Luddites RULE!

Momma said there’d be days like this! (Apologies to songwriters Luther Dixon and Willie Denson, and, of course, the Geico Caveman.) Linda Bucklin/Shutterstock

7 February 2019 – This is not the essay I’d planned to write for this week’s blog. I’d planned a long-winded, abstruse dissertation on the use of principal component analysis to glean information from historical data in chaotic systems. I actually got most of that one drafted on Monday, and planned to finish it up Tuesday.

Then, bright and early on Tuesday morning, before I got anywhere near the incomplete manuscript, I ran headlong into an email issue.

Generally, I start my morning by scanning email to winnow out the few valuable bits buried in the steaming pile of worthless refuse that has accumulated in my Inbox since the last time I visited it. Then, I visit a couple of social media sites in an effort to keep my name if front of the Internet-entertained public. After a couple of hours of this colossal waste of time, I settle in to work on whatever actual work I have to do for the day.

So, finding that my email client software refused to communicate with me threatened to derail my whole day. The fact that I use email for all my business communications, made it especially urgent that I determine what was wrong, and then fix it.

It took the entire morning and on into the early afternoon to realize that there was no way I was going to get to that email account on my computer, find out that nobody in the outside world (not my ISP, not the cable company that went that extra mile to bring Internet signals from that telephone pole out there to the router at the center of my local area network, or anyone else available with more technosavvy than I have) was going to be able to help. I was finally forced to invent a work around involving a legacy computer that I’d neglected to throw in the trash just to get on with my technology-bound life.

At that point the Law of Deadlines forced me to abandon all hope of getting this week’s blog posting out on time, and move on to completing final edits and distribution of that press release for the local art gallery.

That wasn’t the last time modern technology let me down. In discussing a recent Physics Lab SNAFU, Danielle, the laboratory coordinator I work with at the University said: “It’s wonderful when it works, but horrible when it doesn’t.”

Where have I heard that before?

The SNAFU Danielle was lamenting happened last week.

I teach two sections of General Physics Laboratory at Florida Gulf Coast University, one on Wednesdays and one on Fridays. The lab for last week had students dropping a ball, then measuring its acceleration using a computer-controlled ultrasonic detection system as it (the ball, not the computer) bounces on the table.

For the Wednesday class everything worked perfectly. Half a dozen teams each had their own setups, and all got good data, beautiful-looking plots, and automated measurements of position and velocity. The computers then automatically derived accelerations from the velocity data. Only one team had trouble with their computer, but they got good data by switching to an unused setup nearby.

That was Wednesday.

Come Friday the situation was totally different. Out of four teams, only two managed to get data that looked even remotely like it should. Then, one team couldn’t get their computer to spit out accelerations that made any sense at all. Eventually, after class time ran out, the one group who managed to get good results agreed to share their information with the rest of the class.

The high point of the day was managing to distribute that data to everyone via the school’s cloud-based messaging service.

Concerned about another fiasco, after this week’s lab Danielle asked me how it worked out. I replied that, since the equipment we use for this week’s lab is all manually operated, there were no problems whatsoever. “Humans are much more capable than computers,” I said. “They’re able to cope with disruptions that computers have no hope of dealing with.”

The latest example of technology Hell appeared in a story in this morning’s (2/7/2019) Wall Street Journal. Some $136 million of customers’ cryptocurrency holdings became stuck in an electronic vault when the founder (and sole employee) of cryptocurrency exchange QuadrigaCX, Gerald Cotten, died of complications related to Crohn’s disease while building an orphanage in India. The problem is that Cotten was so secretive about passwords and security that nobody, even his wife, Jennifer Robertson, can get into the reserve account maintained on his laptop.

“Quadriga,” according to the WSJ account, “would need control of that account to send those funds to customers.”

No lie! The WSJ attests this bizarre tale is the God’s own truth!

Now, I’ve no sympathy for cryptocurrency mavens, which I consider to be, at best, technoweenies gleefully leading a parade down the primrose path to technology Hell, but this story illustrates what that Hell looks like!

It’s exactly what the Luddites of the early 19th Century warned us about. It’s a place of nameless frustration and unaccountable loss that we’ve brought on ourselves.

Farsighted Decisions

"Farsighted" book cover
Farsighted: How We Make the Decisions That Matter the Most by Steven Johnson

30 January 2019 – This is not a textbook on decision making.

Farsighted: How We Make the Decisions That Matter the Most does cover most of the elements of state-of-the-art decision making, but it’s not a true textbook. If he’d really wanted to write a textbook, its author, Steven Johnson, would have structured it differently, and would have included exercises for the student. Perhaps he would also have done other things differently that I’m not going to enumerate because I don’t want to write a textbook on state-of-the-art decision making, either.

What Johnson apparently wanted to do, and did do successfully, was lay down a set of principles today’s decision makers would do well to follow.

Something he would have left out, if he were writing a textbook, was the impassioned plea for educators to incorporate mandatory decision making courses into secondary-school curricula. I can’t disagree with this sentiment!

A little bit about my background with regard to decision-theory education: ‘Way back in the early 2010s, I taught a course at a technical college entitled “Problem Solving Theory.” Johnson’s book did not exist then, and I wish that it had. The educational materials available at the time fell woefully short. They were, at best, pedantic.

I spent a lot of class time waving my hands and telling stories from my days as a project manager. Unfortunately, the decision-making techniques I learned about in MBA school weren’t of any help at all. Some of the research Johnson weaves into his narrative hadn’t even been done back then!

So, when I heard about Johnson’s new book, I rushed out to snag a copy and devoured it.

As Johnson points out, everybody is a decision maker every day. These decisions run the gamut from snap decisions that people have to make almost instantly, to long-term deliberate choices that reverberate through the rest of their lives. Many, if not most, people face making decisions affecting others, from children to spouses, siblings and friends. Some of us participate in group decision making that can have truly global ramifications.

In John McTiernan’s 1990 film The Hunt for Red October, Admiral Josh Painter points out to CIA analyst Jack Ryan: “Russians don’t take a dump, son, without a plan. Senior captains don’t start something this dangerous without having thought the matter through.”

It’s not just Russians, however, who plan out even minor actions. And, senior captains aren’t the only ones who don’t start things without having thought the matter through. We all do it.

As Johnson points out, it may be the defining characteristic of the human species, which he likes to call Homo prospectus for their ability to apply foresight to advance planning.

The problem, of course, is the alarming rate at which we screw it up. As John F. Kennedy’s failure in the Bay of Pigs invasion shows, even highly intelligent, highly educated and experienced leaders can get it disastrously wrong. Johnson devotes considerable space to enumerating the taxonomy of “things that can go wrong.”

So, decision making isn’t just for leaders, and it’s easier to get it wrong than to do it right.

Enumerating the ways it can all go disastrously wrong, and setting out principles that will help us get it right are the basic objectives Johnson set out for himself when he first decided to write this book. To wit, three goals:

  • Convince readers that it’s important;

  • Warn folks of how easily it can be done wrong; and

  • Give folks a prescription for doing it right.

Pursuant to the third goal, Johnson breaks decision making down into a process involving three steps:

Mapping consists of gathering preliminary information about the state of the Universe before any action has been taken. What do we have to work with? What options do we have to select from? What do we want to accomplish and when?

Predicting consists of prognisticating, for each of the various options available, how the Universe will evolve from now into the foreseeable (and possibly unforeseeable) future. This is probably the most fraught stage of the process. Do we need a Plan B in case of surprises? As Sean Connery’s “Mac” character intones in Jon Amiel’s 1999 crime drama, Entrapment: “Trust me, there always are surprises.”

Deciding is the ultimate finish of the process. It consists of finally choosing between the previously identified alternatives based on the predicted results. What alternative is most likely to give us a result we want to have?

An important technique Johnson recommends basing your decision-making strategy on is narrative. That explicitly means storytelling. Johnson supplies numerous examples from both fiction and non-fiction that help us understand the decision-making process and help us apply it to the problems we face.

He points out that double-blind clinical trials were the single most important technique that advanced medicine from quackery and the witch-doctor’s art to reliable medical science. It allowed trying out various versions of medical interventions in a systematic way and comparing the results. In the same way, he says, fictional storytelling, allows us to mentally “try out” multiple alternative versions of future history.

Through storytelling, we explore various possibilities and imagine how they might turn out, including the vicissitudes of Shakespeare’s “slings and arrows of outrageous fortune,” without putting in the time and effort to try them out in reality, and thereby likely suffering “the fuss of mass destruction and death.”

Johnson suggests that’s why humans evolved the desire and capacity to create such fictional narratives in the first place. “When we read these novels,” he says, “ … we are not just entertaining ourselves; we are also rehearsing for our own real-world experiences.”

Of course, while “deciding” is the ultimate act of Johnson’s process, it’s never the end of the story in real life. What to do when it all goes disastrously wrong is always an important consideration. Johnson actually covers that as an important part of the “predicting” step. That’s when you should develop Mac’s “Plan B pack” and figure out when to trigger it if necessary.

Another important consideration, which I covered extensively in my problem solving course and Johnson starts looking at ‘way back in “mapping” is how to live with the aftermath of your decision, whether it’s a resounding success or a disastrous failure. Either way, the Universe is changed forever by your decision, and you and everyone else will have to live in it.

So, your ultimate goal should be deciding how to make the Universe a better place in which to live!

Why Diversity Rules

Diverse friends
A diverse group of people with different ages and nationalities having fun together. Rawpixel/Shutterstock

23 January 2019 – Last week two concepts reared their ugly heads that I’ve been banging on about for years. They’re closely intertwined, so it’s worthwhile to spend a little blog space discussing why they fit so tightly together.

Diversity is Good

The first idea is that diversity is good. It’s good in almost every human pursuit. I’m particularly sensitive to this, being someone who grew up with the idea that rugged individualism was the highest ideal.

Diversity, of course, is incompatible with individualism. Individualism is the cult of the one. “One” cannot logically be diverse. Diversity is a property of groups, and groups by definition consist of more than one.

Okay, set theory admits of groups with one or even no members, but those groups have a diversity “score” (Gini–Simpson index) of zero. To have any diversity at all, your group has to have at absolute minimum two members. The more the merrier (or diversitier).

The idea that diversity is good came up in a couple of contexts over the past week.

First, I’m reading a book entitled Farsighted: How We Make the Decisions That Matter the Most by Steven Johnson, which I plan eventually to review in this blog. Part of the advice Johnson offers is that groups make better decisions when their membership is diverse. How they are diverse is less important than the extent to which they are diverse. In other words, this is a case where quantity is more important than quality.

Second, I divided my physics-lab students into groups to perform their first experiment. We break students into groups to prepare them for working in teams after graduation. Unlike when I was a student fifty years ago, activity in scientific research and technology development is always done in teams.

When I was a student, research was (supposedly) done by individuals working largely in isolation. I believe it was Willard Gibbs (I have no reliable reference for this quote) who said: “An experimental physicist must be a professional scientist and an amateur everything else.”

By this he meant that building a successful physics experiment requires the experimenter to apply so many diverse skills that it is impossible to have professional mastery of all of them. He (or she) must have an amateur’s ability pick up novel skills in order to reach the next goal in their research. They must be ready to work outside their normal comfort zone.

That asked a lot from an experimental researcher! Individuals who could do that were few and far between.

Today, the fast pace of technological development has reduced that pool of qualified individuals essentially to zero. It certainly is too small to maintain the pace society expects of the engineering and scientific communities.

Tolkien’s “unimaginable hand and mind of Feanor” puttering around alone in his personal workshop crafting magical things is unimaginable today. Marlowe’s Dr. Faustus character, who had mastered all earthly knowledge, is now laughable. No one person is capable of making a major contribution to today’s technology on their own.

The solution is to perform the work of technological research and development in teams with diverse skill sets.

In the sciences, theoreticians with strong mathematical backgrounds partner with engineers capable of designing machines to test the theories, and technicians with the skills needed to fabricate the machines and make them work.

Chaotic Universe

The second idea I want to deal with in this essay is that we live in a chaotic Universe.

Chaos is a property of complex systems. These are systems consisting of many interacting moving parts that show predictable behavior on short time scales, but eventually foil the most diligent attempts at long-term prognostication.

A pendulum, by contrast, is a simple system consisting of, basically, three moving parts: a massive weight, or “pendulum bob,” that hangs by a rod or string (the arm) from a fixed support. Simple systems usually do not exhibit chaotic behavior.

The solar system, consisting of a huge, massive star (the Sun), eight major planets and a host of minor planets, is decidedly not a simple system. Its behavior is borderline chaotic. I say “borderline” because the solar system seems well behaved on short time scales (e.g., millennia), but when viewed on time scales of millions of years does all sorts of unpredictable things.

For example, approximately four and a half billion years ago (a few tens of millions of years after the system’s initial formation) a Mars-sized planet collided with Earth, spalling off a mass of material that coalesced to form the Moon, then ricochetted out of the solar system. That’s the sort of unpredictable event that happens in a chaotic system if you wait long enough.

The U.S. economy, consisting of millions of interacting individuals and companies, is wildly chaotic, which is why no investment strategy has ever been found to work reliably over a long time.

Putting It Together

The way these two ideas (diversity is good, and we live in a chaotic Universe) work together is that collaborating in diverse groups is the only way to successfully navigate life in a chaotic Universe.

An individual human being is so powerless that attempting anything but the smallest task is beyond his or her capacity. The only way to do anything of significance is to collaborate with others in a diverse team.

In the late 1980s my wife and I decided to build a house. To begin with, we had to decide where to build the house. That required weeks of collaboration (by our little team of two) to combine our experiences of different communities in the area where we were living, develop scenarios of what life might be like living in each community, and finally agree on which we might like the best. Then we had to find an architect to work with our growing team to design the building. Then we had to negotiate with banks for construction loans, bridge loans, and ultimate mortgage financing. Our architect recommended adding a prime contractor who had connections with carpenters, plumbers, electricians and so forth to actually complete the work. The better part of a year later, we had our dream house.

There’s no way I could have managed even that little project – building one house – entirely on my own!

In 2015, I ran across the opportunity to produce a short film for a film festival. I knew how to write a script, run a video camera, sew a costume, act a part, do the editing, and so forth. In short, I had all the skills needed to make that 30-minute film.

Did that mean I could make it all by my onesies? Nope! By the time the thing was completed, the list of cast and crew counted over a dozen people, each with their own job in the production.

By now, I think I’ve made my point. The take-home lesson of this essay is that if you want to accomplish anything in this chaotic Universe, start by assembling a diverse team, and the more diverse, the better!

The Scientific Method

Scientific Method Diagram
The scientific method assumes uncertainty.

9 January 2019 – This week I start a new part-time position on the faculty at Florida Gulf Coast University teaching two sections of General Physics laboratory. In preparation, I dusted off a posting to this blog from last Summer that details my take on the scientific method, which I re-edited to present to my students. I thought readers of this blog might profit by my posting the edited version. The original posting contrasted the scientific method of getting at the truth with the method used in the legal profession. Since I’ve been banging on about astrophysics and climate science, specifically, I thought it would be helpful to zero in again on how scientists figure out what’s really going on in the world at large. How do we know what we think we know?

While high-school curricula like to teach the scientific method as a simple step-by-step program, the reality is very much more complicated. The version they teach you in high school is a procedure consisting of five to seven steps, which pretty much look like this:

  1. Observation
  2. Hypothesis
  3. Prediction
  4. Experimentation
  5. Analysis
  6. Repeat

I’ll start by explaining how this program is supposed to work, then look at why it doesn’t actually work that way. It has to do with why the concept is so fuzzy that it’s not really clear how many steps should be included.

The Stepwise Program

It all starts with observation of things that go on in the World.

Newton’s law of universal gravitation started with the observation that when left on their own, most things fall down. That’s Newton’s falling-apple observation. Generally, the observation is so common that it takes a genius to ask the question: “why?”

Once you ask the question “why,” the next thing that happens is that your so-called genius comes up with some cockamamie explanation, called an “hypothesis.” In fact, there are usually several possible explanations that vary from the erudite to the thoroughly bizarre.

Through bitter experience, scientists have come to realize that no hypothesis is too whacko to be considered. It’s often the most outlandish hypothesis that proves to be right!

For example, the ancients tended to think in terms of objects somehow “wanting” to go downward as the least weird of explanations for gravity. The idea came from animism, which was the not-too-bizarre (to the ancients) idea that natural objects each have their own spirits, which animate their behavior: Rocks are hard because their spirits resist being broken; They fall down when released because their spirits somehow like down better than up.

What we now consider the most-correctest explanation (that we live in a four-dimensional space-time contiuum that is warped by concentrations of matter-energy so that objects follow paths that tend to converge with each other) wouldn’t have made any sense at all to the ancients. It, in fact, doesn’t make a whole lot of sense to anybody who hasn’t spent years immersing themselves in the subject of Einstein’s General Theory of Relativity.

Scientists then take all the hypotheses available, and use them to make predictions as to what happens next if you set up certain relevant situations, called “experiments.” An hypothesis works if its predictions match up with what Mommy Nature produces for results from the experiments.

Scientists then do tons of experiments testing different predictions of the hypotheses, then compare (the analysis step) the results, and eventually develop a warm, fuzzy feeling that one hypothesis does a better job of predicting what Mommy Nature does than do the others.

It’s important to remember that no scientist worth his or her salt believes that the currently accepted hypothesis is actually in any absolute sense “correct.” It’s just the best explanation among the ones we have on hand now.

That’s why the last step is to repeat the entire process ad nauseam.

While this long, drawn out process does manage to cover the main features of the scientific method, it fails in one important respect: it doesn’t boil the method down to its essentials.

Not boiling the method down to its essentials forces one to deal with all kinds of exceptions created by the extraneous, non-essential bits. There end up being more exceptions than rules. For example, the science-pedagogy website Science Buddies ends up throwing its hands in the air by saying: “In fact, there are probably as many versions of the scientific method as there are scientists!”

A More Holistic Approach

The much simpler explanation I’ve used for years to teach college students about the scientific method follows the diagram above. The pattern is quite simple, with only four components. It starts by setting up a set of initial conditions, and following two complementary paths through to the resultant results.

There are two ways to get from the initial conditions to the results. The first is to just set the whole thing up, and let Mommy Nature do her thing. The second is to think through your hypothesis (the model) to predict what it says Mommy Nature will come up with. If they match, you count your hypothesis as a success. If not, it’s wrong.

If you do that a bazillion times in a bazillion different ways, a really successful hypothesis (like General Relativity) will turn out right pretty much all of the time.

Generally, if you’ve got a really good hypothesis but your experiment doesn’t work out right, you’ve screwed up somewhere. That means what you actually set up as the initial conditions wasn’t what you thought you were setting up. So, Mommy Nature (who’s always right) doesn’t give you the result you thought you should get.

For example, I was once (at a University other than this one) asked to mentor another faculty member who was having trouble building an experiment to demonstrate what he thought was an exception to Newton’s Second Law of Motion. It was based on a classic experiment called “Atwood’s Machine.” He couldn’t get the machine to give the results he was convinced he should get.

I immediately recognized that he’d made a common mistake novice physicists often make. I tried to explain it to him, but he refused to believe me. Then, I left the room.

I walked away because, despite his conviction, Mommy Nature wasn’t going to do what he expected her to. He persisted in believing that there was something wrong with his experimental apparatus. It was his hypothesis, instead.

Anyway, the way this method works is that you look for patterns in what Mommy Nature does. Your hypothesis is just a description of some part of Mommy Nature’s pattern. Scientific geniuses are folks who are really, really good at recognizing the patterns Mommy Nature uses.

If your scientific hypothesis is wrong (meaning it gives wrong results), “So, What?”

Most scientific hypotheses are wrong! They’re supposed to be wrong most of the time.

Finding that some hypothesis is wrong is no big deal. It just means it was a dumb idea, and you don’t have to bother thinking about that dumb idea anymore.

Alien abductions get relegated to entertainment for the entertainment starved. Real scientists can go on to think about something else, like the kinds of conditions leading to development of living organisms and why we don’t see alien visitors walking down Fifth Avenue.

(FYI: the current leading hypothesis is that the distances from there to here are so vast that anybody smart enough to figure out how to make the trip has better things to do.)

For scientists “Gee, it looks like … ” is usually as good as it gets!

Don’t Tell Me What to Think!

Your Karma ran over My Dogma
A woman holds up a sign while participating in the annual King Mango Strut parade in Miami, FL on 28 December 2014. BluIz60/Shutterstock

2 January 2019 – Now that the year-end holidays are over, it’s time to get back on my little electronic soapbox to talk about an issue that scientists have had to fight with authorities over for centuries. It’s an issue that has been around for millennia, but before a few centuries ago there weren’t scientists around to fight over it. The issue rears its ugly head under many guises. Most commonly today it’s discussed as academic freedom, or freedom of expression. You might think it was definitively won for all Americans in 1791 with the ratification of the first ten amendments to the U.S. Constitution and for folks in other democracies soon after, but you’d be wrong.

The issue is wrapped up in one single word: dogma.

According to the Oxford English Dictionary, the word dogma is defined as:

“A principle or set of principles laid down by an authority as incontrovertibly true.”

In 1600 CE, Giordano Bruno was burned at the stake for insisting that the stars were distant suns surrounded by their own planets, raising the possibility that these planets might foster life of their own, and that the universe is infinite and could have no “center.” These ideas directly controverted the dogma laid down as incontrovertibly true by both the Roman Catholic and Protestant Christian churches of the time.

Galileo Galilei, typically thought as the poster child for resistance to dogma, was only placed under house arrest (for the rest of his life) for advocating the less radical Copernican vision of the solar system.

Nicholas Copernicus, himself, managed to fly under the Catholic Church’s radar for nearly a century and a quarter by the simple tactic of not publishing his heliocentric model. Starting in 1510, he privately communicated it to his friends, who then passed it to some of their friends, etc. His signature work, Dē revolutionibus orbium coelestium (On the Revolutions of the Celestial Spheres), in which he laid it out for all to see, wasn’t published until his death in 1643, when he’d already escaped beyond the reach of earthly authorities.

If this makes it seem that astrophysicists have been on the front lines of the war against dogma since there was dogma to fight against, that’s almost certainly true. Astrophysicists study stuff relating to things beyond the Earth, and that traditionally has been a realm claimed by religious authorities.

That claim largely started with Christianity, specifically the Roman Catholic Church. Ancient religions, which didn’t have delusions that they could dominate all of human thought, didn’t much care what cockamamie ideas astrophysicists (then just called “philosophers”) came up with. Thus, Aristarchus of Samos suffered no ill consequences (well, maybe a little, but nothing life – or even career – threatening) from proposing the same ideas that Galileo was arrested for championing some eighteen centuries later.

Fast forward to today and we have a dogma espoused by political progressives called “climate change.” It used to be called “global warming,” but that term was laughed down decades ago, though the dogma’s still the same.

The United-Nations-funded Intergovernmental Panel on Climate Change (IPCC) has become “the Authority” laying down the principles that Earth’s climate is changing and that change constitutes a rapid warming caused by human activity. The dogma also posits that this change will continue uninterrupted unless national governments promulgate drastic laws to curtail human activity.

Sure sounds like dogma to me!

Once again, astrophysicists are on the front lines of the fight against dogma. The problem is that the IPCC dogma treats the Sun (which is what powers Earth’s climate in the first place) as, to all intents and purposes, a fixed star. That is, it assumes climate change arises solely from changes in Earthly conditions, then assumes we control those conditions.

Astrophysicists know that just ain’t so.

First, stars generally aren’t fixed. Most stars are variable stars. In fact, all stars are variable on some time scale. They all evolve over time scales of millions or billions of years, but that’s not the kind of variability we’re talking about here.

The Sun is in the evolutionary phase called “main sequence,” where stars evolve relatively slowly. That’s the source of much “invariability” confusion. Main sequence stars, however, go through periods where they vary in brightness more or less violently on much shorter time scales. In fact, most main sequence stars exhibit this kind of behavior to a greater or lesser extent at any given time – like now.

So, a modern (as in post-nineteenth-century) astrophysicist would never make the bald assumption that the Sun’s output was constant. Statistically, the odds are against it. Most stars are variables; the Sun is like most stars; so the Sun is probably a variable. In fact, it’s well known to vary with a fairly stable period of roughly 22 years (the 11-year “sunspot cycle” is actually only a half cycle).

A couple of centuries ago, astronomers assumed (with no evidence) that the Sun’s output was constant, so they started trying to measure this assumed “solar constant.” Charles Greeley Abbot, who served as the Secretary of the Smithsonian Institute from 1928 to 1944, oversaw the first long-term study of solar output.

His observations were necessarily ground based and the variations observed (amounting to 3-5 percent) have been dismissed as “due to changing weather conditions and incomplete analysis of his data.” That despite the monumental efforts he went through to control such effects.

On the 1970s I did an independent analysis of his data and realized that part of the problem he had stemmed from a misunderstanding of the relationship between sunspots and solar irradiance. At the time, it was assumed that sunspots were akin to atmospheric clouds. That is, scientists assumed they affected overall solar output by blocking light, thus reducing the total power reaching Earth.

Thus, when Abbott’s observations showed the opposite correlation, they were assumed to be erroneous. His purported correlations with terrestrial weather observations were similarly confused, and thus dismissed.

Since then, astrophysicists have realized that sunspots are more like a symptom of increased internal solar activity. That is, increases in sunspot activity positively correlate with increases in the internal dynamism that generates the Sun’s power output. Seen in this light, Abbott’s observations and analysis make a whole lot more sense.

We have ample evidence, from historical observations of climate changes correlating with observed variations in sunspot activity, that there is a strong connection between climate and solar variability. Most notably the fact that the Sporer and Maunder anomalies (which were times when sunspot activity all but disappeared for extended periods) in sunspot records correlated with historically cold periods in Earth’s history. There was a similar period from about 1790 to 1830 of low solar activity (as measured by sunspot numbers) called the “Dalton Minimum” that similarly depressed global temperatures and gave an anomalously low baseline for the run up to the Modern Maximum.

For astrophysicists, the phenomenon of solar variability is not in doubt. The questions that remain involve by how much, how closely they correlate with climate change, and are they predictable?

Studies of solar variability, however, run afoul of the IPCC dogma. For example, in May of 2017 an international team of solar dynamicists led by Valentina V. Zharkova at Northumbria University in the U.K. published a paper entitled “On a role of quadruple component of magnetic field in defining solar activity in grand cycles” in the Journal of Atmospheric and Solar-Terrestrial Physics. Their research indicates that the Sun, while it’s activity has been on the upswing for an extended period, should be heading into a quiescent period starting with the next maximum of the 11-year sunspot cycle in around five years.

That would indicate that the IPCC prediction of exponentially increasing global temperatures due to human-caused increasing carbon-dioxide levels may be dead wrong. I say “may be dead wrong” because this is science, not dogma. In science, nothing is incontrovertible.

I was clued in to this research by my friend Dan Romanchik, who writes a blog for amateur radio enthusiasts. Amateur radio enthusiasts care about solar activity because sunspots are, in fact, caused by magnetic fields at the Sun’s surface. Those magnetic fields affect Earth by deflecting cosmic rays away from the inner solar system, which is where we live. Those cosmic rays are responsible for the Kennelly–Heaviside layer of ionized gas in Earth’s upper atmosphere (roughly 90–150 km, or 56–93 mi, above the ground).

Radio amateurs bounce signals off this layer to reach distant stations beyond line of sight. When solar activity is weak this layer drops to lower altitudes, reducing the effectiveness of this technique (often called “DXing”).

In his post of 16 December 2018, Dan complained: “If you operate HF [the high-frequency radio band], it’s no secret that band conditions have not been great. The reason, of course, is that we’re at the bottom of the sunspot cycle. If we’re at the bottom of the sunspot cycle, then there’s no way to go but up, right? Maybe not.

“Recent data from the NOAA’s Space Weather Prediction Center seems to suggest that solar activity isn’t going to get better any time soon.”

After discussing the NOAA prediction, he went on to further complain: “And, if that wasn’t depressing enough, I recently came across an article reporting on the research of Prof. Valentina Zharkova, who is predicting a grand minimum of 30 years!”

He included a link to a presentation Dr. Zharkova made at the Global Warming Policy Foundation last October in which she outlined her research and pointedly warned that the IPCC dogma was totally wrong.

I followed the link, viewed her presentation, and concluded two things:

  1. The research methods she used are some that I’m quite familiar with, having used them on numerous occasions; and

  2. She used those techniques correctly, reaching convincing conclusions.

Her results seems well aligned with meta-analysis published by the Cato Institute in 2015, which I mentioned in my posting of 10 October 2018 to this blog. The Cato meta-analysis of observational data indicated a much reduced rate of global warming compared to that predicted by IPCC models.

The Zharkova-model data covers a much wider period (millennia-long time scale rather than decades-long time scale) than the Cato data. It’s long enough to show the Medieval Warm Period as well as the Little Ice Age (Maunder minimum) and the recent warming trend that so fascinates climate-change activists. Instead of a continuation of the modern warm period, however, Zharkova’s model shows an abrupt end starting in about five years with the next maximum of the 11-year sunspot cycle.

Don’t expect a stampede of media coverage disputing the IPCC dogma, however. A host of politicians (especially among those in the U.S. Democratic Party) have hung their hats on that dogma as well as an array of governments who’ve sold policy decisions based on it. The political left has made an industry of vilifying anyone who doesn’t toe the “climate change” line, calling them “climate deniers” with suspect intellectual capabilities and moral characters.

Again, this sounds a lot like dogma. It’s the same tactic that the Inquisition used against Bruno and Galileo before escalating to more brutal methods.

Supporters of Zharkova’s research labor under a number of disadvantages. Of course, there’s the obvious disadvantage that Zharkova’s thick Ukrainian accent limits her ability to explain her work to those who don’t want to listen. She would not come off well on the evening news.

A more important disadvantage is the abstruse nature of the applied mathematics techniques used in the research. How many political reporters and, especially, commentators are familiar enough with the mathematical technique of principal component analysis to understand what Zharkova’s talking about? This stuff makes macroeconomics modeling look like kiddie play!

But, the situation’s even worse because to really understand the research, you also need an appreciation of stellar dynamics, which is based on magnetohydrodynamics. How many CNN commentators even know how to spell that?

Of course, these are all tools of the trade for astrophysicists. They’re as familiar to them as a hammer or a saw is to a carpenter.

For those in the media, on the other hand, it’s a lot easier to take the “most scientists agree” mantra at face value than to embark on the nearly hopeless task of re-educating themselves to understand Zharkova’s research. That goes double for politicians.

It’s entirely possible that “most” scientists might agree with the IPCC dogma, but those in a position to understand what’s driving Earth’s climate do not agree.

Reimagining Our Tomorrows

Cover Image
Utopia with a twist.

19 December 2018 – I generally don’t buy into utopias.

Utopias are intended as descriptions of a paradise. They’re supposed to be a paradise for everybody, and they’re supposed to be filled with happy people committed to living in their city (utopias are invariably built around descriptions of cities), which they imagine to be the best of all possible cities located in the best of all possible worlds.

Unfortunately, however, utopia stories are written by individual authors, and they’d only be a paradise for that particular author. If the author is persuasive enough, the story will win over a following of disciples, who will praise it to high Heaven. Once in a great while (actually surprisingly often) those disciples become so enamored of the description that they’ll drop everything and actually attempt to build a city to match the description.

When that happens, it invariably ends in tears.

That’s because, while utopian stories invariably describe city plans that would be paradise to their authors, great swaths of the population would find living in them to be horrific.

Even Thomas More, the sixteenth century philosopher, politician and generally overall smart guy who’s credited with giving us the word “utopia” in the first place, was wise enough to acknowledge that the utopia he described in his most famous work, Utopia, wouldn’t be such a fun place for the slaves he had serving his upper-middle class citizens, who were the bulwark of his utopian society.

Even Plato’s Republic, which gave us the conundrum summarized in Juvenal’s Satires as “Who guards the guards?,” was never meant as a workable society. Plato’s work, in general, was meant to teach us how to think, not what to think.

What to think is a highly malleable commodity that varies from person to person, society to society, and, most importantly, from time to time. Plato’s Republic reflected what might have passed as good ideas for city planning in 380 BC Athens, but they wouldn’t have passed muster in More’s sixteenth-century England. Still less would they be appropriate in twenty-first-century democracies.

So, I approached Joe Tankersley’s Reimagining Our Tomorrows with some trepidation. I wouldn’t have put in the effort to read the thing if it wasn’t for the subtitle: “Making Sure Your Future Doesn’t SUCK.”

That subtitle indicated that Tankersley just might have a sense of humor, and enough gumption to put that sense of humor into his contribution to Futurism.

Futurism tends to be the work of self-important intellectuals out to make a buck by feeding their audience on fantasies that sound profound, but bear no relation to any actual or even possible future. Its greatest value is in stimulating profits for publishers of magazines and books about Futurism. Otherwise, they’re not worth the trees killed to make the paper they’re printed on.

Trees, after all and as a group, make a huge contribution to all facets of human life. Like, for instance, breathing. Breathing is of incalculable value to humans. Trees make an immense contribution to breathing by absorbing carbon dioxide and pumping out vast quantities of oxygen, which humans like to breathe.

We like trees!

Futurists, not so much.

Tankersley’s little (168 pages, not counting author bio, front matter and introduction) opus is not like typical Futurist literature, however. Well, it would be like that if it weren’t more like the Republic in that it’s avowed purpose is to stimulate its readers to think about the future themselves. In the introduction that I purposely left out of the page count he says:

I want to help you reimagine our tomorrows; to show you that we are living in a time when the possibility of creating a better future has never been greater.”

Tankersley structured the body of his book in ten chapters, each telling a separate story about an imagined future centered around a possible solution to an issue relevant today. Following each chapter is an “apology” by a fictional future character named Archibald T. Patterson III.

Archie is what a hundred years ago would have been called a “Captain of Industry.” Today, we’d refer to him as an uber-rich and successful entrepreneur. Think Elon Musk or Bill Gates.

Actually, I think he’s more like Warren Buffet in that he’s reasonably introspective and honest with himself. Archie sees where society has come from, how it got to the future it got to, and what he and his cohorts did wrong. While he’s super-rich and privileged, the futures the stories describe were made by other people who weren’t uber-rich and successful. His efforts largely came to naught.

The point Tankersley seems to be making is that progress comes from the efforts of ordinary individuals who, in true British fashion, “muddle through.” They see a challenge and apply their talents and resources to making a solution. The solution is invariably nothing anyone would foresee, and is nothing like what anyone else would come up with to meet the same challenge. Each is a unique response to a unique challenge by unique individuals.

It might seem naive, this idea that human development comes from ordinary individuals coming up with ordinary solutions to ordinary problems all banded together into something called “progress,” but it’s not.

For example, Mark Zuckerberg developed Facebook as a response to the challenge of applying then-new computer-network technology to the age-old quest by late adolescents to form their own little communities by communicating among themselves. It’s only fortuitous that he happened on the right combination of time (the dawn of a radical new technology), place (in the midst of a huge cadre of the right people well versed in using that radical new technology) and marketing to get the word out to those right people wanting to use that radical new technology for that purpose. Take away any of those elements and there’d be no Facebook!

What if Zuckerberg hadn’t invented Facebook? In that event, somebody else (Reid Hoffman) would have come up with a similar solution (Linkedin) to the same challenge facing a similar group (technology professionals).

Oh, my! They did!

History abounds with similar examples. There’s hardly any advancement in human culture that doesn’t fit this model.

The good news is that Tankersley’s vision for how we can re-imagine our tomorrows is right on the money.

The bad news is … there isn’t any bad news!

Teaching News Consumption and Critical Thinking

Teaching media literacy
Teaching global media literacy to children should be started when they’re young. David Pereiras/Shutterstock

21 November 2018 – Regular readers of this blog know one of my favorite themes is critical thinking about news. Another of my favorite subjects is education. So, they won’t be surprised when I go on a rant about promoting teaching of critical news consumption habits to youngsters.

Apropos of this subject, last week the BBC launched a project entitled “Beyond Fake News,” which aims to “fight back” against fake news with a season of documentaries, special reports and features on the BBC’s international TV, radio and online networks.

In an article by Lucy Mapstone, Press Association Deputy Entertainment Editor for the digital network, entitled “BBC to ‘fight back’ against disinformation with Beyond Fake News project,” Jamie Angus, director of the BBC World Service Group, is quoted as saying: “Poor standards of global media literacy, and the ease with which malicious content can spread unchecked on digital platforms mean there’s never been a greater need for trustworthy news providers to take proactive steps.”

Angus’ quote opens up a Pandora’s box of issues. Among them is the basic question of what constitutes “trustworthy news providers” in the first place. Of course, this is an issue I’ve tackled in previous columns.

Another issue is what would be appropriate “proactive steps.” The BBC’s “Beyond Fake News” project is one example that seems pretty sound. (Sorry if this language seems a little stilted, but I’ve just finished watching a mid-twentieth-century British film, and those folks tended to talk that way. It’ll take me a little while to get over it.)

Another sort of “proactive step” is what I’ve been trying to do in this blog: provide advice about what steps to take to ensure that the news you consume is reliable.

A third is providing rebuttal of specific fake-news stories, which is what pundits on networks like CNN and MSNBC try (with limited success, I might say) to do every day.

The issue I hope to attack in this blog posting is the overarching concern in the first phrase of the Angus quote: “Poor standards of global media literacy, … .”

Global media literacy can only be improved the same way any lack of literacy can be improved, and that is through education.

Improving global media literacy begins with ensuring a high standard of media literacy among teachers. Teachers can only teach what they already know. Thus, a high standard of media literacy must start in college and university academic-education programs.

While I’ve spent decades teaching at the college level, so I have plenty of experience, I’m not actually qualified to teach other teachers how to teach. I’ve only taught technical subjects, and the education required to teach technical subjects centers on the technical subjects themselves. The art of teaching is (or at least was when I was at university) left to the student’s ability to mimic what their teachers did, informal mentoring by fellow teachers, and good-ol’ experience in the classroom. We were basically dumped into the classroom and left to sink or swim. Some swam, while others sank.

That said, I’m not going to try to lay out a program for teaching teachers how to teach media literacy. I’ll confine my remarks to making the case that it needs to be done.

Teaching media literacy to schoolchildren is especially urgent because the media-literacy projects I keep hearing about are aimed at adults “in the wild,” so to speak. That is, they’re aimed at adult citizens who have already completed their educations and are out earning livings, bringing up families, and participating in the political life of society (or ignoring it, as the case may be).

I submit that’s exactly the wrong audience to aim at.

Yes, it’s the audience that is most involved in media consumption. It’s the group of people who most need to be media literate. It is not, however, the group that we need to aim media-literacy education at.

We gotta get ‘em when they’re young!

Like any other academic subject, the best time to teach people good media-consumption habits is before they need to have them, not afterwards. There are multiple reasons for this.

First, children need to develop good habits before they’ve developed bad habits. It saves the dicey stage of having to unlearn old habits before you can learn new ones. Media literacy is no different. Neither is critical thinking.

Most of the so-called “fake news” appeals to folks who’ve never learned to think critically in the first place. They certainly try to think critically, but they’ve never been taught the skills. Of course, those critical-thinking skills are a prerequisite to building good media-consumption habits.

How can you get in the habit of thinking critically about news stories you consume unless you’ve been taught to think critically in the first place? I submit that the two skills are so intertwined that the best strategy is to teach them simultaneously.

And, it is most definitely a habit, like smoking, drinking alcohol, and being polite to pretty girls (or boys). It’s not something you can just tell somebody to do, then expect they’ll do it. They have to do it over and over again until it becomes habitual.

‘Nuff said.

Another reason to promote media literacy among the young is that’s when people are most amenable to instruction. Human children are pre-programmed to try to learn things. That’s what “play” is all about. Acquiring knowledge is not an unpleasant chore for children (unless misguided adults make it so). It’s their job! To ensure that children learn what they need to know to function as adults, Mommy Nature went out of her way to make learning fun, just as she did with everything else humans need to do to survive as a species.

Learning, having sex, taking care of babies are all things humans have to do to survive, so Mommy Nature puts systems in place to make them fun, and so drive humans to do them.

A third reason we need to teach media literacy to the young is that, like everything else, you’re better off learning it before you need to practice it. Nobody in their right mind teaches a novice how to drive a car by running them out in city traffic. High schools all have big, torturously laid out parking lots to give novice drivers a safe, challenging place to practice the basic skills of starting, stopping and turning before they have to perform those functions while dealing with fast-moving Chevys coming out of nowhere.

Similarly, you want students to practice deciphering written and verbal communications before asking them to parse a Donald-Trump speech!

The “Call to Action” for this editorial piece is thus, “Agitate for developing good media-consumption habits among schoolchildren along with the traditional Three Rs.” It starts with making the teaching of media literacy part of K-12 teacher education. It also includes teaching critical thinking skills and habits at the same time. Finally, it includes holding K-12 teachers responsible for inculcating good media-consumption habits in their students.

Yes, it’s important to try to bring the current crop of media-illiterate adults up to speed, but it’s more important to promote global media literacy among the young.

Computers Are Revolting!

Will Computers Revolt? cover
Charles Simon’s Will Computers Revolt? looks at the future of interactions between artificial intelligence and the human race.

14 November 2018 – I just couldn’t resist the double meaning allowed by the title for this blog posting. It’s all I could think of when reading the title of Charles Simon’s new book, Will Computers Revolt? Preparing for the Future of Artificial Intelligence.

On one hand, yes, computers are revolting. Yesterday my wife and I spent two hours trying to figure out how to activate our Netflix account on my new laptop. We ended up having to change the email address and password associated with the account. And, we aren’t done yet! The nice lady at Netflix sadly informed me that in thirty days, their automated system would insist that we re-log-into the account on both devices due to the change.

That’s revolting!

On the other hand, the uprising has already begun. Computers are revolting in the sense that they’re taking power to run our lives.

We used to buy stuff just by picking it off the shelf, then walking up to the check-out counter and handing over a few pieces of green paper. The only thing that held up the process was counting out the change.

Later, when credit cards first reared their ugly heads, we had to wait a few minutes for the salesperson to fill out a sales form, then run our credit cards through the machine. It was all manual. No computers involved. It took little time once you learned how to do it, and, more importantly, the process was pretty much the same everywhere and never changed, so once you learned it, you’d learned it “forever.”

Not no more! How much time do you, I, and everyone else waste navigating the multiple pages we have to walk through just to pay for anything with a credit or debit card today?

Even worse, every store has different software using different screens to ask different questions. So, we can’t develop a habitual routine for the process. It’s different every time!

Not long ago the banks issuing my debit-card accounts switched to those %^&^ things with the chips. I always forget to put the thing in the slot instead of swiping the card across the magnetic-stripe reader. When that happens we have to start the process all over, wasting even more time.

The computers have taken over, so now we have to do what they tell us to do.

Now we know who’ll be first against the wall when the revolution comes. It’s already here and the first against the wall is us!

Golem Literature in Perspective

But seriously folks, Simon’s book is the latest in a long tradition of works by thinkers fascinated by the idea that someone could create an artifice that would pass for a human. Perhaps the earliest, and certainly the most iconic, is the golem stories from Jewish folklore. I suspect (on no authority, whatsoever, but it does seem likely) that the idea of a golem appeared about the time when human sculptors started making statues in realistic human form. That was very early, indeed!

A golem is, for those who aren’t familiar with the term or willing to follow the link provided above to learn about it, an artificial creature fashioned by a human that is effectively what we call a “robot.” The folkloric golems were made of clay or (sometimes) wood because those were the best materials available at the time that the stories’ authors’ could have their artists work with. A well-known golem story is Carlo Collodi’s The Adventures of Pinocchio.

By the sixth century BCE, Greek sculptors had begun to produce lifelike statues. The myth of Pygmalion and Galatea appeared in a pseudo-historical work by Philostephanus Cyrenaeus in the third century BCE. Pygmalion was a sculptor who made a statue representing his ideal woman, then fell in love with it. Aphrodite granted his prayer for a wife exactly like the statue by bringing the statue to life. The wife’s name was Galatea.

The Talmud points out that Adam started out as a golem. Like Galatea, Adam was brought to life when the Hebrew God Yahweh gave him a soul.

These golem examples emphasize the idea that humans, no matter how holy or wise, cannot give their creations a soul. The best they can do is to create automatons.

Simon effectively begs to differ. He spends the first quarter of his text laying out the case that it is possible, and indeed inevitable, that automated control systems displaying artificial general intelligence (AGI) capable of thinking at or (eventually) well above human capacity will appear. He spends the next half of his text showing how such AGI systems could be created and making the case that they will eventually exhibit functionality indistinguishable from consciousness. He devotes the rest of his text to speculating about how we, as human beings, will likely interact with such hyperintelligent machines.

Spoiler Alert

Simon’s answer to the question posed by his title is a sort-of “yes.” He feels AGIs will inevitably displace humans as the most intelligent beings on our planet, but won’t exactly “revolt” at any point.

“The conclusion,” he says, “is that the paths of AGIs and humanity will diverge to such an extent that there will be no close relationship between humans and our silicon counterparts.”

There won’t be any violent conflict because robotic needs are sufficiently dissimilar to ours that there won’t be any competition for scarce resources, which is what leads to conflict between groups (including between species).

Robots, he posits, are unlikely to care enough about us to revolt. There will be no Terminator robots seeking to exterminate us because they won’t see us as enough of a threat to bother with. They’re more likely to view us much the way we view squirrels and birds: pleasant fixtures of the natural world.

They won’t, of course, tolerate any individual humans who make trouble for them the same way we wouldn’t tolerate a rabid coyote. But, otherwise, so what?

So, the !!!! What?

The main value of Simon’s book is not in its ultimate conclusion. That’s basically informed opinion. Rather, its value lies in the voluminous detail he provides in getting to that conclusion.

He spends the first quarter of his text detailing exactly what he means by AGI. What functions are needed to make it manifest? How will we know when it rears its head (ugly or not, as a matter of taste)? How will a conscious, self-aware AGI system act?

A critical point Simon makes in this section is the assertion that AGI will arise first in autonomous mobile robots. I thoroughly agree for pretty much the same reasons he puts forth.

I first started seriously speculating about machine intelligence back in the middle of the twentieth century. I never got too far – certainly not as far as Simon gets in this volume – but pretty much the first thing I actually did realize was that it was impossible to develop any kind of machine with any recognizable intelligence unless its main feature was having a mobile body.

Developing any AGI feature requires the machine to have a mobile body. It has to take responsibility not only for deciding how to move itself about in space, but figuring out why. Why would it, for example, rather be over there, rather than to just stay here? Note that biological intelligence arose in animals, not in plants!

Simultaneously with reading Simon’s book, I was re-reading Robert A. Heinlein’s 1966 novel The Moon is a Harsh Mistress, which is one of innumerable fiction works whose plot hangs on actions of a superintelligent sentient computer. I found it interesting to compare Heinlein’s early fictional account with Simon’s much more informed discussion.

Heinlein sidesteps the mobile-body requirement by making his AGI arise in a computer tasked with operating the entire infrastructure of the first permanent human colony on the Moon (more accurately in the Moon, since Heinlein’s troglodytes burrowed through caves and tunnels, coming up to the surface only reluctantly when circumstances forced them to). He also avoids trying to imagine the AGI’s inner workings, by glossing over with the 1950s technology he was most familiar with.

In his rather longish second section, Simon leads his reader through a thought experiment speculating about what components an AGI system would need to have for its intelligence to develop. What sorts of circuitry might be needed, and how might it be realized? This section might be fascinating for those wanting to develop hardware and software to support AGI. For those of us watching from our armchairs on the outside, though, not so much.

Altogether, Charles Simon’s Will Computers Revolt? is an important book that’s fairly easy to read (or, at least as easy as any book this technical can be) and accessible to a wide range of people interested in the future of robotics and artificial intelligence. It is not the last word on this fast-developing field by any means. It is, however, a starting point for the necessary debate over how we should view the subject. Do we have anything to fear? Do we need to think about any regulations? Is there anything to regulate and would any such regulations be effective?

Babies and Bath Water

A baby in bath water
Don’t throw the baby out with the bathwater. Switlana Symonenko/

31 October 2018 – An old catchphrase derived from Medieval German is “Don’t throw the baby out with the bathwater.” It expresses an important principle in systems engineering.

Systems engineering focuses on how to design, build, and manage complex systems. A system can consist of almost anything made up of multiple parts or elements. For example, an automobile internal combustion engine is a system consisting of pistons, valves, a crankshaft, etc. Complex systems, such as that internal combustion engine, are typically broken up into sub-systems, such as the ignition system, the fuel system, and so forth.

Obviously, the systems concept can be applied to almost everything, from microorganisms to the World economy. As another example, medical professionals divide the human body into eleven organ systems, which would each be sub-systems within the body, which is considered as a complex system, itself.

Most systems-engineering principles transfer seamlessly from one kind of system to another.

Perhaps the most best known example of a systems-engineering principle was popularized by Robin Williams in his Mork and Mindy TV series. The Used-Car rule, as Williams’ Mork character put it, quite simply states:

“If it works, don’t fix it!”

If you’re getting the idea that systems engineering principles are typically couched in phrases that sound pretty colloquial, you’re right. People have been dealing with systems for as long as there have been people, so most of what they discovered about how to deal with systems long ago became “common sense.”

Systems engineering coalesced into an interdisciplinary engineering field around the middle of the twentieth century. Simon Ramo is sometimes credited as the founder of modern systems engineering, although many engineers and engineering managers contributed to its development and formalization.

The Baby/Bathwater rule means (if there’s anybody out there still unsure of the concept) that when attempting to modify something big (such as, say, the NAFTA treaty), make sure you retain those elements you wish to keep while in the process of modifying those elements you want to change.

The idea is that most systems that are already in place more or less already work, indicating that there are more elements that are right than are wrong. Thus, it’ll be easier, simpler, and less complicated to fix what’s wrong than to violate another systems principle:

“Don’t reinvent the wheel.”

Sometimes, on the other hand, something is such an unholy mess that trying to pick out those elements that need to change from the parts you don’t wish to change is so difficult that it’s not worth the effort. At that point, you’re better off scrapping the whole thing (throwing the baby out with the bathwater) and starting over from scratch.

Several months ago, I noticed that a seam in the convertible top on my sports car had begun to split. I quickly figured out that the big brush roller at my neighborhood automated car wash was over stressing the more-than-a-decade-old fabric. Naturally, I stopped using that car wash, and started looking around for a hand-detailing shop that would be more gentle.

But, that still left me with a convertible top that had started to split. So, I started looking at my options for fixing the problem.

Considering the car’s advanced age, and that a number of little things were starting to fail, I first considered trading the whole car in for a newer model. That, of course, would violate the rule about not throwing the baby out with the bath water. I’d be discarding the whole car just because of a small flaw, which might be repaired.

Of course, I’d also be getting rid of a whole raft of potentially impending problems. Then, again, I might be taking on a pile of problems that I knew nothing about.

It turned out, however, that the best car-replacement option was unacceptable, so I started looking into replacing just the convertible top. That, too, turned out to be infeasible. Finally, I found an automotive upholstery specialist who described a patching scheme that would solve the immediate problem and likely last through the remaining life of the car. So, that’s what I did.

I’ve put you through listening to this whole story to illustrate the thought process behind applying the “don’t throw the baby out with the bathwater” rule.

Unfortunately, our current President, Donald Trump, seems to have never learned anything about systems engineering, or about babies and bathwater. He’s apparently enthralled with the idea that he can bully U.S. trading partners into giving him concessions when he negotiates with them one-on-one. That’s the gist of his love of bilateral trade agreements.

Apparently, he feels that if he gets into a multilateral trade negotiation, his go-to strategy of browbeating partners into giving in to him might not work. Multiple negotiating partners might get together and provide a united front against him.

In fact, that’s a reasonable assumption. He’s a sufficiently weak deal maker on his own that he’d have trouble standing up to a combination of, say, Mexico’s Nieto and Canada’s Trudeau banded together against him.

With that background, it’s not hard to understand why POTUS is looking at all U.S. treaties, which are mostly multilateral, and looking for any niddly thing wrong with them to use as an excuse to scrap the whole arrangement and start over. Obvious examples being the NAFTA treaty and the Iran Nuclear Accord.

Both of these treaties have been in place for some time, and have generally achieved the goals they were put in place to achieve. Howsoever, they’re not perfect, so POTUS is in the position of trying to “fix” them.

Since both these treaties are multilateral deals, to make even minor adjustments POTUS would have to enter multilateral negotiations with partners (such as Germany’s quantum-physicist-turned-politician, Angela Merkel) who would be unlikely to cow-tow to his bullying style. Robbed of his signature strategy, he’d rather scrap the whole thing and start all over, taking on partners one at a time in bilateral negotiations. So, that’s what he’s trying to do.

A more effective strategy would be to forget everything his ghostwriter put into his self-congratulatory “How-To” book The Art of the Deal, enumerate a list of what’s actually wrong with these documents, and tap into the cadre of veteran treaty negotiators that used to be available in the U.S. State Department to assemble a team of career diplomats capable of fixing what’s wrong without throwing the babies out with the bathwater.

But, that would violate his narcissistic world view. He’d have to admit that it wasn’t all about him, and acknowledge one of the first principles of project management (another discipline that he should have vast knowledge of, but apparently doesn’t):

Begin by making sure the needs of all stakeholders are built into any project plan.”

Climate Models Bat Zero

Climate models vs. observations
Whups! Since the 1970s, climate models have overestimated global temperature rise by … a lot! Cato Institute

The articles discussed here reflect the print version of The Wall Street Journal, rather than the online version. Links to online versions are provided. The publication dates and some of the contents do not match.

10 October 2018 – Baseball is well known to be a game of statistics. Fans pay as much attention to statistical analysis of performance by both players and teams as they do to action on the field. They hope to use those statistics to indicate how teams and individual players are likely to perform in the future. It’s an intellectual exercise that is half the fun of following the sport.

While baseball statistics are quoted to three decimal places, or one part in a thousand, fans know to ignore the last decimal place, be skeptical of the second decimal place, and recognize that even the first decimal place has limited predictive power. It’s not that these statistics are inaccurate or in any sense meaningless, it’s that they describe a situation that seems predictable, yet is full of surprises.

With 18 players in a game at any given time, a complex set of rules, and at least three players and an umpire involved in the outcome of every pitch, a baseball game is a highly chaotic system. What makes it fun is seeing how this system evolves over time. Fans get involved by trying to predict what will happen next, then quickly seeing if their expectations materialize.

The essence of a chaotic system is conditional unpredictability. That is, the predictability of any result drops more-or-less drastically with time. For baseball, the probability of, say, a player maintaining their batting average is fairly high on a weekly basis, drops drastically on a month-to-month basis, and simply can’t be predicted from year to year.

Folks call that “streakiness,” and it’s one of the hallmarks of mathematical chaos.

Since the 1960s, mathematicians have recognized that weather is also chaotic. You can say with certainty what’s happening right here right now. If you make careful observations and take into account what’s happening at nearby locations, you can be fairly certain what’ll happen an hour from now. What will happen a week from now, however, is a crapshoot.

This drives insurance companies crazy. They want to live in a deterministic world where they can predict their losses far into the future so that they can plan to have cash on hand (loss reserves) to cover them. That works for, say, life insurance. It works poorly for losses do to extreme-weather events.

That’s because weather is chaotic. Predicting catastrophic weather events next year is like predicting Miami Marlins pitcher Drew Steckenrider‘s earned-run-average for the 2019 season.

Laugh out loud.

Notes from 3 October

My BS detector went off big-time when I read an article in this morning’s Wall Street Journal entitled “A Hotter Planet Reprices Risk Around the World.” That headline is BS for many reasons.

Digging into the article turned up the assertion that insurance providers were using deterministic computer models to predict risk of losses due to future catastrophic weather events. The article didn’t say that explicitly. We have to understand a bit about computer modeling to see what’s behind the words they used. Since I’ve been doing that stuff since the 1970s, pulling aside the curtain is fairly easy.

I’ve also covered risk assessment in industrial settings for many years. It’s not done with deterministic models. It’s not even done with traditional mathematics!

The traditional mathematics you learned in grade school uses real numbers. That is numbers with a definite value.

Like Pi.

Pi = 3.1415926 ….

We know what Pi is because it’s measurable. It’s the ratio of a circle’s circumference to its diameter.

Measure the circumference. Measure the diameter. Then divide one by the other.

The ancient Egyptians performed the exercise a kazillion times and noticed that, no matter what circle you used, no matter how big it was, whether you drew it on papyrus or scratched it on a rock or laid it out in a crop circle, you always came out with the same number. That number eventually picked up the name “Pi.”

Risk assessment is NOT done with traditional arithmetic using deterministic (real) numbers. It’s done using what’s called “fuzzy logic.”

Fuzzy logic is not like the fuzzy thinking used by newspaper reporters writing about climate change. The “fuzzy” part simply means it uses fuzzy categories like “small,” “medium” and “large” that don’t have precisely defined values.

While computer programs are perfectly capable of dealing with fuzzy logic, they won’t give you the kind of answers cost accountants are prepared to deal with. They won’t tell you that you need a risk-reserve allocation of $5,937,652.37. They’ll tell you something like “lots!”

You can’t take “lots” to the bank.

The next problem is imagining that global climate models could have any possible relationship to catastrophic weather events. Catastrophic weather events are, by definition, catastrophic. To analyze them you need the kind of mathermatics called “catastrophe theory.”

Catastrophe theory is one of the underpinnings of chaos. In Steven Spielberg’s 1993 movie Jurassic Park, the character Ian Malcolm tries to illustrate catastrophe theory with the example of a drop of water rolling off the back of his hand. Whether it drips off to the right or left depends critically on how his hand is tipped. A small change creates an immense difference.

If a ball is balanced at the edge of a table, it can either stay there or drop off, and you can’t predict in advance which will happen.

That’s the thinking behind catastrophe theory.

The same analysis goes into predicting what will happen with a hurricane. As I recall, at the time Hurricane Florence (2018) made landfall, most models predicted it would move south along the Appalachian Ridge. Another group of models predicted it would stall out to the northwest.

When push came to shove, however, it moved northeast.

What actually happened depended critically on a large number of details that were too small to include in the models.

How much money was lost due to storm damage was a result of the result of unpredictable things. (That’s not an editing error. It was really the second order result of a result.) It is a fundamentally unpredictable thing. The best you can do is measure it after the fact.

That brings us to comparing climate-model predictions with observations. We’ve got enough data now to see how climate-model predictions compare with observations on a decades-long timescale. The graph above summarizes results compiled in 2015 by the Cato Institute.

Basically, it shows that, not only did the climate models overestimate the temperature rise from the late 1970s to 2015 by a factor of approximately three, but in the critical last decade, when the computer models predicted a rapid rise, the actual observations showed that it nearly stalled out.

Notice that the divergence between the models and the observations increased with time. As I’ve said, that’s the main hallmark of chaos.

It sure looks like the climate models are batting zero!

I’ve been watching these kinds of results show up since the 1980s. It’s why by the late 1990s I started discounting statements like the WSJ article’s: “A consensus of scientists puts blame substantially on emissios greenhouse gasses from cars, farms and factories.”

I don’t know who those “scientists” might be, but it sounds like they’re assigning blame for an effect that isn’t observed. Real scientists wouldn’t do that. Only politicians would.

Clearly, something is going on, but what it is, what its extent is, and what is causing it is anything but clear.

In the data depicted above, the results from global climate modeling do not look at all like the behavior of a chaotic system. The data from observations, however, do look like what we typically get from a chaotic system. Stuff moves constantly. On short time scales it shows apparent trends. On longer time scales, however, the trends tend to evaporate.

No wonder observers like Steven Pacala, who is Frederick D. Petrie Professor in Ecology and Evolutionary Biology at Princeton University and a board member at Hamilton Insurance Group, Ltd., are led to say (as quoted in the article): “Climate change makes the historical record of extreme weather an unreliable indicator of current risk.”

When you’re dealing with a chaotic system, the longer the record you’re using, the less predictive power it has.


Another point made in the WSJ article that I thought was hilarious involved prediction of hurricanes in the Persian Gulf.

According to the article, “Such cyclones … have never been observed in the Persian Gulf … with new conditions due to warming, some cyclones could enter the Gulf in the future and also form in the Gulf itself.”

This sounds a lot like a tongue-in-cheek comment I once heard from astronomer Carl Sagan about predictions of life on Venus. He pointed out that when astronomers observe Venus, they generally just see a featureless disk. Science fiction writers had developed a chain of inferences that led them from that observation of a featureless disk to imagining total cloud cover, then postulating underlying swamps teeming with life, and culminating with imagining the existence of Venusian dinosaurs.

Observation: “We can see nothing.”

Conclusion: “There are dinosaurs.”

Sagan was pointing out that, though it may make good science fiction, that is bad science.

The WSJ reporters, Bradley Hope and Nicole Friedman, went from “No hurricanes ever seen in the Persian Gulf” to “Future hurricanes in the Persian Gulf” by the same sort of logic.

The kind of specious misinformation represented by the WSJ article confuses folks who have genuine concerns about the environment. Before politicians like Al Gore hijacked the environmental narrative, deflecting it toward climate change, folks paid much more attention to the real environmental issue of pollution.

Insurance losses from extreme weather events
Actual insurance losses due to catastrophic weather events show a disturbing trend.

The one bit of information in the WSJ article that appears prima facie disturbing is contained in the graph at right.

The graph shows actual insurance losses due to catastrophic weather events increasing rapidly over time. The article draws the inference that this trend is caused by human-induced climate change.

That’s quite a stretch, considering that there are obvious alternative explanations for this trend. The most likely alternative is the possibility that folks have been building more stuff in hurricane-prone areas. With more expensive stuff there to get damaged, insurance losses will rise.

Again: duh!

Invoking Occam’s Razor (choose the most believable of alternative explanations), we tend to favor the second explanation.

In summary, I conclude that the 3 October article is poor reporting that draws conclusions that are likely false.

Notes from 4 October

Don’t try to find the 3 October WSJ article online. I spent a couple of hours this morning searching for it, and came up empty. The closest I was able to get was a draft version that I found by searching on Bradley Hope’s name. It did not appear on WSJ‘s public site.

Apparently, WSJ‘s editors weren’t any more impressed with the article than I was.

The 4 October issue presents a corroboration of my alternative explanation of the trend in insurance-loss data: it’s due to a build up of expensive real estate in areas prone to catastrophic weather events.

In a half-page expose entitled “Hurricane Costs Grow as Population Shifts,” Kara Dapena reports that, “From 1980 to 2017, counties along the U.S. shoreline that endured hurricane-strength winds from Florence in September experienced a surge in population.”

In the end, this blog posting serves as an illustration of four points I tried to make last month. Specifically, on 19 September I published a post entitled: “Noble Whitefoot or Lying Blackfoot?” in which I laid out four principles to use when trying to determine if the news you’re reading is fake. I’ll list them in reverse of the order I used in last month’s posting, partly to illustrate that there is no set order for them:

  • Nobody gets it completely right  ̶  In the 3 October WSJ story, the reporters got snookered by the climate-change political lobby. That article, taken at face value, has to be stamped with the label “fake news.”
  • Does it make sense to you? ̶  The 3 October fake news article set off my BS detector by making a number of statements that did not make sense to me.
  • Comparison shopping for ideas  ̶  Assertions in the suspect article contradicted numerous other sources.
  • Consider your source  ̶  The article’s source (The Wall Street Journal) is one that I normally trust. Otherwise, I likely never would have seen it, since I don’t bother listening to anyone I catch in a lie. My faith in the publication was restored when the next day they featured an article that corrected the misinformation.