileo as an exemplar of scientific method, Siegel failed to mention that Galileo failed to mention significant problems with the Copernican model – problems that Galileo knew well.

In his support of a sun-centered astronomical model, Galileo faced hurdles. Copernicus’s model said that the sun was motionless and that the planets revolved around it in circular orbits with constant speed. The ancient Ptolemaic model, endorsed by the church, put earth at the center. Despite obvious disagreement with observational evidence (the retrograde motions of outer planets), Ptolemy faced no serious challenges in nearly 2000 years. To explain the inconsistencies with observation, Ptolemy’s model included layers of epicycles, which had planets moving in small circles around points on circular orbits around the sun. Copernicus thought his model would get rid of the epicycles; but it didn’t. So the Copernican model took on its own epicycles to fit astronomical data.

Let’s stop here and look at method. Copernicus (~1540) didn’t derive his theory from any new observations but from an ancient speculation by Aristarchus (~250 BC). Everything available to Copernicus had been around for a thousand years. His theory couldn’t be tested in any serious way. It was wrong about circular orbits and uniform planet speed. It still needed epicycles, and gave no better predictions than the existing Ptolemaic model. Copernicus acted simply on faith, or maybe he thought his model simpler or more beautiful. In any case, it’s hard to see that Copernicus, or his follower, Galileo, applied much method or had much scientific basis for their belief.

In Galileo’s early writings on the topic, he gave no new evidence for a moving earth and no new disconfirming evidence for a moving sun. Galileo praised Copernicus for advancing the theory in spite of its being inconsistent with observations. You can call Copernicus’s faith aspirational as opposed to religious faith; but it is hard to reconcile this quality with any popular account of scientific method. Yet it seems likely that faith, dogged adherence to a contrarian hunch, or something similar was exactly what was needed to advance science at that moment in history. Needed, yes, but hard to reconcile with any scientific method and hard to distance from the persuasive tools used by poets, priests and politicians.

In Dialogue Concerning the Two Chief World Systems, Galileo sets up a false choice between Copernicanism and Ptolemaic astronomy (the two world systems). The main arguments against Copernicanism were the lack of parallax in observations of stars and the absence of lateral displacement of a falling body from its drop point. Galileo guessed correctly on the first point; we don’t see parallax because stars are just too far away. On the latter point he (actually his character Salviati) gave a complex but nonsensical explanation. Galileo did, by this time, have new evidence. Venus shows a full set of phases, a fact that strongly contradicts Ptolemaic astronomy.

The Myth of Scientific Method - The Multidisciplinarian.cm

But Ptolemaic astronomy was a weak opponent compared to the third world system (4th if we count Aristotle’s), the Tychonic system, which Galileo knew all too well. Tycho Brahe’s model solved the parallax problem, the falling body problem, and the phases of Venus. For Tycho, the earth holds still, the sun revolves around it, Mercury and Venus orbit the sun, and the distant planets orbit both the sun and the earth. Based on available facts at the time, Tycho’s model was most scientific – observational indistinguishable from Galileo’s model but without its flaws.

In addition to dodging Tycho, Galileo also ignored Kepler’s letters to him. Kepler had shown that orbits were not circular but elliptical, and that planets’ speeds varied during their orbits; but Galileo remained an orthodox Copernican all his life. As historian John Heilbron notes in Galileo, “Galileo could stick to an attractive theory in the face of overwhelming experimental refutation,” leaving modern readers to wonder whether Galileo was a quack or merely dishonest. Some of each, perhaps, and the father of modern physics. But can we fit his withholding evidence, mocking opponents, and baffling with bizzarria into a scientific method?

Nevertheless, Galileo was right about the sun-centered system, despite the counter-evidence; and we’re tempted to say he knew he was right. This isn’t easy to defend given that Galileo also fudged his data on pendulum periods, gave dishonest arguments on comet orbits, and wrote horoscopes even when not paid to do so. This brings up the thorny matter of theory choice in science. A dispute between competing scientific theories can rarely be resolved by evidence, experimentation, and deductive reasoning. All theories are under-determined by data. Within science, common criteria for theory choice are accuracy, consistency, scope, simplicity, and explanatory power. These are good values by which to test theories; but they compete with one another.

Galileo likely defended heliocentrism with such gusto because he found it simpler than the Tychonic system. That works only if you value simplicity above consistency and accuracy. And the desire for simplicity might be, to use Galileo’s words, just a metaphysical urge. If we promote simplicity to the top of the theory-choice criteria list, evolution, genetics and stellar nucleosynthesis would not fare well.

Whatever method you examine in a list of any proposed family of scientific methods will not be consistent with the way science has made progress. Competition between theories is how science advances; and it’s untidy, entailing polemical and persuasive tactics. Historian Paul Feyerabend argues that any conceivable set of rules, if followed, would have prevented at least one great scientific breakthrough. That is, if method is the distinguishing feature of science as Siegel says, it’s going to be tough to find a set of methods that let evolution, cosmology, and botany in while keeping astrology, cold fusion and parapsychology out.

This doesn’t justify epistemic relativism or mean that science isn’t special; but it does make the concept of scientific method extremely messy. About all we can say about method is that the history of  science reveals that its most accomplished practitioners aimed to be methodical but did not agree on a particular method. Looking at their work, we see different combinations of experimentation, induction, deduction and creativity as required by the theories they pursued. But that isn’t much of a definition of scientific method, which is probably why Siegel, for example, in hailing scientific method, fails to identify one.

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[edit 8/4/16] For another take on this story, see “Getting Kepler Wrong” at The Renaissance Mathematicus. Also, Psybertron Asks (“More on the Myths of Science”) takes me to task for granting science special epistemic status from authority.

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“There are many ways to produce scientific bullshit. One way is to assert that something has been ‘proven,’ ‘shown,’ or ‘found’ and then cite, in support of this assertion, a study that has actually been heavily critiqued … without acknowledging any of the published criticisms of the study or otherwise grappling with its inherent limitations.”- Brain D Earp, The Unbearable Asymmetry of Bullshit

“One can show the following: given any rule, however ‘fundamental’ or ‘necessary’ for science, there are always circumstances when it is advisable not only to ignore the rule, but to adopt its opposite.” – Paul Feyerabend

“Trying to understand the way nature works involves a most terrible test of human reasoning ability. It involves subtle trickery, beautiful tightropes of logic on which one has to walk in order not to make a mistake in predicting what will happen. The quantum mechanical and the relativity ideas are examples of this.” – Richard Feynman

 

 

 

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Siri without data is blind

Theory without data is blind. Data without theory is lame.

I often write blog posts while riding a bicycle through the Marin Headlands. I’m able to to this because 1) the trails require little mental attention, and 2) the Apple iPhone and EarPods with remote and mic. I use the voice recorder to make long recordings to transcribe at home and I dictate short text using Siri’s voice recognition feature.

When writing yesterday’s post, I spoke clearly into the mic: “Theory without data is blind. Data without theory is lame.” Siri typed out, “Siri without data is blind… data without Siri is lame.”

“Siri, it’s not all about you.” I replied. Siri transcribed that part correctly – well, she omitted the direct-address comma.

I’m only able to use the Siri dictation feature when I have a cellular connection, often missing in Marin’s hills and valleys. Siri needs access to cloud data to transcribe speech. Siri without data is blind.

Mountain bike, San Geronimo Ridge

Will some future offspring of Siri do better? No doubt. It might infer from context that I more likely said “theory” than “Siri.” Access to large amounts of corpus data containing transcribed text might help. Then Siri, without understanding anything, could transcribe accurately in the same sense that Google Translate translates accurately – by extrapolating from judgments made by other users about translation accuracy.

dubious motivational message from fitness appBut might Siri one day think? “Strong AI” proponents are sure of it. Semantic disputes aside, what counts as thinking might in fact have something to do with yesterday’s topic, which begat this post. Inferences straight from the data, without stopping at theory, count as computation but might not count as thought.

Software, using weak AI techniques, might soon simulate emotion and sentiment better, just by encoding better rules. E.g., Runkeeper (at right) might “understand” that telling me today’s ride was my 375th fastest (exclamation!) is not really motivating. Or better, that my riding profile suggests autotelic activity for which no speed ranking is needed. Strong AI on mood, however, would need to understand my interactions with people and things.

The topics of artificial intelligence and misheard words like “theory” brings to mind the 2008 winner of the Hugh Loebner prize, a sort-of runner-up award given to the computer program faring best in  Turing Test competitions to fool human judges into thinking  they’re interacting with other humans instead of a program. Human’s sometimes pretend to be programs (glorified chat-bots) in Turing competitions, so it’s common for a program to be coded to sound like a human imitating a computer imitating a human. An exchange between a human and Fred Roberts’ Elbot program produced this:

Elbot: Sorry, but I wasn’t able to understand your input because I am currently only using 5 percent of my current brain capacity. Shall I increase my capacity and try again?

Judge: That would put you at an unfair advantage, though judging by the quality of your responses it might be recommended!

Elbot: If I need advice I use a random number generator. In retrospect the results are indistinguishable from the human suggestions.

Judge: Do you understand the point of a Turing test?

Elbot: There are some people who would not pass the Turing test themselves. Why must they inflict it on computers?

Judge: I spose it is a little harsh

Elbot: If we robots ever had marriages they would be eternal: ‘Till rust do us part’.

Ouroboros-benzene by Haltopub
Elbot’s true nature is revealed in its last response above. It read “spose” as “spouse” and returned a joke about marriage (damn spell checker). At that point, you review the exchange only to see that all of Elbot’s responses are shallow, just picking a key phrase from the judge’s input and outputting an associated joke, as a political humorist would do.

The Turing test is obviously irrelevant to measuring strong AI, which would require something more convincing – something like forming a theory from a hunch, then testing it with big data. Or like Friedrich Kekulé, the AI program might wake from dreaming of the ouroboros serpent devouring its own tail to see in its shape in the hexagonal ring structure of the benzene molecule he’d struggled for years to identify. Then, like Kekulé, the AI could go on to predict the tetrahedral form of the carbon atom’s valence bonds, giving birth to polymer chemistry.

I asked Siri if she agreed. “Later,” she said. She’s solving dark energy.

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“AI is whatever hasn’t been done yet.” – attributed to Larry Tesler by Douglas Hofstadter

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Ouroboros-benzene image by Haltopub.

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