William M. Briggs

Statistician to the Stars!

Page 145 of 684

Pascal’s Pensées, A Tour: II

PascalSince our walk through Summa Contra Gentiles is going so well, why not let’s do the same with Pascal’s sketchbook on what we can now call Thinking Thursdays. We’ll use the Dutton Edition, freely available at Project Gutenberg. (I’m removing that edition’s footnotes.)

Previous post.

2

There are different kinds of right understanding; some have right understanding in a certain order of things, and not in others, where they go astray. Some draw conclusions well from a few premises, and this displays an acute judgment.

Others draw conclusions well where there are many premises.

For example, the former easily learn hydrostatics, where the premises are few, but the conclusions are so fine that only the greatest acuteness can reach them.

And in spite of that these persons would perhaps not be great mathematicians, because mathematics contain a great number of premises, and there is perhaps a kind of intellect that can search with ease a few premises to the bottom, and cannot in the least penetrate those matters in which there are many premises.

There are then two kinds of intellect: the one able to penetrate acutely and deeply into the conclusions of given premises, and this is the precise intellect; the other able to comprehend a great number of premises without confusing them, and this is the mathematical intellect. The one has force and exactness, the other comprehension. Now the one quality can exist without the other; the intellect can be strong and narrow, and can also be comprehensive and weak.1

3

Those who are accustomed to judge by feeling do not understand the process of reasoning, for they would understand at first sight, and are not used to seek for principles. And others, on the contrary, who are accustomed to reason from principles, do not at all understand matters of feeling, seeking principles, and being unable to see at a glance.2

———————————————————————————————

1A tacit premise here are the levels of intellectual ability. Einstein was famously considered by his peers to be less than an immaculate mathematician. But Einstein’s mathematical skills, relatively (get it? get it?) poor as they were, were orders of magnitude greater than, say, those of the homme moyen. I think what Pascal means is that in any of us one form of thinking dominates the other, and perhaps this isn’t by choice: he cannot be saying that each of us is either a great mathematician or a terrific inductionist.

The two manners of thinking reminds of the little science fiction I remember. Asimov’s men versus his robots, which he always had men call “logical, not reasonable.” Many see this as a goal. Peter Kreeft warns of the consequence: (in Socratic Logic, p. 35) “a new species of human mind has appeared: one that does not know the difference between a human mind and a computer”. Who would aspire to be a walking calculator?

2I prefer induction over intuition, inductive over intuitive, and so forth. Our culture with just suspicion looks down on “intuitionist” modes of thinking, for this is where “feelings” reign and charlatans of every stripe live. Robotics is promoted, philosophy maligned, or rather it is aligned to robotics. But our “feelings” are not Pascal’s “feelings.” Because we don’t keep this straight, we incorrectly condemn, or rather fail to reward, inductivist thinking. Deepak Chopra in an intuitionist; Einstein was an inductivist.

What Pascal shows is that there is a touch of truth to the academic “different ways of knowing” fraud. Some of us come at the truth at once, and some by formal rigorous calculation. Surely the path taken does not matter if one arrives at the proper destination?

Except the difficultly is that not all truths can be reached by calculation. Rigorous calculation proves this! Right, Gödel? Empiricism is circular and all mathematics, logic, and rationality must begin on inductivist grounds. Inductivist thinking is thus superior because it holds and discovers greater truths.

Quoting in Groarke p. 293): “‘Not to know of what things one may demand a demonstration, and of what things one may not’ is, for Aristotle, to lack education.” And (pp. 298-299): “If modern philosophers have generally focused on logic and deduction as the most authoritative source of knowledge, Thomas [Aquinas], following after Plotinous, considers intellectus or “understanding” to be a higher form of knowing.”

Intellection, or inductivism, is to know “through a species of immediate, instantaneous illumination.” It is to see the universal in the particular, to see the essence in an example. It is a gift.

On Thomas’ account, what we call reasoning [ratio] is, in fact, an inferior form of knowing. We do not immediately grasp the implications of the facts that we know. We are not intelligent enough for that! Because we lack intelligence, we must reason through a middle term [as in syllogisms]. Discovering the truth requires effort. We need an aid, a crutch. This is what logic provides. Because “of the dimness of the intellectual light in [our] souls,” we must reason things out, moving step by step from premise, to premise, to conclusion. We see part of the truth and use it to logically calculate another part of it. Although Thomas could be said to devote his whole theological and philosophical career to logical argument, he himself recognized that this form on insight is inferior.

Warning Any commentary on global warming in this post will be deleted.

The Moral Case For Fossil Fuels Reviewed

His fig.

His fig.

People first

Just in time for the Federally-Recognized-Holiday-Of-25-December-That-Shall-Go-Unnamed we have a suitable gift for science deniers everywhere. Alex Epstein’s The Moral Case For Fossil Fuels

Science deniers? Yes, sir. Those who deny the science that failed forecasts imply failed theories, and those that claim fossil fuels have been more harmful than good to the human race. Invariably and amusingly, these deniers use fossil fuel-derived technologies, like computers, to make their claims. But we don’t expect rationality from deniers, do we?

Deniers is as stupid a word as it sounds, folks. Be embarrassed for whomever uses it. To the book!

Here is a scientific near certainty: if we were to curtail dramatically the use of fossil fuels, the world would be destroyed. Here is a scientific probability: that if we do not dramatically curtail the use of fossil fuels, the world might be inconvenienced by global warming.

The chance of heat doom is exceedingly small both because the models which predict this devastation have proved themselves incapable of making skillful forecasts, thus there is little evidence of such a calamity, and because humans are clever at adapting to changes in the environment, proved by you sitting at your computer reading this. But the chance that we fall into chaos and death by the removal of oil is so certain as to be almost a truism.

Incidentally, when Epstein uses world he means human beings, their livelihoods and culture and not anything else. Aren’t these the most important things on the planet? Doesn’t the welfare of human beings trump every other consideration?

If you say “yes”, you are sane. If not, not—and probably even a little dangerous. If you would choose the life of a tree or snail over a person, then you are fundamentally broken. Many are. And it is this divide—people versus everything else—which most interests Epstein. This is why he says correctly things like “the 50-95 percent bans [of fossil fuels] over the next several decades that have been proposed, is a guaranteed death sentence for billions—we would be willing to accept ten times more hurricanes if we had to.” And “The less [oil] we produce, the more preventable suffering and death will exist. To not use fossil fuels, therefore, is beyond a risk—it is certain moral peril for mankind.”

Wait. Who endorses 95-percent bans? Bill McKibben, for one. And don’t forget John Holdren, President Obama’s science adviser, called McKibben “the nation’s leading environmentalist.”

I was thrilled to read that Epstein understood “hindcasting” is not a measure of future forecast skill. He knows “a model is not valid until it makes real, forward predictions“, a once prominent scientific precept now abandoned for obvious political reasons. Epstein also knows that “every prediction of drastic future consequences is based on speculative models that have failed to predict the climate trend so far and that speculate a radically different trend than what has actually happened in the last thirty to eights years of emitting substantial amounts of CO2.” I wept when I read that.

Here’s more science Epstein knows: “What’s most striking is that these extremely positive plant effects of CO2 are scientifically uncontroversial yet practically never mentioned, even by the climate science community. This is a dereliction of duty.” I once mentioned this to a prominent scientist during a television interview at the Madrid Royal Science Academy. The scientist went apoplectic, said it wasn’t, couldn’t be true! Yeesh. Try taking carbon dioxide from plants and see how many bowls of Corn Flakes you can fill.

A thousand words

The book is filled with wisdom, but my favorite story is the (admittedly cartoon-like) graph which appears at the top of this post. Unquestionably, CO2 output from humans is on the increase. But look at what tremendous benefits that arise from this! Life expectancy is soaring, and so is our ability to create things. Population is also increasing.

“Briggs, you fool! Population increase is a bad thing!”

Why? Don’t you like people? Are you a hater? Those who misunderstand Malthus never get this right. It is increasing food supplies and other creature comforts provided by oil that caused the population increase (and now decrease in birth rates in Western nations). “To put it bluntly, in our ‘natural climate,’ absent technology, human beings are as sick as dogs and drop like flies.” If there wasn’t enough food, then there wouldn’t be new people. That’s that. That people can’t see this seems to be one of those uncorrectable errors, an error which is a central tenet of enviro-religion.

A gas fire would warm them nicely.

A natural gas fire would warm them nicely.

Resources

Epstein emphasizes that resources are made, not discovered. Gaseous and liquid pockets of stuff underground amount to nothing until we turn them into useful things. “Oil is the most coveted (and controversial) fuel in the world because it is almost eerily engineered by natural processes, not just for cheapness, not just for reliability, not just for scalability, but also for another characteristic crucial to a functional civilization: portability.” And “It’s true that once we burn a barrel of oil, it’s gone. But it’s also true that human ingenuity can dramatically increase the amount of coal, oil, or gas that is available.” Like by the new f-word, fracking.

“For something to be cheap and plentiful, every part of the process to produce it, including every input that goes into it, must be cheap and plentiful.” This is not so for wind and solar energy, nor biofuels. The first two are not reliable, are expensive, variable and unpredictable. The last sacrifices food for expensive feel-good car fuel. How about hydropower, then? Why not! It’s clean, sure, and efficient? Oops. No. “Environmental activists have spent decades shutting down as many hydroelectric dams as possible…despite hydro’s proven track record as a cheap, reliable source of CO2-free power, in the name of protecting species of fish, free-flowing rivers, and other justifications that focus on nonhuman nature.”

People are well down the list of things to protect for environmentalists.

Example? Epstein writes of a lake in China, a “vast, hissing cauldron of chemicals…seven million tons a year of mined rare earth after it has been doused in acid and chemicals…Dalahai villagers say their teeth began to fall our, their hair turned white…severe skin and respiratory disease..cancer rates rocketed.” Etc. He puts this story to college students who then call for whatever it is that caused this lake to be “banned”.

Until they learn this waste is the result of processing for solar energy materiel. Then students have appointments they suddenly remember.

Environmental “Impact”

Environmentalists want to “minimize” our “impact” on the environment. But to minimize is to eliminate, for if any man lives, even for a moment, he must necessarily “impact” the environment. Even dying “impacts”. Thus the only way to minimize is for everybody to commit suicide instantly.

Skip it. Logical arguments won’t get you far when dealing with enviro-religion, nor with the mostly well-to-do Westerners who pray at the temple of Gaia. Quoting Milton Friedman: “The rich in ancient Greece would have benefited hardly at all from modern plumbing—running servants replaced running water. Television and radio—the patricians of Rome could enjoy the leading musicians and actors in their home, could have the leads artists as domestic retainers] ” The wealthy environmentalists have their comforts, but what they can’t abide is anybody else joining their club.

“There are 7 billion people in the world, but 1.3 billion have no electricity” and another 3 billion have very little. If by allowing these people to use oil, we ever-so-slightly, and probably not at all, increase the chance of a wee increase in the temperature, one which we could well adapt to, then it’s worth it. Unless you care more about yourself than others.

Ah, but everybody already knows environmentalists love The People and hate people. Example. “Prince Philip, former head of the World Wildlife Fun, has said, ‘In the event that I am reincarnated, I would like to return as a deadly virus, in order to contribute something to solve overpopulation.'” Charming.

We need to say “loudly and proudly” that “human life is our one and only standard of value.” Yet what do oil companies do? Epstein discovered Exxon Mobil, Shell, and Chevron won’t even use the word oil on their home pages. They instead focus on “charitable contributions”, they praise their enemies as “idealistic”, they apologize for their “environmental footprint.” They play only defense. “The industry’s position amounts to this: ‘Our product isn’t moral, but it’s something that we will need for some time as we transition to the ideal fossil-free future.'”

Appeasement never works: it only exacerbates.

Four fallacies

Epstein outlines four common bad arguments (there are more). (1) “Abuse-use”: “It is irrational to say that because a technology or practice can be abused, it ought not be used.” If we followed this reasoning, we’d have to eliminate all government. Right? (2) “False-Attribution”: As in showing your water can catch fire and blaming fracking. “A more sophisticated version of false attribution uses prestigious studies based on speculative models.” Amen. (3) “No-threshold”: “A poison or pollutant is always a combination of substance and dose….People said we should have zero tolerance for radiation—not knowing, apparently, that the potassium in their bone tissue emits radiation, enough so that sleeping with a spouse gives you almost as much radiation as standing right outside a nuclear power plant.” (4) “Artificial” “Man-made.” All we need say here is Boo!

What about fusion, the epitome of clean, renewable, potentially unlimited energy? “Leading environmentalist Jeremy Rifkin: ‘It’s the worst thing that could happen to our planet.'” “Paul Erhlich: Developing fusion for human beings would be ‘like giving a machine gun to an idiot child.'” And that’s only a small sample of the appeals to emotion progressives use. Radiation! Boo!

Faults

Epstein wanders into strange territory when he says curious and false things about religions (“many religious people think that it is wrong to eat certain foods or to engage in certain sexual acts, not because there is any evidence that these foods or acts are unhealthy or otherwise harmful to human beings but simply because they believe God forbids them”), and he uses too many stacked bar and line charts, which are always a sin. But nobody bats 1.000. Buy the book.

—————————————————————————–

Mr Epstein graciously provided me with my draft copy of his book. All emphasis marks above original.

Can We Predict The Unpredictable?

Figure 1 (b) from the paper.

Figure 1 (b) from the paper.

The most intriguing thing about the new peer-reviewed paper of the same name as today’s post in Nature: Scientific Reports by Abbas Golestani and Robin Gras is that it is longer than the one word it takes to authoritatively answer the question.

No. We cannot predict the unpredictable.

Nor do we do that good a job at predicting the predictable, as anybody who has ever spent time with models of complex systems like the stock market and global climate system realize (or should realize).

Those who have long been at this game have seen every form of hype and promise come down the pike, each new method touted to fix the shortcomings of the previous wonder. Yet the glory always fades.

Anyway, our authors have devised a “novel” algorithm which they christen GenericPred, which fixes the shortcomings of models such as ARMIA, GARCH, and MLP. Before describing it, at the top of this post is a picture of one of the predictions of the Dow Jones Industrial Average.

We don’t need to understand how forecast “goodness” was defined to be able to see that GenericPred handily beats three other standard methods. Two other pictures show similar performance for other periods of the DJIA. The GenericPred also seems to do well predicting (one version of) the global average temperature anomaly (it predicts an increase of a whopping ~2.5C over the next century).

Looking only at these pictures, it appears GenericPred is hot stuff. Obviously, the old-fashioned methods bit into the seeming increase of the DJIA (pictured above) and were burnt. But not GenericPred. No, sir. It—somehow—sensed that changes were coming and, more or less, nailed the decrease.

I don’t buy it. I may be wrong—yes, dear reader, it is possible that I am mistaken—but I don’t think so. My misgivings flow from the nature of their algorithm and its relation to chaotic signals. But, hey, if I’m wrong and these guys really can predict the stock market two years in advance, they’ll be billionaires in short order and I’ll still be running this penny-ante blog.

GenericPred is based on a good idea, which I’ll roughly summarize. Take a series y1, y2, …, yT and compute some measure of chaos, like a Lyapunov exponent. Now posit new values of the series, yT+1, yT+2, …, yT+m, and then compute the (say) Lyapunov exponent for the augmented series y1, …, yT+m. Pick those values of yT+1, …, yT+m that minimize the distance between the (say) Lyapunov exponent of the original and the augmented series.

This makes some sense because, if the series is indeed chaotic, and no external changes in the causes of this series are expected, then the nature of that series, as measured by various indexes, should remain constant. That there are or can be external changes in causes is obvious, and is why the authors look before and after the last financial “crisis.”

Still with me?

Now if you have had any experience with chaotic time series, you know that, like the DJIA, they are apt to fly off hither and yon. Chaotic signals are those that are caused (as is every time series) but which are sensitive to initial conditions. The weest perturbation at the beginning (or really anywhere) could and does result in wildly different values now. This is why they are so difficult to predict.

Take another look at the prediction picture above. The data before (on or about 1 July 2007) were used to fit the various models, and the predictions came afterward. What would this plot look like if the authors had used date up to, say, 29 June 2007? The old-school models would probably still stink. But it’s not at all clear GenericPred would do as well—because the series is chaotic. Chaotic signals are just as likely to rise as fall. Very curious they got it so startlingly right.

And could something as humble as the Lyapunov exponent (or some other univariate measure) really hold the secret to all possible future values of the stock market?

Every scientist wants to think the best of his creation. Could these researchers have played (in honest earnestness) with the dates to show us the most dramatic discrepancies between their model and the old-school methods? Since these series are chaotic it would be truly remarkable if the method weren’t sensitive to the dates chosen to model and forecast.

The pictures look a little too neat, the points of departure of the old-school models and GenericPred a little too sharp. I do not suggest any nefariousness. But if this new method is to follow the history of all other methods, and prove to be not as exciting as initially promised, it will be because the authors fooled themselves by tinkering one step too many to present their best case.

Anyway, I predict at least a brief GenericPred boom among prediction clients. If the model works as well as promised by these figures, then the authors ought to get rich off the stock market.

Incidentally, all predictions methods should be accompanied by measures of uncertainty. The old-school methods automatically give these, but GenericPred does not. That should be the authors’ next step.

Update One of the authors (Gras) responded in comments below.

—————————————————————————-

Thanks to reader Rich Kyllo for bringing this paper to our attention.

Climate Change Causing Short Peruvians!

The authors' Figure 1.

The authors’ Figure 1.

Who remembers those crank scientists who wanted to genetically engineer human beings so that they would be sprier and narrower and thus have smaller “carbon footprints”? We don’t need ’em!

At least, not according to the peer-reviewed paper “El Niño adversely affected childhood stature and lean mass in northern Peru” in the new and exciting journal Climate Change Responses—it looks like it will prove fertile pickings for posts about bad statistics—by Heather Danysh and a handful of others.

Before we begin, let us warn ourselves against height triumphalism. Picking on short people is wrong and hurtful. We would never use terms like “height deficits” like the cruel authors of the paper do.

To the science! The hypothesis: “Due to severe food shortages and increased incidence of infectious diseases during El Niño, we hypothesized that children born in northern Peru during and after the 1997–1998 El Niño may be shorter for their age and sex than children born in other years.”

(Incidentally, there are more typos per paragraph in this article than in any post of mine. May give some indication of this journal’s quality.)

The authors were keen on height-for-age, or HAZ, and bioelectrical impedance. Impedance is used to guess the amount of lean mass versus water, a method which requires some form of calibration and which always gives an answer which is not certain and thus which should always be accompanied by some measure of uncertainty. This, of course, did not happen here. Keen readers should delve into the paper for their description of a “flood likelihood score” (instead of measuring whether kids lived through a flood, they calculated an index to guess: the Epidemiologist fallacy lurks).

Kids born between 1991 and 2001 were measured. The authors used a “linear mixed model” to explain “height in later childhood” on these “variables”: years between birth date and January 1991, and “the number of years between the child’s birth date and the onset of El Niño for the each child born after the onset of El Niño” in 1997.

Now 1997-1998 was, according to the Oceanic Niño Index (ONI), a strong event. But moderate events were seen in 1991-92, and 1994-95. And 1995-96 was classed as a weak La Niña event, and 1998-2000 moderate to strong La Niña.

So we have a mixture of signals here. What about dose-response? Did kids get taller during La Niñas?

The authors say, “All regression models were adjusted for sex, socioeconomic status (SES) index, and likelihood of living in a flood-prone household, and accounted for clustering using a random intercept by village.” Regression is always abused like this: regression does not prove causality. Say, does that SES index have uncertainty? Skip it.

The top of the post shows the author’s Figure 1. Does it look to you like that 1997-98 event shrunk kids (actually, the HAZ index)? Looks to be like the “rapid urbanization” the authors noted in their study village since 1991 might account for the generally increasing heights, no? And, hey, isn’t this a mean index with no indication of spread (not every kid had the same HAZ), i.e. a graph which is uncertain presented and used in analyses as if it were certain, a move which guarantees over-certainty? I’m just asking.

Actually, the authors’ model acknowledges the increasing (too sure) HAZ! Read that twice. Yet a wee p-value confirmed that kids born after the 1997-1998 event were increasing in HAZ at a rate slightly smaller than kids before it.

So even if the authors’ hypothesis is right, it’s not that El Niño shrunk kids, it only slowed the observed rate of increase. The increase is still acknowledge to be there. Popular accounts like Mother Jones‘ “Another Side Effect of Climate Change and El Niño Events? Shorter Kids” are thus wildly wrong. Who could have guessed?

But there is plenty of doubt about that hypothesis. Somehow El Niño knew only to effect lean and not fat mass of the kids. Wee p-values confirmed “children born after the onset of El Niño have significantly less lean mass than what would be expected if El Niño had not occurred” (forgetting that we didn’t carry uncertainty of the impedance, SES, or HAZ measures forward). Or maybe urbanization produced fatter, better-fed kids?

Now if the authors were to properly carry the uncertainty of all their measures forward, and they properly took into account what kids actually ate, it’s almost certain the wee p-values would become un-wee, thus canceling all headlines.

Yes, the Epidemiologist Fallacy has struck again. El Niño cannot cause shrunken kids. Starvation can—and so can a host of other things. Yet none of these things were measured here. None.

It’s well past the time for anybody to take seriously these Oh-My-God correlation studies. Science should be the search for causes, not the production of goofy statistics.

————————————————————————–

« Older posts Newer posts »

© 2016 William M. Briggs

Theme by Anders NorenUp ↑