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Finally got to see the documentary, “After Truth: Disinformation and the Cost of Fake News,” after I’d PVR’d it on HBO a week ago. Rather than relitigating the actual conspiracy theories, the producers took a more direct approach to the human cost of combining ignorance with malicious disinformation. And while the documentary prioritizes victims over some rather lame-brained conspiracies—the aim is to shed light on the fake news epidemic, and expose the pernicious rejection of science, logic, and solid empirical evidence.

Just making shit up and getting credulous others to buy into it…

If this gets your rocks off, you’re really pathetic, I’m sorry to say. Let’s not mince words. If this sort of sociopathology turns you on, you’re better off visiting one of those ethical Suicide Parlors Kurt Vonnegut Jr. fantasized about in his novella, Welcome to the Monkey House—than try and pretend to be getting to the bottom of who gets to decide what images and ideas are true and safe to trust in.

It’s not a victimless crime to suggest we might experiment with injecting Clorox to fight the Covid pandemic. There are dire consequences to such chinless propagation of fake news. “Yeah, there are terrible, negative potential consequences, but so what,” sniggered Republican lobbyist Jack Burkman. “That’s what I say: So what?”

So what? What about Pizzagate?” Remember that? Where the popular Washington DC pizzeria Comet Ping Pong was invaded by Edgar Maddison Welch of Salisbury, NC, wielding an AR-15 assault rifle—which he fired inside—searching for a nonexistent pedophile ring he was led to believe was being run out of the pizza parlor by Hillary Clinton, and her squirrelly co-conspirators in the Democratic Party.

What Truth is There to Conspiracy Theories Involving Covid-19 and Bill Gates?

The world needs to prepare for pandemics in the same serious way it prepares for war.

—Microsoft co-founder and philanthropist Bill Gates

The next outbreak? We’re not ready | Bill Gates

In 2015, business magnate and philanthropist Bill Gates warned the world of an impending pandemic in a TED Talk he gave, where he predicted a novel virus could kill over 30-million people worldwide. Gates said five years ago that this would take place “in the next decade,” and be similar to the Spanish influenza of 1918 that killed over 50-million. Turns out this is predictably true.

You may have heard or read about incarnate les tricoteuses linking the rather mundane talk Gates gave with a “Humans Are Free” article—claiming Mr. Gates was in cahoots with clandestine research labs that were, or are, secretly creating “weaponized viral strains designed to sell more deadly vaccines—killing off a few thousand, or perhaps a few million, people,” one conspiracy-based report nonchalantly appended.

Current Chief White House correspondent for the One America News Network, Emerald Robinson, posted a series of misleading tweets that accuse Mr. Gates with nothing to back them up but innuendo. For example, she twittered, “The more you study this virus the more you find the same name: Bill Gates. He’s the 22nd largest funder of WHO. He’s building seven vaccine labs. Faucci. Tedros. Event 201. ID 2020. He basically controls global health policy. What’s the plan? Using vaccines to track people.” With microchips. Sounds like Ms. Robinson may be missing a few.

The fact is that the Gates Foundation (with close to a $50-billion endowment) had indeed advocated for expanded covid-19 testing and has funded such research. But neither of those initiatives involves implanted microchips for heaven’s sake. What Gates actually said on March 18th: “Eventually, we will have some digital certificates to show who has recovered or been tested recently, or when we have a vaccine, who has received it.”

The following day, Fox media celebrity Laura Ingraham and more crater heads at the Biohackinfo.com website posted the headline: “Bill Gates will use microchip implants to fight coronavirus.” The story likened Gates’ reference to “digital certificates” as “human-implantable ‘quantum-dot tattoos.’” And more than another 16,000 crackerjacks immediately shared this ordure on Facebook.

According to data from CrowdTangle and Zignal Labs, the mash-up of two unrelated stories has been viewed close to 5-million times. Stupidity, it seems, is as contagious as covid-19.

In 2011, a multidisciplinary team based at UC Berkeley and the University of Pennsylvania—researchers like Canadian-American political scientist Philip Tetlock, Barbara Mellers, et al launched the Good Judgement Project (GJP) with the Intelligence Advanced Research Projects Activity (IARPA). Their aim was to find ways for figuring out things like: would protests in Russia spread, would the price of gold plummet, or would war break out on the Korean peninsula?

By varying the experimental conditions, the researchers were able to gauge which factors improved foresight, by how much, over which time frames, and how good individuals could become at it if best practices were layered on top of each other. The project consistently outperformed the official control group by 78 percent, and surpassed their university-affiliated competitors—including professional intelligence analysts from the University of Michigan and MIT—from anywhere between 30 to 70 percent.

At its core—despite this era of wickedly powerful supercomputers and impossibly indecipherable algorithms—the successes reported in Philip E. Tetlock and Dan Gardner’s Superforecasting: The Art and Science of Prediction involves the study and practice of subjective judgment. In the way people think and decide things, nothing more. The authors admit that complex subjective judgment studies take

I have been struck by how important measurement is to improving the human condition. You can achieve incredible progress if you set a clear goal and find a measure that will drive progress toward that goal.

—Bill Gates, “My Plan to Fix the World’s Biggest Problems,” Wall Street Journal, 2013.

time and can be expensive to administer—when you have a well-validated statistical algorithm, they say, use it.

However, understand that there is a vast difference between asking a computer, “Which two Russian leaders traded jobs?” and “Will two Russian leaders trade jobs again?” say Tetlock and Gardner. The former is a historical fact. The latter requires the computer to make informed guesses about the intentions of Vladimir Putin, the character of Dmitri Medvedev, and the causal dynamics of Russian politics. Then desegregate all of that into a cohesive “judgment call.”

The human brain does this sort of thing as a matter of course. Daily. But such tasks are still a long way off even for supercomputers Deep Blue or IBM’s Watson (which finally managed to beat Jeopardy! champions in 2011). There’s a huge difference between “mimicking human meaning” and “reflecting and originating meaning,” said Watson’s chief engineer, David Ferrucci, “That’s a space human judgment will always occupy” (italics mine).

There Are Ten Commandments for Aspiring Superforecasters to Master— Here Are Three of Them

( For the fully-detailed guidelines—since these three examples have been severely abridged and only partially sketched-out here—please read, or check out the free audio version of, the book and visit: https://goodjudgment.com )

1. Break seemingly intractable problems into tractable sub-problems.

Decompose the problem into its knowable and unknowable parts. Expose and examine your assumptions by flushing ignorance out in the open. And dare to be wrong by risking best guesses.

Consider Peter Backus, a lonely guy in London, England, who guesstimated the number of potential female partners in his vicinity by starting with the population of London (approximately six million)—then winnowing that down by the percentage of those who are women, then by the proportion of single women, then by the proportion of those in the right age range, by the fraction of those who are university graduates, by the share of those he would likely find attractive, by the percentage of those likely to find Peter attractive, and finally by the proportion of those likely to be fully compatible with him.

Mr. Backus’ final tally: roughly 26 female candidates in his pool. Now the hard part begins: Predicting the best ways to find them. And although there can be no objectively correct answers to questions of true love—we can score the accuracy of the estimates that superforecasters generated in the

IARPA tournament (Jo Graven McGinty, “To Find a Romantic Match, Try Some Love Math,” Wall Street Journal, February 14, 2015) to read how it’s done.

  1. Strike the right balance between under- and overconfidence, between prudence and decisiveness. Superforecasters understand the risks of rushing to judgment, and those of hang fire and procrastination. They routinely manage the trade-off between taking a decisive stand and needing to qualify their options. For example, by putting as much time and effort into calibration (weighing and sizing) as resolution (sorting and solving). It’s not enough just to avoid our most recent mistakes. We have to find creative new ways to even the score, by tamping down both misses and false alarms. Learning ready patience.
  2. Bring out the best in others and let others bring out the best in you. Helping others to clarify their arguments so they are not misunderstood by other members of the team is an act of leadership. Master the fine arts and science of team management, especially understanding the arguments and perspectives of either side. So well, in fact, that you can readily reproduce other’s POV to their satisfaction when challenged to do so. Constructive criticism and confrontation are necessities—learning to disagree without being disagreeable—are acts of leadership also. Wise leaders know where to draw the line between helpful suggestions and unnecessary micro-meddling—or between being too unbending and making a firm decision. It can make a big difference. Between heading a featherheaded flock of sycophants, or creating an openminded team of rivals to play and have fun with.

Amor fati, Peter Ć

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