Tag: science

COVID-19 and “Naïve Probabilism”

[from the London Mathematical Laboratory]

In the early weeks of the 2020 U.S. COVID-19 outbreak, guidance from the scientific establishment and government agencies included a number of dubious claims—masks don’t work, there’s no evidence of human-to-human transmission, and the risk to the public is low. These statements were backed by health authorities, as well as public intellectuals, but were later disavowed or disproven, and the initial under-reaction was followed by an equal overreaction and imposition of draconian restrictions on human social activities.

In a recent paper, LML Fellow Harry Crane examines how these early mis-steps ultimately contributed to higher death tolls, prolonged lockdowns, and diminished trust in science and government leadership. Even so, the organizations and individuals most responsible for misleading the public suffered little or no consequences, or even benefited from their mistakes. As he discusses, this perverse outcome can be seen as the result of authorities applying a formulaic procedure of “naïve probabilism” in facing highly uncertain and complex problems, and largely assuming that decision-making under uncertainty boils down to probability calculations and statistical analysis.

This attitude, he suggests, might be captured in a few simple “axioms of naïve probabilism”:

Axiom 1: more complex the problem, the more complicated the solution.

This idea is a hallmark of naïve decision making. The COVID-19 outbreak was highly complex, being a novel virus of uncertain origins, and spreading through the interconnected global society. But the potential usefulness of masks was not one of these complexities. The mask mistake was consequential not because masks were the antidote to COVID-19, but because they were a low cost measure the effect of which would be neutral at worst; wearing a mask can’t hurt in reducing the spread of a virus.

Yet the experts neglected common sense in favor of a more “scientific response” based on rigorous peer review and sufficient data. Two months after the initial U.S. outbreak, a study confirmed the obvious, and masks went from being strongly discouraged to being mandated by law. Precious time had been wasted, many lives lost, and the economy stalled.

Crane also considers another rule of naïve probabilism:

Axiom 2: Until proven otherwise, assume that the future will resemble the past.

In the COVID-19 pandemic, of course, there was at first no data that masks work, no data that travel restrictions work, no data of human-to-human transmission. How could there be? Yet some naïve experts took this as a reason to maintain the status quo. Indeed, many universities refused to do anything in preparation until a few cases had been detected on campus—at which point they had some data, as well as hundreds or thousands of other as yet undetected infections.

Crane touches on some of the more extreme examples of his kind of thinking, which assumes that whatever can’t be explained in terms of something that happened in the past is speculative, non-scientific and unjustifiable:

“This argument was put forward by John Ioannidis in mid-March 2020, as the pandemic outbreak was already spiralling out of control. Ioannidis wrote that COVID-19 wasn’t a ‘once-in-a-century pandemic,’ as many were saying, but rather a ‘once-in-a-century data-fiasco’. Ioannidis’s main argument was that we knew very little about the disease, its fatality rate, and the overall risks it poses to public health; and that in face of this uncertainty, we should seek data-driven policy decisions. Until the data was available, we should assume COVID-19 acts as a typical strain of the flu (a different disease entirely).”

Unfortunately, waiting for the data also means waiting too long, if it turns out that the virus turns out to be more serious. This is like waiting to hit the tree before accepting that the available data indeed supports wearing a seatbelt. Moreover, in the pandemic example, this “lack of evidence” argument ignores other evidence from before the virus entered the United States. China had locked down a city of 10 million; Italy had locked down its entire northern region, with the entire country soon to follow. There was worldwide consensus that the virus was novel, the virus was spreading fast and medical communities had no idea how to treat it. That’s data, and plenty of information to act on.

Crane goes on to consider a 3rd axiom of naïve probabilism, which aims to turn ignorance into a strength. Overall, he argues, these axioms, despite being widely used by many prominent authorities and academic experts, actually capture a set of dangerous fallacies for action in the real world.

In reality, complex problems call for simple, actionable solutions; the past doesn’t repeat indefinitely (i.e., COVID-19 was never the flu); and ignorance is not a form of wisdom. The Naïve Probabilist’s primary objective is to be accurate with high probability rather than to protect against high-consequence, low-probability outcomes. This goes against common sense principles of decision making in uncertain environments with potentially very severe consequences.

Importantly, Crane emphasizes, the hallmark of Naïve Probabilism is naïveté, not ignorance, stupidity, crudeness or other such base qualities. The typical Naïve Probabilist lacks not knowledge or refinement, but the experience and good judgment that comes from making real decisions with real consequences in the real world. The most prominent naïve probabilists are recognized (academic) experts in mathematical probability, or relatedly statistics, physics, psychology, economics, epistemology, medicine or so-called decision sciences. Moreover, and worryingly, the best known naïve probabilists are quite sophisticated, skilled in the art of influencing public policy decisions without suffering from the risks those policies impose on the rest of society.

Read the paper. [Archived PDF]

Mathematics and the World: London Mathematical Laboratory

Stability of Heteroclinic Cycles in Rings of Coupled Oscillators

[from the London Mathematical Laboratory]

Complex networks of interconnected physical systems arise in many areas of mathematics, science and engineering. Many such systems exhibit heteroclinic cycles—dynamical trajectories that show a roughly periodic behavior, with non-convergent time averages. In these systems, average quantities fluctuate continuously, although the fluctuations slow down as the dynamics repeatedly and systematically approach a set of fixed points. Despite this general understanding, key open questions remain concerning the existence and stability of such cycles in general dynamical networks.

In a new paper [archived PDF], LML Fellow Claire Postlethwaite and Rob Sturman of the University of Leeds investigate a family of coupled map lattices defined on ring networks and establish stability properties of the possible families of heteroclinic cycles. To begin, they first consider a simple system of N coupled systems, each system based on the logistic map, and coupling between systems determined by a parameter γ. If γ = 0, each node independently follows logistic map dynamics, showing stable periodic cycles or chaotic behavior. The authors design the coupling between systems to have a general inhibitory effect, driving the dynamics toward zero. Intuitively, this should encourage oscillatory behavior, as nodes can alternately be active (take a non-zero value), and hence inhibit those nodes to which it is connected to, decay, when other nodes in turn inhibit them; and finally grow again to an active state as the nodes inhibiting them decay in turn. In the simple case of N = 3, for example, this dynamics leads to a trajectory which cycles between three fixed points.

The authors then extend earlier work to consider larger networks of coupled systems as described by a directed graph, describing how to find the fixed points and heteroclinic connections for such a system. In general, they show, this procedure results in highly complex and difficult to analyze heteroclinic network. Simplifying to the special case of N-node directed graphs with one-way nearest neighbor coupling, they successfully derive results for the dynamic stability of subcycles within this network, establishing that only one of the subcycles can ever be stable.

Overall, this work demonstrates that heteroclinic networks can typically arise in the phase space dynamics of certain types of symmetric graphs with inhibitory coupling. Moreover, it establishes that at most one of the subcycles can be stable (and hence observable in simulations) for an open set of parameters. Interestingly, Postlethwaite and Sturman find that the dynamics associated with such cycles are not ergodic, so that long-term averages do not converge. In particular, averaged observed quantities such as Lyapunov exponents are ill-defined, and will oscillate at a progressively slower rate.

In addition, the authors also address the more general question of whether or not a stable heteroclinic cycle is likely to be found in the corresponding phase space dynamics of a randomly generated physical network of nodes. In preliminary investigations using randomly generated Erdős–Rényi graphs, they find that the probability of existence of heteroclinic cycles increases both as the number of nodes in the physical network increases, and also as the density of edges in the physical network decreases. However, even in cases where the probability of existence of heteroclinic cycles is high, there is also a high chance of the existence of a stable fixed point in the phase space. From this they conclude that the question of the stability of the heteroclinic cycle is important in determining whether or not the heteroclinic cycle, and associated slowing down of trajectories, will be observed in the phase space associated with a randomly generated graph.

The paper is available as a pre-print here [archived PDF].

Education and Word and Number Hidden Vagueness

These mini-essays help students of any age to re-understand education in a deeper and more connected way.

They look for “circum-spective” intelligence. (Not in the sense of prudential or cautious but in the sense of “around-looking.”)

One of the things to begin to see is that explaining things in schools is misleading “ab initio” (i.e., from the beginning).

Let’s do an example:

In basic algebra, you’re asked: what happens to (x2 – 1)/(x – 1) as x “goes to” (i.e., becomes) 1.

If you look at the numerator (thing on top), x2 is also 1 (since 1 times 1 is 1) and (1 – 1) is zero. The denominator is also (1 – 1) and zero.

Thus you get 0 divided by 0.

You’re then told that’s a no-no and that’s because zeros and infinities lead to all kinds of arithmetic “bad behavior” or singularities.

You’re then supposed to see that x2 – 1 can be re-written as (x – 1)(x + 1) and since “like cancels like,” you cancel the x – 1 is the numerator and denominator and “get rid” of it.

This leaves simply x + 1. So, as x goes to 1, x + 1 goes to 2 and you have a “legitimate” answer and have bypassed the impasse of 0 acting badly (i.e., zero divided by zero).

If you re-understand all this more slowly you’ll see that there are endless potential confusions:

For example: you cannot say that (x2 – 1)/(x – 1) = x + 1 since looking at the two sides of the equal sign shows different expressions which are not equal.

They’re also not really equivalent.

You could say that coming up with x + 1 is a workaround or a “reduced form” or a “downstream rewrite” of (x2 – 1)/(x – 1).

This reminds us of the endless confusions in high school science: if you combine hydrogen gas (H2) with oxygen gas (O2) you don’t get water (H2O). Water is the result of a chemical reaction giving you a compound.

A mixture is not a compound. Chemistry is based on this distinction.

Math and science for that matter, are based on taking a formula or expression (like the one we saw above) and “de-cluttering” it or “shaking loose” a variant form which is not identical and not the same but functionally equivalent in a restricted way.

A lot of students who fail to follow high school or college science sense these and other “language and number” problems of hidden vagueness.
School courses punish students who “muse” to themselves about hidden vagueness. This behavior is pre-defined as “bad woolgathering” but we turn this upside down and claim it is potentially “good woolgathering” and might lead to enchantment which then underlies progress in getting past one’s fear of something like math or science or anything else.

One is surrounded by this layer of reality on all sides, what Wittgenstein calls “philosophy problems which are really language games.”

Think of daily life: you say to someone: “you can count one me.” You mean trust, rely on, depend on, where count on is a “set phrase.” (The origin of the phrase and how it became a set phrase is probably unknowable and lost in the mists of time.)

“You can count on me” does not mean you can stand on me and then count something…one, two, three.

In other words in all kinds of language (English, say, or math as a language) one is constantly “skating over” such logic-and-nuance-and-meaning issues.

The genius Kurt Gödel (Einstein’s walk around buddy at Princeton) saw this in a deep way and said that it’s deeply surprising that languages work at all (spoken, written or mathematical) since the bilateral sharing of these ambiguities would seem deadly to any clarity at all and communication itself would seem a rather unlikely outcome.

You could also say that drama giants of the twentieth century like Pinter, Ionesco and Beckett, intuit these difficulties which then underlie their plays.

All of this together gives you a more “composite” “circum-spective” view of what is really happening in knowledge acquisition.

Education and “Intuition Pumps”

Professor Daniel Dennett of Tufts uses the word “intuition pumps” in discussing intuitive understanding and its tweaking.

Let’s do a simple example, avoiding as always “rocket science,” where the intricacies weigh you down in advance. We make a U-turn and go back by choice to elementary notions and examples.

Think of the basic statistics curve. It’s called the Bell Curve, the Gaussian, the Normal Curve.

The first name is sort of intuitive based on appearance unless of course it’s shifted or squeezed and then it’s less obvious. The second name must be based on either the discoverer or the “name-giver” or both, if the same person. The third is a bit vague.

Already one’s intuitions and hunches are not fool-proof.

The formula for the Bell Curve is:

\begin{equation} y = \frac{1}{\sqrt{2\pi}}e^{\frac{-x^2}{2}} \end{equation}

We immediately see the two key constants: π (pi) and e. These are: 22/7 and 2.71823 (base of natural logs).

The first captures something about circularity, the second continuous growth as in continuous compounding of interest.

You would not necessarily anticipate seeing these two “irrational numbers” (they “go on” forever) in a statistics graph. Does that mean your intuition is poor or untutored or does it mean that “mathworld” is surprising?

It’s far from obvious.

For openers, why should π (pi) be everywhere in math and physics?

Remember Euler’s identity: eiπ + 1 = 0

That the two key integers (1 and 0) should relate to π (pi), e, and i (-1) is completely unexpected and exotic.

Our relationship to “mathworld” is quite enigmatic and this raises the question whether Professor Max Tegmark of MIT who proposes to explain “ultimate reality” through the “math fabric” of all reality might be combining undoubted brilliance with quixotism. We don’t know.

Education and Finality Claims

Stephen Hawking kept saying he wanted to discover the ultimate world-equation. This would be the final “triumph of the rational human mind.”

This would presumably imply that if one had such a world-equation, one could infer or deduce all the formalisms in a university physics book with its thousand pages of equations, puzzles and conundrums, footnotes and names and dates.

While hypothetically imaginable, this seems very unlikely because too many phenomena are included, too many topics, too many rules and laws.

There’s another deep problem with such Hawking-type “final equation” quests. Think of the fact that a Henri Poincaré (died in 1912) suddenly appears and writes hundreds of excellent science papers. Think of Paul Erdős (died in 1996) and his hundreds of number theory papers. Since the appearance of such geniuses and powerhouses is not knowable in advance, the production of new knowledge is unpredictable and would “overwhelm” any move towards some world-equation which was formulated without the new knowledge since it was not known at the time that the world-equation was formalized.

Furthermore, if the universe is mathematical as MIT’s Professor Max Tegmark claims, then a Hawking-type “world-equation” would cover all mathematics without which parts of Tegmark’s universe would be “unaccounted for.”

In other words, history and the historical experience, cast doubt on the Stephen Hawking “finality” project. It’s not just that parts of physics don’t fit together. (General relativity and quantum mechanics, gravity and the other three fundamental forces.) Finality would also imply that there would be no new Stephen Hawking who would refute the world-equation as it stands at a certain point in time. In other words, if you choose, as scientists like Freeman Dyson claim that the universe is a “vast evolutionary” process, then the mathematical thinking about it is also evolving or co-evolving and there’s no end.

There are no final works in poetry, novels, jokes, language, movies or songs and there’s perhaps also no end to science.

Thus a Hawking-type quest for the final world-equation seems enchanting but quixotic.

Education and the Kinds of Wholeness

We have stated several times that we seek more educational holism in particular, the tentative kind made by students themselves after they study these “exercises in holism.” Let’s explore this.

There’s a short story by Anton Pavlovich Chekhov (29 January 1860 – 15 July 1904), a Russian playwright and short-story writer, who is considered to be among the greatest writers of short fiction in history. In his classic “A Boring Story,” he tells the reader about his inner yearning for some wholeness in his life:

“In my predilection for science, in my wish to live, in this siting on a strange bed and trying to know myself, in all the thoughts, feelings and conceptions I form about everything, something general is lacking that would unite it all into a single whole.

Each feeling and thought lives separately in me, and in all my opinions about science, the theater, literature, students, and in all the pictures drawn by my imagination, even the most skillful analyst would be unable to find what is known as a general idea or the god of the living man.

And if there isn’t that. there’s nothing.”

(Selected Stories of Anton Chekhov, Anton Chekhov, Richard Pevear and Larissa Volokhonsky (translators), Modern Library, 2000, page 104, “A Boring Story”)

The author wants to unite everything (i.e., “something general is lacking that would unite it all into a single whole,” as he puts it).

This is not what we have in mind because there is no “scheme of things” or “unified field theory” that we impart to students. That is for various kinds of “madrasas” (Arabic: مدرسة) including secular ones.

Rather, we encourage students to “walk around” topics, fields, educations, discussions, books, movies, quizzes and exams, lectures, assignments to develop a more “circum-spective” view of knowledge.

Remember “Husserl’s rhomboid.” Edmund Husserl (Heidegger’s teacher, died in 1938) would bring a matchbox to his classes in Germany and get students in his classroom to see that one cannot view the whole matchbox at once nor can rotating it capture all of it. Parts are visible, the whole matchbox is not.

We apply this principle to education and knowledge acquisition and offer the mental habit for students of “homemade” exercises in making more holistic views.

The narrator in Chekhov’s story, yearning to have a god-like view of reality and knowledge and experience (theatre, science, etc.) as a unified “thing” is not our interest since it too elusive.

William James (died in 1910) says several times in his writings that “one mind can’t swallow the whole of reality.” Therefore we avoid such “totalizing” visions in favor of much more modest attempts at connecting things better.

Meaningfulness versus Informativeness

The Decoding Reality book is a classic contemporary analysis of the foundations of physics and the implications for the human world. The scientists don’t see that physics and science are the infrastructure on which the human “quest for meaning” takes place. Ortega (Ortega y Gasset, died in 1955) tells us that a person is “a point of view directed at the universe.” This level of meaning cannot be reduced to bits or qubits or electrons since man is a “linguistic creature” who invents fictional stories to explain “things” that are not things.

The following dialog between Paul Davies (the outstanding science writer) and Vlatko Vedral (the distinguished physicist) gropes along on these issues: the difference between science as one kind of story and the human interpretation of life and self expressed in “tales” and parables, fictions and beliefs:

Davies: “When humans communicate, a certain quantity of information passes between them. But that information differs from the bits (or qubits) physicists normally consider, inasmuch as it possesses meaning. We may be able to quantify the information exchanged, but meaning is a qualitative property—a value—and therefore hard, maybe impossible, to capture mathematically. Nevertheless the concept of meaning obviously has, well… meaning. Will we ever have a credible physical theory of ‘meaningful information,’ or is ‘meaning’ simply outside the scope of physical science?”

Vedral: “This is a really difficult one. The success of Shannon’s formulation of ‘information’ lies precisely in the fact that he stripped it of all “meaning” and reduced it only to the notion of probability. Once we are able to estimate the probability for something to occur, we can immediately talk about its information content. But this sole dependence on probability could also be thought of as the main limitation of Shannon’s information theory (as you imply in your question). One could, for instance, argue that the DNA has the same information content inside as well as outside of a biological cell. However, it is really only when it has access to the cell’s machinery that it starts to serve its main biological purpose (i.e., it starts to make sense). Expressing this in your own words, the DNA has a meaning only within the context of a biological cell. The meaning of meaning is therefore obviously important. Though there has been some work on the theory of meaning, I have not really seen anything convincing yet. Intuitively we need some kind of a ‘relative information’ concept, information that is not only dependent on the probability, but also on its context, but I am afraid that we still do not have this.”

For a physicist, all the world is information. The universe and its workings are the ebb and flow of information. We are all transient patterns of information, passing on the recipe for our basic forms to future generations using a four-letter digital code called DNA.

See Decoding Reality.

In this engaging and mind-stretching account, Vlatko Vedral considers some of the deepest questions about the universe and considers the implications of interpreting it in terms of information. He explains the nature of information, the idea of entropy, and the roots of this thinking in thermodynamics. He describes the bizarre effects of quantum behavior—effects such as “entanglement,” which Einstein called “spooky action at a distance” and explores cutting edge work on the harnessing quantum effects in hyper-fast quantum computers, and how recent evidence suggests that the weirdness of the quantum world, once thought limited to the tiniest scales, may reach into the macro world.

Vedral finishes by considering the answer to the ultimate question: Where did all of the information in the universe come from? The answers he considers are exhilarating, drawing upon the work of distinguished physicist John Wheeler. The ideas challenge our concept of the nature of particles, of time, of determinism, and of reality itself.

Science is an “ontic” quest. Human life is an “ontological” quest. They are a “twisted pair” where each strand must be seen clearly and not confused. The content of your telephone conversation with your friend, say. is not reducible to the workings of a phone or the subtle electrical engineering and physics involved. A musical symphony is not just “an acoustical blast.”

The “meaning of meaning” is evocative and not logically expressible. There’s a “spooky action at a distance” between these levels of meaning versus information but they are different “realms” or “domains.”

Words and Reality and Change: What Is a Fluctuation?

Ludwig Boltzmann who died in 1906 was a giant in the history of physics.

His name is associated with various fields like statistical mechanics, entropy and so on.

A standard physics overview book called Introducing Quantum Theory (2007, Icon/Totem Books) shows a “cartoon” of Boltzmann which says, “I also introduced the controversial notion of fluctuations.” (page 25)

In common parlance, some common synonyms of fluctuate are oscillate, sway, swing, undulate, vibrate and waver. While all these words mean “to move from one direction to its opposite,” fluctuate suggests (sort of) constant irregular changes of level, intensity or value. Pulses and some pulsations suggest themselves as related.

Expressions like “Boltzmann brains” refer to this great physicist Boltzmann and you can find this notion described here: “Boltzmann Brain.”

Notice that the word “fluctuation” occurs four times in one of the paragraphs of the article “Boltzmann Brain,” as you can see:

“In 1931, astronomer Arthur Eddington pointed out that, because a large fluctuation is exponentially less probable than a small fluctuation, observers in Boltzmann universes will be vastly outnumbered by observers in smaller fluctuations. Physicist Richard Feynman published a similar counterargument within his widely read 1964 Feynman Lectures on Physics. By 2004, physicists had pushed Eddington’s observation to its logical conclusion: the most numerous observers in an eternity of thermal fluctuations would be minimal “Boltzmann brains” popping up in an otherwise featureless universe.”

You may remember perhaps you’ve also heard the term, perhaps on a PBS Nova episode on quantum fluctuation.

In the classic history of science book, The Merely Personal by Dr. Jeremy Bernstein (Ivan Dee, Chicago, 2001), one encounters the word fluctuation all over:

“This uniform density of matter …and fluctuations from the average are what would produce the unwanted instability.”

“So Einstein chose the cosmological constant…” (page 83 of Bernstein’s book)

Suppose we allow our minds to be restless and turn to economics to “change the lens” we are using to look at the world, since lens-changing is one of the pillars of Meta Intelligence.

What do we see?

In 1927, Keynes’s professor Arthur Cecil Pigou (died in 1959) published the famous work, Industrial Fluctuations.

In 1915, twelve years earlier, the famous Sir Dennis Holme Robertson (died in 1963) published A Study of Industrial Fluctuation.

The word fluctuation seems to be migrating to or resonating in economics.

The larger point (i.e., the Meta Intelligent one): is the use of this word a linguistic accident or fashion or is something basic being discovered about how some “things” “jump around” in the world?

Is the world seen as more “jumpy” or has it become more jumpy due to global integration or disintegration or in going to the deeper levels of physics with the replacement of a Newtonian world by an Einsteinian one?

The phenomena of change—call it “change-ology” whooshes up in front of us and a Meta Intelligent student of the world would immediately ponder fluctuations versus blips versus oscillations versus jumps and saltations (used in biology) and so on. What about pulsations? Gyrations?

This immediately places in front of you the question of the relationship of languages (words, numbers, images) to events.

The point is not to nail down some final answer. Our task here is not to delve into fields like physics or economics or whatever but to notice the very terms we are using across fields and in daily life (i.e., stock price fluctuations).

Notice, say, how the next blog post on oil price dynamics begins:

“Our oil price decomposition, reported weekly, examines what’s behind recent fluctuations in oil prices…”

The real point is to keep pondering and “sniffing” (i.e., Meta Intelligence), since MI is an awareness quest before all.

Science and Its Discontents: The Case of Natsume Sōseki (夏目 漱石) in Japan

Natsume Sōseki (夏目 漱石) is and was the most prestigious and respected novelist in modern Japan and every student has to engage with such novels of his as Botchan (坊っちゃん, “Young Master”) and Kokoro (こゝろ, or in post-war orthography こころ, “Heart”). Sōseki died in 1916.

Sōseki’s feeling that the modern world is some kind of runaway train with no brakes is expressed clearly in his 1913 novel, Kōjin (行人 , “The Wayfarer”).

One of Sōseki’s dialogues in the novel is about the current science and technology world, which was quite visible already then, and has a very nerve-racking or frightening tempo of a turbulent tsunami.

One character says:

“Now what you call insecurity is the insecurity of the entire human race, and it isn’t peculiar to you alone. Constant motion and flow is our very fate.

“Man’s insecurity stems from the advance of science. Never once has science, which never ceases to move forward, allowed us to pause. From walking to ricksha, from ricksha to carriage, from carriage to train, from train to automobile, from there on to the dirigible, further on to the airplane, and further on and on, no matter how far we may go, it won’t let us take a breath. How far it will sweep us along, nobody knows for sure. It is really frightening.”

Yes, it is frightening, indeed, I agreed.

“It is frightening because the fate that the whole of humanity will reach in several centuries, I must go through—in my own lifetime—and at that all alone. That’s why it is frightening. In short, I gather within myself the whole insecurity of the human race, and distill that insecurity down into every moment, that is the fright that I am experiencing.”

(Natsume Sōseki, Kōjin [行人], Charles E. Tuttle Company, 1991, 9th printing, page 285)

Comment: There’s no need to dismiss these feelings as Luddite. They represent a reaction to the vertiginous or dizzying pace of the modern techno-protean change machine with no pause button.

Notice that Sōseki’s life (1867-1916) is basically congruent with Globalization I (i.e., the period of 1870-1913) discussed in the previous essay on Arthur Lewis’s classic Growth and Fluctuations, 1870-1913.

Sōseki has been, like his spokesmen in the citation above, swept up into a change-storm which led to a globalization backlash from 1914-1945, the era of deglobalization. WWI is the beginning bookend of all this.

Notice that the micro world of feelings and moods in the novel are resonant with the macro world though people at a certain time, such as the Sōseki protagonists, are not rigorous or prophetic theoreticians but rather groping in the dark.

Science and Its Limits

The outstanding physics theoretician Max Tegmark of MIT tells the story of how Ernest Rutherford’s 1933 prediction about atomic energy (i.e., that is was “moonshine”)—was refuted before 24 hours had passed when Szilard (the Hungarian genius) realized that a nuclear chain reaction could be set in motion getting around Rutherford’s pessimistic prediction of only a few hours before:

“In London, where Southampton Row passes Russell Square, across from the British Museum in Bloomsbury, Leo Szilard waited irritably one gray Depression morning for the stoplight to change. A trace of rain had fallen during the night; Tuesday, September 12, 1933, dawned cool, humid and dull. Drizzling rain would begin again in early afternoon. When Szilard told the story later he never mentioned his destination that morning. He may have had none; he often walked to think. In any case another destination intervened. The stoplight changed to green. Szilard stepped off the curb. As he crossed the street time cracked open before him and he saw a way to the future, death into the world and all our woes, the shape of things to come…”

(Richard Rhodes, The Making of the Atomic Bomb)

This Tegmark/Szilard “refutation” of Rutherford in our times reminds one of MIT’s AI pioneer, Prof. Marvin Minsky’s limitless and perhaps too rosy predictions for AI and human intelligence in the sixties and seventies.

A student pursuing education has to live with the paradox and puzzle that unpredicted surprises and leaps do occur in the world of science and they are astonishing. It is true at the same time, that the realm of science (i.e., “how” questions) cannot address “why” questions. The question “how was I born?” cannot replace “why was I born?”

Both of these questions have possible answers at various levels and are subject to hierarchies.

Steven Jay Gould, the late Harvard biologist, had a felicitous phrase, “separate magisteria” (i.e., separate realms or domains) to describe this gap between the pursuit of personal meaning (human quest) and the pursuit of (tentative) accuracy (scientific quest).