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?

Radicalism and the Death of Discourse

Gaussian political spectrum
Most Americans prefer to be in the middle of the political spectrum, but most of the noise comes from the far right and far left.

7 November 2018 – During the week of 22 October 2018 two events dominated the news: Cesar Sayoc mailed fourteen pipe bombs to prominent individuals critical of Donald Trump, and Robert Bowers shot up a synagogue because he didn’t like Jews. Both of these individuals identified themselves with far-right ideology, so the media has been full of rhetoric condemning far-right activists.

To be legally correct, I have to note that, while I’ve written the above paragraph as if those individuals’ culpability for those crimes is established fact, they (as of this writing) haven’t been convicted. It’s entirely possible that some deus ex machina will appear out of the blue and exonerate one or both of them.

Clearly, things have gotten out of hand with Red Team activists when they start “throwing” pipe bombs and bullets. But, I’m here to say “naughty, naughty” to both sides.

Both sides are culpable.

I don’t want you to interpret that last sentence as agreement with Donald Trump’s idiotic statement after last year’s Charlottesville incident that there were “very fine people on both sides.”

There aren’t “very fine people” on both sides. Extremists are “bad” people no matter what side they’re on.

For example, not long ago social media sites (specifically Linkedin and, especially, Facebook) were lit up with vitriol about the Justice Kavanaugh hearings by pundits from both the Red Team and the Blue Team. It got so hot that I was embarrassed!

Some have pointed out that, statistically, most of the actual violence has been perpetrated by the Red Team.

Does that mean the Red Team is more culpable than the Blue Team?

No. It means they’re using different weapons.

The Blue Team, which I believe consists mainly of extremists from the liberal/progressive wing of the Democratic Party, has traditionally chosen written and spoken words as their main weapon. Recall some of the political correctness verbiage used to attack free expression in the late 20th Century, and demonstrations against conservative speakers on college campuses in our own.

The Red Team, which today consists of the Trumpian remnants of the Republican Party, has traditionally chosen to throw hard things, like rocks, bullets and pipe bombs.

Both sides also attempt to disarm the other side. The Blue Team wisely attempts to disarm the Red Team by taking away their guns. The Red Team, which eschews anything that smacks of wisdom, tries to disarm the Blue Team by (figuratively, so far) burning their books.

Recognize that calling the Free Press “the enemy of the people” is morally equivalent to throwing books on a bonfire. They’re both attempts to promote ignorance.

What’s actually happening is that the fringes of society are making all of the noise, and the mass of moderate-thinking citizens can’t get a word in edgewise.

George Schultz pointed out: “He who walks in the middle of the roads gets hit from both sides.”

I think it was Douglas Adams who pointed out that fanatics get to run things because they care enough to put in the effort. Moderates don’t because they don’t.

Both of these pundits point out the sad fact that Nature favors extremes. The most successful companies are those with the highest growth rates. Most drivers exceed the speed limit. The squeaky wheel gets the most grease. And, those who express the most extreme views get the most media attention.

Our Constitution specifies in no uncertain terms that the nation is founded on (small “d”) democratic principles. Democratic principles insist that policy matters be debated and resolved by consensus of the voting population. That can only be done when people meet together in the middle.

Extremists on both the Red Team and Blue Team don’t want that. They treat politics as a sporting event.

In a baseball game, for example, nobody roots for a tie. They root for a win by one team or the other.

Government is not a sporting event.

When one team or the other wins, all Americans lose.

The enemy we are facing now, which is the same enemy democracies face around the world, is not the right or left. It is extremism in general. Always has been. Always will be.

Authoritarians always go for one extreme or the other. Hitler went for the right. Stalin went for the left.

The reason authoritarians pick an extreme is that’s where there are people who are passionate enough about their ideas to shoot anyone who doesn’t agree with them. That, authoritarians realize, is the only way they can become “Dictator for Life.” Since that is their goal, they have to pick an extreme.

We love democracy because it’s the best way for “We the People” to ensure nobody gets to be “Dictator for Life.” When everyone meets in the middle (which is the only place everyone can meet), authoritarians get nowhere.

Ergo, authoritarians love extremes and everyone else needs the middle.

Vilifying “nationalism” as a Red Team vice misses the point. In the U.S. (or any similar democracy), nationalism requires more-or-less moderate political views. There’s lots of room in the middle for healthy (and ultimately entertaining) debate, but very little room at the extremes.

Try going for the middle.

To quote Victor “Animal” Palotti in Roland Emmerich’s 1998 film Godzilla: “C’mon. It’ll be fun! It’ll be fun! It’ll be fun!”

Six Tips to Protect Your Vote from Election Meddlers

Theresa Payton headshot
Theresa Payton, cybersecurity expert and CEO of Fortalice Solutions. photo courtesy Fortalice Solutions

6 November 2018 – Below is from a press release I received yesterday (Monday, 11/5) evening. It’s of sufficient import and urgent timing that I decided to post it to this blog verbatim.

There’s been a lot of talk about cybersecurity and whether or not the Trump administration is prepared for tomorrow’s midterm elections, but now that we’re down to the wire, former White House CIO and Fortalice Solutions CEO Theresa Payton says it’s time for voters to think about what they can do to make sure their voices are heard.

Theresa’s six cyber tips for voters ahead of midterms:

  • Don’t zone out while you’re voting. Pay close attention to how you cast your ballot and who you cast your ballot for.

  • Take your time during the review process, and double-check your vote before you finalize it;

  • It may sound cliche, but if you see something say something. If something seems strange, report it to your State Board of Elections immediately;

  • If you see suspicious social media personas pushing information that’s designed to influence (and maybe even misinform) voters, here’s where you can report it:

  • Check your voter registration status before you go to the polls. Voters in 37 states and the District of Columbia can register to vote online. Visit vote.org to find out how to check your registration status in your state;

  • Unless you are a resident of West Virginia or you’re serving overseas in the U.S. military, you cannot vote electronically on your phone. Protect yourself from text messages and email scams that indicate that you can. Knowledge is power.

Finally, trust the system. Yes, it’s flawed. Yes, it’s imperfect. But it’s the bedrock of our democracy. If you stay home or lose trust in the legitimacy of the process, our cyber enemies win.

Theresa is one of the nation’s leading experts in cyber security and IT strategy. She is the CEO of Fortalice Solutions, an industry-leading security consulting company. Under President George W. Bush, she served as the first female chief information officer at the White House, overseeing IT operations for POTUS and his staff. She was named #4 on IFSEC Global’s list of the world’s Top 50 cybersecurity influencers in security & fire 2017. See her profiled in the Washington Post for her role on the 2017 CBS reality show “Hunted” here.

Babies and Bath Water

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

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.”

Reaping the Whirlwind

Tornado
Powerful Tornado destroying property, with lightning in the background. Solarseven/Shutterstock.com

24 October 2018 – “They sow the wind, and they shall reap the whirlwind” is a saying from The Holy Bible‘s Old Testament Book of Hosea. I’m certainly not a Bible scholar, but, having been paying attention for seven decades, I can attest to saying’s validity.

The equivalent Buddhist concept is karma, which is the motive force driving the Wheel of Birth and Death. It is also wrapped up with samsara, which is epitomized by the saying: “What goes around comes around.”

Actions have consequences.

If you smoke a pack of Camels a day, you’re gonna get sick!

By now, you should have gotten the idea that “reaping the whirlwind” is a common theme among the world’s religions and philosophies. You’ve got to be pretty stone headed to have missed it.

Apparently the current President of the United States (POTUS), Donald J. Trump, has been stone headed enough to miss it.

POTUS is well known for trying to duck consequences of his actions. For example, during his 2016 Presidential Election campaign, he went out of his way to capitalize on Wikileaks‘ publication of emails stolen from Hillary Clinton‘s private email server. That indiscretion and his attempt to cover it up by firing then-FBI-Director James Comey grew into a Special Counsel Investigation, which now threatens to unmask all the nefarious activities he’s engaged in throughout his entire life.

Of course, Hillary’s unsanctioned use of that private email server while serving as Secretary of State is what opened her up to the email hacking in the first place! That error came back to bite her in the backside by giving the Russians something to hack. They then forwarded that junk to Wikileaks, who eventually made it public, arguably costing her the 2016 Presidential election.

Or, maybe it was her standing up for her philandering husband, or maybe lingering suspicions surrounding the pair’s involvement in the Whitewater scandal. Whatever the reason(s), Hillary, too, reaped the whirlwind.

In his turn, Russian President Vladimir Putin sowed the wind by tasking operatives to do the hacking of Hillary’s email server. Now he’s reaping the whirlwind in the form of a laundry list sanctions by western governments and Special Counsel Investigation indictments against the operatives he sent to do the hacking.

Again, POTUS showed his stone-headedness about the Bible verse by cuddling up to nearly every autocrat in the world: Vlad Putin, Kim Jong Un, Xi Jinping, … . The list goes on. Sensing waves of love emanating from Washington, those idiots have become ever more extravagant in their misbehavior.

The latest example of an authoritarian regime rubbing POTUS’ nose in filth is the apparent murder and dismemberment of Saudi Arabian journalist Jamal Khashoggi when he briefly entered the Saudi embassy in Turkey on personal business.

The most popular theory of the crime lays blame at the feet of Mohammad Bin Salman Al Saud (MBS), Crown Prince of Saudi Arabia and the country’s de facto ruler. Unwilling to point his finger at another would-be autocrat, POTUS is promoting a Saudi cover-up attempt suggesting the murder was done by some unnamed “rogue agents.”

Actually, that theory deserves some consideration. The idea that MBS was emboldened (spelled S-T-U-P-I-D) enough to have ordered Kashoggi’s assassination in such a ham-fisted way strains credulity. We should consider the possibility that ultra-conservative Wahabist factions within the Saudi government, who see MBS’ reforms as a threat to their historical patronage from the oil-rich Saudi monarchy, might have created the incident to embarrass MBS.

No matter what the true story is, the blow back is a whirlwind!

MBS has gone out of his way to promote himself as a business-friendly reformer. This reputation has persisted despite repeated instances of continued repression in the country he controls.

The whirlwind, however, is threatening MBS’ and the Saudi monarchy’s standing in the international community. Especially, international bankers, led by JP Morgan Chase’s Jamie Dimon, and a host of Silicon Valley tech companies are running for the exits from Saudi Arabia’s three-day Financial Investment Initiative conference that was scheduled to start Tuesday (23 October 2018).

That is a major embarrassment and will likely derail MBS’ efforts to modernize Saudi Arabia’s economy away from dependence on oil revenue.

It appears that these high-powered executives are rethinking the wisdom of dealing with the authoritarian Saudi regime. They’ve decided not to sow the wind by dealing with the Saudis because they don’t want to reap the whirlwind likely to result!

Update

Since this manuscript was drafted it’s become clear that we’ll never get the full story about the Kashoggi incident. Both regimes involved (Turkey and Saudi Arabia) are authoritarians with no incentive to be honest about this story. While Saudi Arabia seems to make a pretense of press freedom, this incident shows their true colors (i.e, color them repressive). Turkey hasn’t given even a passing nod to press freedom for years. It’s like two rival foxes telling the dog about a hen house break in.

On the “dog” side, we’re stuck with a POTUS who attacks press freedom on a daily basis. So, who’s going to ferret out the truth? Maybe the Brits or the French, but not the U.S. Executive Branch!

Immigration in Perspective

Day without immigrants protest
During ‘A Day Without Immigrants’ , more than 500,000 people marched down Wilshire Boulevard in Los Angeles, CA to protest a proposed federal crackdown on illegal immigration. Krista Kennell / Shutterstock.com

17 October 2018 – Immigration is, by and large, a good thing. It’s not always a good thing, and it carries with it a host of potential problems, but in general immigration is better than its opposite: emigration. And, there are a number of reasons for that.

Immigration is movement toward some place. Emigration is flow away from a place.

Mathematically, population shifts are described by a non-homogeneous second-order differential equation. I expect that statement means absolutely nothing to about half the target audience for this blog, and a fair fraction of the others have (like me) forgotten most of what they ever knew (or wanted to know) about such equations. So, I’ll start with a short review of the relevant points of how the things behave.

It’ll help the rest of this blog make a lot more sense, so bear with me.

Basically, the relevant non-homogeneous second-order differential equation is something called the “diffusion equation.” Leaving the detailed math aside, what this equation says is that the rate of migration of just about anything from one place to another depends on the spatial distribution of population density, a mobility factor, and a driving force pushing the population in one direction or the other.

Things (such as people) “diffuse” from places with higher densities to those with lower densities.

That tendency is moderated by a “mobility” factor that expresses how easy it is to get from place to place. It’s hard to walk across a desert, so mobility of people through a desert is low. Similarly, if you build a wall across the migration path, that also reduces mobility. Throwing up all kinds of passport checks, visas and customs inspections also reduces mobility.

Giving people automobiles, buses and airplanes, on the other hand, pushes mobility up by a lot!

But, changing mobility only affects the rate of flow. It doesn’t do anything to change the direction of flow, or to actually stop it. That’s why building walls has never actually worked. It didn’t work for the First Emperor of China. It didn’t work for Hadrian. It hasn’t done much for the Israelis, either.

Direction of flow is controlled by a forcing term. Existence of that forcing term is what makes the equation “non-homogeneous” rather than “homogeneous.” The homogeneous version (without the forcing term) is called the “heat equation” because it models what dumb-old thermal energy does.

Things that can choose what to do (like people), and have feet to help them act on their choices, get to “vote with their feet.” That means they can go where they want, instead of always floating downstream like a dead leaf.

The forcing term largely accounts for the desirability of being in one place instead of another. For example, the United States has a reputation for being a nice place to live. Thus, people try to flock here in droves from places that are not so nice. Thus, there’s a forcing term that points people from other places to the U.S.

That’s the big reason you want to live in a country that has immigration issues, rather than one with emigration issues. The Middle East had a serious emigration problem in 2015. For a number of reasons, it had become a nasty place to live. Folks that lived there wanted out in a big way. So, they voted with their feet.

There was a huge forcing term that pushed a million people from the Middle East to elsewhere, specifically Europe. Europe was considered a much nicer place to be, so people were willing to go through Hell to get there. Thus: emigration from the Middle East, and immigration into Europe.

In another example Nazi occupation in the first half of the twentieth century made most places in Europe distasteful, especially for certain groups of people. So, the forcing term pushed a lot of people across the Atlantic toward America. In 1942 Michael Curtiz made a film about that. It was called Casablanca and is arguably one of the greatest films Humphrey Bogart starred in.

Similarly, for decades Mexico had some serious problems with poverty, organized crime and corruption. Those are things that make a place nasty to live in, so there was a big forcing function pushing people to cross the border into the much nicer United States.

In recent decades, regime change in Mexico cleaned up a lot of the country’s problems, so migration from Mexico to the United States dropped like a stone in the last years of the Obama administration. When Mexico became a nicer place to live, people stopped wanting to move away.

Duh!

There are two morals to this story:

  1. If you want to cut down on immigration from some other country, help that other country become a nicer place to live. (Conversely, you could turn your own country into a third-world toilet so nobody wants to come in, but that’s not what we want.)
  2. Putting up walls and other barriers to immigration doesn’t stop it. They only slow it down.

We’re All Immigrants

I’d should subtitle this section, “The Bigot’s Lament.”

There isn’t a bi-manual (two-handed) biped (two-legged) creature anywhere in North or South America who isn’t an immigrant or a descendant of immigrants.

There have been two major influxes of human population in the history (and pre-history) of the Americas. The first occurred near the end of the last Ice Age, and the second occurred during the European Age of Discovery.

Before about ten-thousand years ago, there were horses, wolves, saber-tooth tigers, camels(!), elephants, bison and all sorts of big and little critters running around the Americas, but not a single human being.

(The actual date is controversial, but you get the idea.)

Anatomically modern humans, (and there aren’t any others because everyone else went extinct tens of thousands of years ago) developed in East Africa about 200,000 years ago.

They were, by the way, almost certainly negroes. A fact every racist wants to ignore is that: everybody has black ancestors! You can’t hate black people without hating your own forefathers.

More important for this discussion, however, is that every human being in North and South America is descended from somebody who came here from somewhere else. So-called “Native Americans” came here in the Pleistocene Epoch, most likely from Siberia. Most everybody else showed up after Christopher Columbus accidentally fell over North America.

That started the second big migration of people into the Americas: European colonization.

Mostly these later immigrants were imported to fill America’s chronic labor shortage.

America’s labor shortage has persisted since the Spanish conquistadores pretty much wiped out the indigenous people, leaving the Spaniards with hardly anybody to do the manual labor on which their economy depended. Waves of forced and unforced migration have never caught up. We still have a chronic labor shortage.

Immigrants generally don’t come to take jobs from “real” Americans. They come here because there are by-and-large more available jobs than workers.

Currently, natural reductions in birth rates among better educated, better housed, and generally wealthier Americans have left the United States (similar to most developed countries) with the problem that the the working-age population is declining while the older, retired population expands. That means we haven’t got enough young squirts to support us old farts in retirement.

The only viable solution is to import more young squirts. That means welcoming working-age immigrants.

End of story.

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.

Duh!

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.

News vs. Opinion

News reporting
Journalists report reopening of Lindt cafe in Sydney after ISIS siege, 20 March 2015. M. W. Hunt / Shutterstock.com

26 September 2018 – This is NOT a news story!

Last week I spent a lot of space yammering on about how to tell fake news from the real stuff. I made a big point about how real news organizations don’t allow editorializing in news stories. I included an example of a New York Times op-ed (opinion editorial) that was decidedly not a news story.

On the other hand, last night I growled at my TV screen when I heard a CNN commentator say that she’d been taught that journalists must have opinions and should voice them. I growled because her statement could be construed to mean something anathema to journalistic ethics. I’m afraid way too many TV journalists may be confused about this issue. Certainly too many news consumers are confused!

It’s easy to get confused. For example, I got myself in trouble some years ago in a discussion over dinner and drinks with Andy Wilson, Founding Editor at Vision Systems Design, over a related issue that is less important to political-news reporting, but is crucial for business-to-business (B2B) journalism: the role of advertising in editorial considerations.

Andy insisted upon strictly ignoring advertiser needs when making editorial decisions. I advocated a more nuanced approach. I said that ignoring advertiser needs and desires would lead to cutting oneself off from our most important source of technology-trends information.

I’m not going to delve too deeply into that subject because it has only peripheral significance for this blog posting. The overlap with news reporting is that both activities involve dealing with biased sources.

My disagreement with Andy arose from my veteran-project-manager’s sensitivity to all stakeholders in any activity. In the B2B case, editors have several ways of enforcing journalistic discipline without biting the hand that feeds us. I was especially sensitive to the issue because I specialized in case studies, which necessarily discuss technology embodied in commercial products. Basically, I insisted on limiting (to one) actual product mentions in each story, and suppressing any claims that the mentioned product was the only possible way to access the embodied technology. In essence, I policed the stories I wrote or edited to avoid the “buy our stuff” messages that advertisers love and that send chills down Andy’s (and my) spine.

In the news-media realm, journalists need to police their writing for “buy our ideas” messages in news stories. “Just the facts, ma’am” needs to be the goal for news. Expressing editorial opinions in news stories is dangerous. That’s when the lines between fake news and real news get blurry.

Those lines need to be sharp to help news consumers judge the … information … they’re being fed.

Perhaps “information” isn’t exactly the right word.

It might be best to start with the distinction between “information” and “data.”

The distinction is not always clear in a general setting. It is, however, stark in the world of science, which is where I originally came from.

What comes into our brains from the outside world is “data.” It’s facts and figures. Contrary to what many people imagine, “data” is devoid of meaning. Scientists often refer to it as “raw data” to emphasize this characteristic.

There is nothing actionable in raw data. The observation that “the sky is blue” can’t even tell you if the sky was blue yesterday, or how likely it is to be blue tomorrow. It just says: “the sky is blue.” End of story.

Turning “data” into “information” involves combining it with other, related data, and making inferences about or deductions from patterns perceivable in the resulting superset. The process is called “interpretation,” and it’s the second step in turning data into knowledge. It’s what our brains are good for.

So, does this mean that news reporters are to be empty-headed recorders of raw facts?

Not by a long shot!

The CNN commentator’s point was that reporters are far from empty headed. While learning their trade, they develop ways to, for example, tell when some data source is lying to them.

In the hard sciences it’s called “instrumental error,” and experimental scientists (as I was) spend careers detecting and eliminating it.

Similarly, what a reporter does when faced with a lying source is the hard part of news reporting. Do you say, “This source is unreliable” and suppress what they told you? Do you report what they said along with a comment that they’re a lying so-and-so who shouldn’t be believed? Certainly, you try to find another source who tells you something you can rely on. But, what if the second source is lying, too?

???

That’s why we news consumers have to rely on professionals who actually care about the truth for our news.

On the other hand, nobody goes to news outlets for just raw data. We want something we can use. We want something actionable.

Most of us have neither the time nor the tools to interpret all the drivel we’re faced with. Even if we happen to be able to work it out for ourselves, we could always use some help, even if just to corroborate our own conclusions.

Who better to help us interpret the data (news) and glean actionable opinions from it than those journalists who’ve been spending their careers listening to the crap newsmakers want to feed us?

That’s where commentators come in. The difference between an editor and a reporter is that the editor has enough background and experience to interpret the raw data and turn it into actionable information.

That is: opinion you can use to make a decision. Like, maybe, who to vote for.

People with the chops to interpret news and make comments about it are called “commentators.”

When I was looking to hire what we used to call a “Technical Editor” for Test & Measurement World, I specifically looked for someone with a technical degree and experience developing the technology I wanted that person to cover. So, for example, when I was looking for someone to cover advances in testing of electronics for the telecommunications industry, I went looking for a telecommunications engineer. I figured that if I found one who could also tell a story, I could train them to be a journalist.

That brings us back to the CNN commentator who thought she should have opinions.

The relevant word here is “commentator.”

She’s not just a reporter. To be a commentator, she supposedly has access to the best available “data” and enough background to skillfully interpret it. So, what she was saying is true for a commentator rather than just a reporter.

Howsomever, ya can’t just give a conclusion without showing how the facts lead to it.

Let’s look at how I assemble a post for this blog as an example of what you should look for in a reliable op-ed piece.

Obviously, I look for a subject about which I feel I have something worthwhile to say. Specifically, I look for what I call the “take-home lesson” on which I base every piece of blather I write.

The “take-home lesson” is the basic point I want my reader to remember. Come Thursday next you won’t remember every word or even every point I make in this column. You’re (hopefully) going to remember some concept from it that you should be able to summarize in one or two sentences. It may be the “call to action” my eighth-grade English teacher, Miss Langley, told me to look for in every well-written editorial. Or, it could be just some idea, such as “Racism sucks,” that I want my reader to believe.

Whatever it is, it’s what I want the reader to “take home” from my writing. All the rest is just stuff I use to convince the reader to buy into the “take-home lesson.”

Usually, I start off by providing the reader with some context in which to fit what I have to say. It’s there so that the reader and I start off on the same page. This is important to help the reader fit what I have to say into the knowledge pattern of their own mind. (I hope that makes sense!)

After setting the context, I provide the facts that I have available from which to draw my conclusion. The conclusion will be, of course, the “take-home lesson.”

I can’t be sure that my readers will have the facts already, so I provide links to what I consider reliable outside sources. Sometimes I provide primary sources, but more often they’re secondary sources.

Primary sources for, say, a biographical sketch of Thomas Edison would be diary pages or financial records, which few readers would have immediate access to.

A secondary source might be a well-researched entry on, say, the Biography.com website, which the reader can easily get access to and which can, in turn, provide links to useful primary sources.

In any case, I try to provide sources for each piece of data on which I base my conclusion.

Then, I’ll outline the logical path that leads from the data pattern to my conclusion. While the reader should have no need to dispute the “data,” he or she should look very carefully to see whether my logic makes sense. Does it lead inevitably from the data to my conclusion?

Finally, I’ll clearly state the conclusion.

In general, every consumer of ideas should look for this same pattern in every information source they use.

Noble Whitefoot or Lying Blackfoot?

Fake News feed
How do you know when the news you’re reading is fake? Rawpixel/Shutterstock

19 September 2018 – Back in the mid-1970s, we RPI astrophysics graduate students had this great office at the very top of the Science Building at Rensselaer Polytechnic Institute.The construction was an exact duplicate of the top floor of an airport control tower, with the huge outward-sloping windows and the wrap-around balcony.

Every morning we’d gather ’round the desk of our compatriot Ron Held, builder of stellar-interior computer models extraordinaire, to hear him read “what fits” from the days issue of The New York Times. Ron had noticed that when taken out of context much of what is written in newspapers sounds hilarious. He had a deadpan way of reading this stuff out loud that only emphasized the effect. He’d modified the Times‘ slogan, “All the news that’s fit to print” into “All the news that fits.”

Whenever I hear unmitigated garbage coming out of supposed news outlets, I think of Ron’s “All the news that fits.”

These days, I’m on a kick about fake news and how to spot it. It isn’t easy because it’s become so pervasive that it becomes almost believable. This goes along with my lifelong philosophical study that I call: “How do we know what we think we know?”

Early on I developed what I call my “BS detector.” It’s a mental alarm bell that goes off whenever someone tries to convince me of something that’s unbelievable.

It’s not perfect. It’s been wrong on a whole lot of occasions.

For example, back in the early 1970s somebody told me about something called “superconductivity,” where certain materials, when cooled to near absolute zero, lost all electrical resistance. My first reaction, based on the proposition that if something sounds too good to be true, it’s not, was: “Yeah, and if you believe that I’ve got this bridge between Manhattan and Brooklyn to sell you.”

After seeing a few experiments and practical demonstrations, my BS detector stopped going off and I was able to listen to explanations about Cooper Pairs, and electron-phonon interactions and became convinced. I eventually learned that nearly everything involving quantum theory sounds like BS until you get to understand it.

Another time I bought into the notion that Interferon would develop into a useful AIDS treatment. Being a monogamous heterosexual, I didn’t personally worry about AIDS, but I had many friends who did, so I cared. I cared enough to pay attention, and watch as the treatment just didn’t develop.

Most of the time, however, my BS detector works quite well, thank you, and I’ve spent a lot of time trying to divine what sets it off, and what a person can do to separate the grains of truth from the BS pile.

Consider Your Source(s)

There’s and old saying: “Figures don’t lie, but liars can figure.”

First off, never believe anybody whom you’ve caught lying to you in the past. For example, Donald Trump has been caught lying numerous times in the past. I know. I’ve seen video of him mouthing words that I’ve known at the time were incorrect. It’s happened so often that my BS detector goes off so loudly whenever he opens his mouth that the noise drowns out what he’s trying to say.

I had the same problem with Bill Clinton when he was President (he seems to have gotten better, now, but I’m still wary).

Nixon was pretty bad, too.

There’s a lot of noise these days about “reliable sources.” But, who’s a reliable source? You can’t take their word for it. It’s like the old riddle of the lying blackfoot indian and the truthful whitefoot.

Unfortunately, in the real world nobody always lies or always tells the truth, even Donald Trump. So, they can’t be unmasked by calling on the riddle’s answer. If you’re unfamiliar with the riddle, look it up.

The best thing to do is try to figure out what the source’s game is. Everyone in the communications business is selling something. It’s up to you to figure out what they’re selling and whether you want to buy it.

News is information collected on a global scale, and it’s done by news organizations. The New York Times is one such organization. Another is The Wall Street Journal, which is a subsidiary of Dow Jones & Company, a division of News Corp.

So, basically, what a legitimate news organization is selling is information. If you get a whiff that they’re selling anything else, like racism, or anarchy, or Donald Trump, they aren’t a real news organization.

The structure of a news organization is:

Publisher: An individual or group of individuals generally responsible for running the business. The publisher manages the Circulation, Advertising, Production, and Editorial departments. The Publisher’s job is to try to sell what the news organization has to sell (that is, information) at a profit.

Circulation: A group of individuals responsible for recruiting subscribers and promoting sales of individal copies of the news organization’s output.

Advertising: A group of individuals under the direct supervision of the Publisher who are responsible for selling advertising space to individuals and businesses who want to present their own messages to people who consume the news organization’s output.

Production: A group of individuals responsible for packaging the information gathered by the Editorial department into physical form and distributing it to consumers.

Editorial: A group of trained journalists under a Chief Editor responsible for gathering and qualifying information the news organization will distribute to consumers.

Notice the italics on “and qualifying” in the entry on the Editorial Department. Every publication has their self-selected editorial focus. For a publication like The Wall Street journal, whose editorial focus is business news, every story has to fit that editorial focus. A story that, say, affects how readers select stocks to buy or sell is in their editorial focus. A story that doesn’t isn’t.

A story about why Donald Trump lies doesn’t belong in The Wall Street Journal. It belongs in Psychology Today.

That’s why editors and reporters have to be “trained journalists.” You can’t hire just anybody off the street, slap a fedora on their head and call them a “reporter.” That never even worked in the movies. Journalism is a profession and journalists require training. They’re also expected behave in a manner consistent with journalistic ethics.

One of those ethical principles is that you don’t “editorialize” in news stories. That means you gather facts and report those facts. You don’t distort facts to fit your personal opinions. You for sure don’t make up facts out of thin air just ’cause you’d like it to be so.

Taking the example of The Wall Street Journal again, a reporter handed some fact doesn’t know what the reader will do with that fact. Some will do some things and others will do something else. If a reporter makes something up, and readers make business decisions based on that fiction, bad results will happen. Business people don’t like that. They’d stop buying copies of the newspaper. Circulation would collapse. Advertisers would abandon it.

Soon, no more The Wall Street Journal.

It’s the Chief Editor’s job to make sure reporters seek out information useful to their readers, don’t editorialize, and check their facts to make sure nobody’s been lying to them. Thus, the Chief Editor is the main gatekeeper that consumers rely on to keep out fake news.

That, by the way, is the fatal flaw in social media as a news source: there’s no Chief Editor.

One final note: A lot of people today buy into the cynical belief that this vision of journalism is naive. As a veteran journalist I can tell you that it’s NOT. If you think real journalism doesn’t work this way, you’re living in a Trumpian alternate reality.

Bang your head on the nearest wall hoping to knock some sense into it!

So, for you, the news consumer, to guard against fake news, your first job is to figure out if your source’s Chief Editor is trustworthy.

Unfortunately, it’s very seldom that most people get to know a news source’s Chief Editor well enough to know whether to trust him or her.

Comparison Shopping for Ideas

That’s why you don’t take the word of just one source. You comparison shop for ideas the same way you do for groceries, or anything else. You go to different stores. You check their prices. You look at sell-by dates. You sniff the air for stale aromas. You do the same thing in the marketplace for ideas.

If you check three-to-five news outlets, and they present the same facts, you gotta figure they’re all reporting the facts that were given to them. If somebody’s out of whack compared to the others, it’s a bad sign.

Of course, you have to consider the sources they use as well. Remember that everyone providing information to a news organization has something to sell. You need to make sure they’re not providing BS to the news organization to hype sales of their particular product. That’s why a credible news organization will always tell you who their sources are for every fact.

For example, a recent story in the news (from several outlets) was that The New York Times published an opinion-editorial piece (NOT a news story, by the way) saying very unflattering things about how President Trump was managing the Executive Branch. A very big red flag went up because the op-ed was signed “Anonymous.”

That red flag was minimized by the paper’s Chief Editor, Dean Baquet, assuring us all that he, at least, knew who the author was, and that it was a very high official who knew what they were talking about. If we believe him, we figure we’re likely dealing with a credible source.

Our confidence in the op-ed’s credibility was also bolstered by the fact that the piece included a lot of information that was available from other sources that corroborated it. The only new piece of information, that there was a faction within the White House that was acting to thwart the President’s worst impulses, fitted seamlessly with the verifiable information. So, we tend to believe it.

As another example, during the 1990s I was watching the scientific literature for reports of climate-change research results. I’d already seen signs that there was a problem with this particular branch of science. It had become too political, and the politicians were selling policies based on questionable results. I noticed that studies generally were reporting inconclusive results, but each article ended with a concluding paragraph warning of the dangers of human-induced climate change that did not fit seamlessly with the research results reported in the article. So, I tended to disbelieve the final conclusions.

Does It Make Sense to You?

This is where we all stumble when ferreting out fake news. If you’re pre-programmed to accept some idea, it won’t set off your BS detector. It won’t disagree with the other sources you’ve chosen to trust. It will seem reasonable to you. It will make sense, whether it’s right or wrong.

That’s a situation we all have to face, and the only antidote is to do an experiment.

Experiments are great! They’re our way of asking Mommy Nature to set us on the right path. And, if we ask often enough, and carefully enough, she will.

That’s how I learned the reality of superconductivity against my inbred bias. That’s how I learned how naive my faith in interferon had been.

With those cautions, let’s look at how we know what we think we know.

It starts with our parents. We start out truly impressed by our parents’ physical and intellectual capabilities. After all, they can walk! They can talk! They can (in some cases) do arithmetic!

Parents have a natural drive to stuff everything they know into our little heads, and we have a natural drive to suck it all in. It’s only later that we notice that not everyone agrees with our parents, and they aren’t necessarily the smartest beings on the planet. That’s when comparison shopping for ideas begins. Eventually, we develop our own ideas that fit our personalities.

Along the way, Mommy Nature has provided a guiding hand to either confirm or discredit our developing ideas. If we’re not pathological, we end up with a more or less reliable feel for what makes sense.

For example, almost everybody has a deep-seated conviction that torturing pets is wrong. We’ve all done bad things to pets, usually unintentionally, and found it made us feel sad. We don’t want to do it again.

So, if somebody advocates perpetrating cruelty to animals, most of us recoil. We’d have to be given a darn good reason to do it. Like, being told “If you don’t shoot that squirrel, there’ll be no dinner tonight.”

That would do it.

Our brains are full up with all kinds of ideas like that. When somebody presents us with a novel idea, or a report of something they suggest is a fact, our first line of defense is whether it makes sense to us.

If it’s unbelievable, it’s probably not true.

It could still be true, since a lot of unbelievable stuff actually happens, but it’s probably not. We can note it pending confirmation by other sources or some kind of experimental result (like looking to see the actual bloody mess).

But, we don’t buy it out of hand.

Nobody Gets It Completely Right

As Dr. Who (Tom Baker) once said: “To err is computer. To forgive is fine.”

The real naive attitude about news, which I used to hear a lot fifty or sixty years ago is, “If it’s in print, it’s gotta be true.”

Reporters, editors and publishers are human. They make mistakes. And, catching those mistakes follows the 95:5 rule.That is, you’ll expend 95% of your effort to catch the last 5% of the errors. It’s also called “The Law of Diminishing Returns,” and it’s how we know to quit obsessing.

The way this works for the news business is that news output involves a lot of information. I’m not going to waste space here estimating the amount of information (in bits) in an average newspaper, but let’s just say it’s 1.3 s**tloads!

It’s a lot. Getting it all right, then getting it all corroborated, then getting it all fact checked (a different, and tougher, job than just corroboration), then putting it into words that convey that information to readers, is an enormous task, especially when a deadline is involved. It’s why the classic image of a journalist is some frazzled guy wearing a fedora pushed back on his head, suitcoat off, sleeves rolled up and tie loosened, maniacally tapping at a typewriter keyboard.

So, don’t expect everything you read to be right (or even spelled right).

The easiest things to get right are basic facts, the Who, What, Where, and When.

How many deaths due to Hurricane Maria on Puerto Rico? Estimates have run from 16 to nearly 3,000 depending on who’s doing the estimating, what axes they have to grind, and how they made the estimate. Nobody was ever able to collect the bodies in one place to count them. It’s unlikely that they ever found all the bodies to collect for the count!

Those are the first four Ws of news reporting. The fifth one, Why, is by far the hardest ’cause you gotta get inside someone’s head.

So, the last part of judging whether news is fake is recognizing that nobody gets it entirely right. Just because you see it in print doesn’t make it fact. And, just because somebody got it wrong, doesn’t make them a liar.

They could get one thing wrong, and most everything else right. In fact, they could get 5 things wrong, and 95 things right!

What you look for is folks who make the effort to try to get things right. If somebody is really trying, they’ll make some mistakes, but they’ll own up to them. They’ll say something like: “Yesterday we told you that there were 16 deaths, but today we have better information and the death toll is up to 2,975.”

Anybody who won’t admit they’re ever wrong is a liar, and whatever they say is most likely fake news.

Authoritarianism vs. Democracy

Seating for the French National Assembly - 1789
Seating arrangements in the room used by the National Constituent Assembly at the start of the French Revolution led to two factions gathering together on opposite sides of the hall. The revolutionaries happened to gather on the left, while those opposed to revolution gathered on the right. By Marzolino/Shutterstock

14 September 2018 – This is an extra edition of my usual weekly post on this blog. I’m writing it to tell you about an online event called “Open Future” put on by The Economist weekly newsmagazine and to encourage you to participate by visiting the URL www.economist.com/openfuture. The event is scheduled for tomorrow, 15 September 2018, but the website is already up, and some parts of the event are already live.

The newsmagazine’s Editor-in-Chief, Zanny Minton Beddoes, describes the event as “an initiative to remake the case for liberal values and policies in the 21st century.”

Now, don’t get put off by the use of the word “liberal.” These folks are Brits and, as I’ve often quipped: “The British invented the language, but they still can’t spell it or pronounce it.” They also sometimes use words to mean different things.

What The Economist calls “liberal” is not what we in the U.S. usually think of as liberal. You can get a clear idea of what The Economist refers to as “liberal” by perusing the list of seminal works in their article “The literature of liberalism.”

We in the U.S. are confused by typically hearing the word “liberal” used to describe leftist policies classed as Liberal with a capital L. Big-L Liberals have co-opted the word to refer to the agenda of the Democratic Party, which, as I’ll explain below, isn’t quite what The Economist refers to as small-L liberal.

The Economist‘s idea of liberal is more like what we usually call “libertarian.” Libertarians tend to take some ideas usually reserved for the left, and some from the right. Their main tenet, however, which is best expressed as “think for yourself,” is anathema to both ends of the political spectrum.

But, those of us in the habit of thinking for ourselves like it.

Unfortunately (or maybe not) small-L libertarianism is in danger of being similarly co-opted in the U.S. by the current big-L Libertarian Party. But, that’s a rant for another day!

What’s more important today is understanding a different way of dividing up political ideologies.

Left vs. Right

Two-hundred twenty-nine years ago, political discourse invented the terms “The Left” and “The Right” as a means of classifying political parties along ideological lines. The terms arose at the start of the French Revolution when delegates to the National Constituent Assembly still included foes of the revolution as well as its supporters.

As the ancient Greek proverb says, “birds of a feather flock together,” so supporters of revolution tended to pick seats near each other, and those against it sat together as well. Those supporting the revolution happened to sit on the left side of the hall, so those of more conservative bent gathered on the right. The terminology became institutionalized, so we now divide the political spectrum between a liberal/progressive Left and a conservative Right.

While the Left/Right-dichotomy works for describing what happened during the first meeting of the French National Constituent Assembly, it poorly reflects the concepts humans actually use to manage governments. In the real world, there is an equally simple, but far more relevant way of dividing up political views: authoritarianism versus democracy.

Authoritarians are all those people (and there’s a whole bunch of them) who want to tell everybody else what to do. It includes most religious leaders, most alpha males (and females), and, in fact, just about everyone who wants to lead anything from teenage gangs to the U.N. General Assembly. Patriarchal and matriarchal families are run on authoritarian principles.

Experience, by the way, shows that authoritarianism is a lousy way to run a railroad, despite the fact that virtually every business on the Planet is organized that way. Managment consultants and organizational-behavior researchers pretty much universally agree that spreading decision making throughout the organization, even down to the lowest levels, makes for the most robust, healthiest companies.

If you want your factory’s floors to be clean, make sure the janitors have a say in what mops and buckets to use!

The opposite of authoritarianism is democracy. Little-D democracy is the antithesis of authoritarianism. Small-D democrats don’t tell people what to do, they ask them what they (the people) want to do, and try to make it possible for them to do it. It takes a lot more savvy to balance all the conflicting desires of all those people than to petulently insist on things being done your way, but, if you can make it work, you get better results.

Now, political discourse based on the Left/Right dichotomy is simple and easy for political parties to espouse. Big-D Democrats have a laundry list of causes they champion. Similarly, Republicans have a laundry list of what they want to promote.

Those lists, however, absolutely do not fit the democracy/authoratarianism picture. And, there’s no reason to expect them to.

Politicians, generally, want to tell other people what to do. If they didn’t, they’d go do something else. That’s the very nature of politics. Thus, by and large, politicians are authoritarians.

They dress their plans up in terms that sound like democracy because most people don’t like being told what to do. In America, we’ve institutionalized the notion that people don’t like being told what to do, so bald-faced authoritarianism is a non-starter.

We Don’t Need No Stinking Authoritarians

(Apologies to the Man in the Gold Sombrero from John Huston’s 1948 film The Treasure of the Sierra Madre.)

It started in England with the Magna Carta, in which the English nobles told King John “enough is enough.”

Yeah, King John is the same guy as the “Prince John” who was cast as the arch-enemy of fictional hero Robin Hood. See, we don’t like authoritarians, and generally cast them as the villains in our favorite stories.

Not wanting to be told what to do was imported to North America by the English colonists, who extended the concept (eventually) to everyone regardless of socio-economic status. From there, it was picked up by the French revolutionaries, then spread throughout Europe and parts East.

So, generally, nobody wants authoritarians telling them what to do, which is why they have to point guns at us to get us to do it.

The fact that most people would simultaneously like to be the authoritarian pointing the gun and doing the telling, and a fair fraction (probably about 25%) aren’t smart enough to see the incongruity involved, gives fascist populists a ready supply of people willing to hold the guns. Nazi Germany worked (for a while) because of this phenomenon. With a population North of 60 million, those statistics gave Hitler some 15 million gun holders to work with.

In the modern U.S.A., with a population over 300 million, the same statistical analysis gives modern fascists 75 million potential recruits. And, they’re walking around with more than their fair share of the guns!

Luckily, the rest of us have guns, too.

More importantly, we all have votes.

So, what’s an American who really doesn’t want any authoritarian telling them what to do … to do?

The first thing to do is open your eyes to the flim-flim represented by the Left/Right dichotomy. As long as you buy that drivel, you’ll never see what’s really going on. It’s set up as a sporting event where you’re required to back one of two teams: the Reds or the Blues.

Either one you pick, you’ll end up being told what to do by either the Red-team authoritarians or the Blue-team authoritarians. Because it’s treated as a sporting event, the object is to win, and there’s nothing at stake beyond winning. There isn’t even a trophy!

The next thing to do is look for people who would like to help, but don’t actually want to tell anyone what to do. When you find them, talk them into running for office.

Since you’ve picked on people who don’t really want to tell other people what to do, you’ll have to promise you won’t make them do it forever. After a while, you promise, you’ll let them off the hook so they can go do something else. That means putting term limits on elected officials.

The authoritarians, who get their jollies by telling other people what to do, won’t like that. The ones who just want to help out will be happy they can do their part for a while, then go home.

Then, you vote for those (small-L) libertarians.