Jim Rickards explains why there’s a financial crisis coming, and in so doing, reviews the unusual origins of his predictive analytics tool. He also explores complexity theory and Bayesian statistics. Jim Rickards is a renowned author and the chief global strategist at Meraglim. Filmed on July 12, 2018 in New York.
This has roots that go back to 9/11.12:24Tragic day, September 11, 2001, when the 9/11 attack took place.12:31And what happened then– there was insider trading in advance of 9/11.12:37In the two trading days prior to the attack, average daily volume and puts, which is short12:43position, put option buying on American Airlines and United Airlines, was 286 times the average12:50daily volume.12:51Now you don’t have to be an option trader, and I order a cheeseburger for lunch every12:55day, and one day, I order 286 cheeseburgers, something’s up.12:59There’s a crowd here.13:00I was tapped by the CIA, along with others, to take that fact and take it forward.13:06The CIA is not a criminal investigative agency.13:10Leave that to the FBI and the SEC.13:11But what the CIA said was, OK, if there was insider trading ahead of 9/11, if there were13:17going to be another spectacular terrorist attack, something of that magnitude, would13:22there be insider trading again?13:24Could you detect it?13:26Could you trace it to the source, get a FISA warrant, break down the door, stop the attack,13:30and save lives?13:31That was the mission.13:32We call this Project Prophecy.13:34I was the co-project director, along with a couple of other people at the CIA.13:39Worked on this for five years from 2002 to 2007.13:43When I got to the CIA, you ran into some old timers.13:47They would say something like, well, Al-Qaeda or any terrorist group, they would never compromise13:53operational security by doing insider trading in a way that you might be able to find.13:59And I had a two word answer for that, which is, Martha Stewart.14:03Martha Stewart was a legitimate billionaire.14:05She made a billion dollars through creativity and her own company.14:08She ended up behind bars because of a $100,000 trade.14:11My point is, there’s something in human nature that cannot resist betting on a sure thing.14:15And I said, nobody thinks that Mohamed Atta, on his way to Logan Airport, to hijack a plane,14:21stopped at Charles Schwab and bought some options.14:23Nobody thinks that.14:24But even terrorists exist in the social network.14:26There’s a mother, father, sister, brother safe house operator, car driver, cook.14:32Somebody in that social network who knows enough about the attack and they’re like,14:36if I had $5,000, I could make 50, just buy a put option.14:40The crooks and terrorists, they always go to options because they have the most leverage,14:44and the SEC knows where to look.14:47So that’s how it happens.14:49And then the question was, could you detect it.14:52So we started out.14:53There are about 6,000 tickers on the New York Stock Exchange and the NASDAQ.14:57And we’re talking about second by second data for years on 6,000 tickers.15:03That’s an enormous, almost unmanageable amount of data.15:06So what we did is we reduced the targets.15:08We said, well, look, there’s not going to be any impact on Ben and Jerry’s ice cream15:12if there’s a terrorist attack.15:14You’re looking at cruise ships, amusement parks, hotels, landmark buildings.15:18there’s a set of stocks that would be most effective.15:22So we’re able to narrow it down to about 400 tickers, which is much more manageable.15:26Second thing you do, you establish a baseline.15:28Say, what’s the normal volatility, the normal average daily volume, normal correlation in15:35the stock market.15:36So-called beta and so forth.15:37And then you look for abnormalities.15:39So the stock market’s up.15:42The transportation sector is up.15:43Airlines are up, but one airline is down.15:46What’s up with that?15:47So that’s the anomaly you look for.15:48And then the third thing you do.15:49You look for news.15:50Well, OK, the CEO just resigned because of some scandal.15:54OK, got it, that would explain why the stock is down.15:57But when you see the anomalous behavior, and there’s no news, your reference is, somebody16:03knows something I don’t.16:04People aren’t stupid, they’re not crazy.16:06There’s a reason for that, just not public.16:08That’s the red flag.16:09And then you start to, OK, we’re in the target zone.16:12We’re in these 400 stocks most affected.16:15We see this anomalous behavior.16:17Somebody is taking a short position while the market is up and there’s no news.16:21That gets you a red light.16:23And then you drill down.16:24You use what in intelligence work we call all source fusion, and say, well, gee, is16:28there some pocket litter from a prisoner picked up in Pakistan that says cruise ships or something16:34along– you sort of get intelligence from all sources at that point drilled down So16:38that was the project.16:40We built a working model.16:41It worked fine.16:42It actually worked better than we expected.16:44I told the agency, I said, well, we’ll build you a go-kart, but if you want a Rolls Royce,16:48that’s going to be a little more expensive.16:50The go-kart actually worked like a Rolls Royce.16:52Got a direct hit in August 2006.16:55We were getting a flashing red signal on American Airlines three days before MI5 and New Scotland17:04Yard took down that liquid bomb attack that were going to blow up 10 planes in midair17:09with mostly Americans aboard.17:11So it probably would have killed 3,000 Americans on American Airlines and Delta and other flights17:16flying from Heathrow to New York.17:18That plot was taken down.17:20But again, we had that signal based on– and they made hundreds of arrests in this neighborhood17:26in London.17:27So this worked perfectly.17:30Unfortunately, the agency had their own reasons for not taking it forward.17:37They were worried about headline risk, they were worried about political risk.17:41You say, well, we were using all open source information.17:45You can pay the Chicago Mercantile Exchange for data feed to the New York Stock Exchange.17:48This is stuff that anybody can get.17:49You might to pay for it, but you can get it.17:52But the agency was afraid of the New York Times headline, CIA trolls through 401(k)17:58accounts, which we were not doing.18:00It was during the time of waterboarding and all that, and they decided not to pursue the18:06project.18:07So I let it go, there were plenty of other things to do.18:09And then as time went on, a few years later, I ended up in Bahrain at a wargame– financial18:14war game– with a lot of thinkers and subject matter experts from around the world.18:20Ran into a great guy named Kevin Massengill, a former Army Ranger retired Major in the18:27US army, who was working for Raytheon in the area at the time was part of this war game.18:32We were sort of the two American, little more out of the box thinkers, if you want to put18:36it that way.18:38We hit it off and I took talked him through this project I just described.18:42And we said, well look, if the government doesn’t want to do it, why don’t we do it18:45privately?18:46Why don’t we start a company to do this?18:47And that’s exactly what we did.18:49Our company is, as I mentioned, Meraglim.18:51Our website, Meraglim.com, and our product is Raven.18:55So the question is, OK, you had a successful pilot project with the CIA.19:01It worked.19:02By the way, this is a new branch of intelligence in the intelligence.19:06I-N-T, INT, is short for intelligence.19:08And depending on the source, you have SIGINT, which is signal intelligence, you have HUMINT19:14which is human intelligence, and a number of others.19:17We created a new field called MARKINT, which is market intelligence.19:20How can you use market data to predict things that are happening.19:24So this was the origin of it.19:26We privatized it, got some great scientists on board.19:30We’re building this out ourselves.19:31Who partnered with IBM, and IBM’s Watson, which is the greatest, most powerful plain19:38language processor.19:40Watson can read literally millions of pages of documents– 10-Ks, 10-Qs, AKs, speeches,19:47press releases, news reports.19:51More than a million analysts could read on their own, let alone any individual, and process19:57that in plain language.19:58And that’s one of our important technology partners in this.20:02And we have others.20:04What do we actually do?20:07What’s the science behind this.20:09First of all, just spend a minute on what Wall Street does and what most analysts do,20:13because it’s badly flawed.20:15It’s no surprise that– every year, the Fed does a one year forward forecast.20:22So in 2009, they predict 2010.20:24In 2010, they predict 2011.20:27So on.20:28Same thing for the IMF, same thing for Wall Street.20:30They are off by orders of magnitude year after year.20:34I mean, how can you be wrong by a lot eight years in a row, and then have any credibility?20:38And again, the same thing with Wall Street.20:41You see these charts.20:43And the charts show the actual path of interest rates or the actual path of growth.20:48And then along the timeline, which is the x-axis, they’ll show what people were predicting20:52at various times.20:54The predictions are always way off the actual path.20:57There’s actually good social science research that shows that economists do worse than trained21:02monkeys on terms of forecasting.21:04And I don’t say that in a disparaging way– here’s the science.21:07A monkey knows nothing.21:08So if you have a binary outcome– up, down, high, low, growth, recession– and you ask21:17a monkey, they’re going to be right half the time and wrong half the time, because they21:20don’t know what they’re doing.21:21So you’re to get a random outcome.21:24Economists are actually wrong more than half the time for two reasons.21:28One, their models are flawed.21:29Number two, what’s called herding or group behavior.21:32An economist would rather be wrong in the pack than go out on a limb and maybe be right,21:37but if it turns out you’re not right, you’re exposed.21:40But there are institutional constraints.21:42People want to protect their jobs.21:44They’re worried about other things than getting it right.21:47So the forecasting market is pretty bad.21:48The reasons for that– they use equilibrium models.21:52The capital markets are not in equilibrium system, so forget your equal equilibrium model.21:57They use the efficient market hypothesis, which is all the information is out there,22:01you can’t beat the market.22:02Markets are not efficient, we know that.22:05They use stress tests, which are flawed, because they’re based on the past, but we’re outside22:12the past.22:13The future could be extremely different.22:16They look at 9/11, they look at long term capital management, they look at the tequila22:20crisis.22:21Fine, but if the next crisis is worse, there’s nothing in that history that’s going to tell22:25you how bad it can get.22:27And so they assume prices move continuously and smoothly.22:30So price can go from here to here or from here to here.22:34But as a trader, you can get out anywhere in between, and that’s for all these portfolio22:38insurance models and stop losses come from.22:41That’s not how markets behave.22:42That go like this– they just gap up.22:44They don’t hit those in between points.22:45Or they gap down.22:46You’re way underwater, or you missed a profit opportunity before you even knew it.22:51So in other words, the actual behavior of markets is completely at odds with all the22:56models that they use.22:57So it’s no surprise the forecasting is wrong.23:00So what are the good models?23:01What are the models that do work?23:03What is the good science?23:04The first thing is complexity theory.23:07Complexity theory has a long pedigree in physics, meteorology, seismology, forest fire management,23:13traffic, lots of fields where it’s been applied with a lot of success.23:18Capital markets are complex systems.23:20The four hallmarks of a complex system.23:24One is their diversity of actors, sure.23:26Two is their interaction– are the actors talking to each other or are they all sort23:30of in their separate cages.23:31Well, there’s plenty of interaction.23:33Is there communication and is there adaptive behavior?23:37So yeah, there are diverse actors, there’s communication.23:40They’re interacting.23:42And if you’re losing money, you better change your behavior quickly.23:45That’s an example of adaptive behavior.23:47So capital markets are four for four in terms of what makes a complex system.23:51So why not just take complexity science and bring it over to capital markets?23:55That’s what we’ve done, and we’re getting fantastic results.23:57So that’s the first thing.23:58The second thing we use is something called Bayesian statistics.24:03It’s basically a mathematical model that you use when you don’t have enough data.24:08So for example, if I’ve got a million bits of data, yeah, do your correlations and regressions,24:14that’s fine.24:15And I learned this at the CIA, this is the problem we confronted after 9/11.24:19We had one data point– 9/11.24:20Janet Yellen would say, wait for 10 more attacks, and 30,000 dead, and then we’ll have a time24:26series and we can figure this out.24:28No.24:29To paraphrase Don Rumsfeld, you go to war with the data you have.24:33And so what you use is this kind of inferential method.24:37And the reason statisticians dislike it is because you start with a guess.24:41But it could be a smart guess, it could be an informed guess.24:44The data may be scarce.24:45You make the best guess you can.24:47And if you have no information at all, just make it 50/50.24:50Maybe Fed is going to raise rates, maybe they’re not.24:53I think we do better than that on the Fed.24:55But if you didn’t have any information, you just do 50/50.24:58But then what you do is you observe phenomena after the initial hypothesis, and then you25:05update the original hypothesis based on the subsequent data.25:07You ask yourself, OK this thing happened later.25:10What is the conditional correlation that the second thing would happen if the first thing25:14were true or not?25:16And then based on that, you’d go back, and you either increase the probability of the25:19hypothesis being correct, or you decrease it.25:21It gets low enough, you abandon it, try something else.25:24If it gets high enough, now you can be a lot more confident in your prediction.25:27So that’s Bayesian statistic.25:29You use it to find missing aircraft, hunt submarines.25:32It’s used for a lot of things, but you can use it in capital markets.25:36Third thing, behavioral psychology.25:38This has been pretty well vetted.25:40I think most economists are familiar with it, even though they don’t use it very much.25:43But humans turn out to be a bundle of biases.25:48We have anchoring bias, we get an idea in our heads, and we can’t change it.25:52We have recency bias.25:54We tend to be influenced by the last thing we heard.25:57And anchoring bias is the opposite, we tend to be influenced by something we heard a long26:01time ago.26:03Recency bias and anchoring bias are completely different, but they’re both true.26:07This is how you have to get your mind around all these contradictions.26:11But when you work through that, people make mistakes or exhibit bias, it turns out, in26:16very predictable ways.26:18So factor that in.26:19And then the fourth thing we use, and economists really hate this, is history.26:23But history is a very valuable teacher.26:26So those four areas, complexity theory, Bayesian statistics, behavioral psychology, and history26:33are the branches of science that we use.26:35Now what do we do with it?26:37Well, we take it and we put it into something that would look like a pretty normal neural26:41network.26:42You have nodes and edges and some influence in this direction, some have a feedback loop,26:47some influence in another direction, some are influenced by others, et cetera.26:51So for Fed policy for example, you’d set these nodes, and it would include the things I mentioned26:55earlier– inflation, deflation, job creation, economic growth, capacity, what’s going on27:02in Europe, et cetera.27:03Those will be nodes and there will be influences.27:05But then inside the node, that’s the secret sauce.27:08That’s where we have the mathematics, including some of the things I mentioned.27:12But then you say, OK, well, how do you populate these nodes?27:15You’ve got math in there, you’ve got equations, but where’s the news come from?27:19That’s where Watson comes in.27:20Watson’s reading all these records, feeding the nodes, they’re pulsing, they’re putting27:24input.27:25And then we have these actionable cells.27:26So the euro-dollar cross rate, the Yuandollar cross rate, yen, major benchmark, bonds, yields27:36on 10 year treasury notes, bunds, JGBs, et cetera.27:41These are sort of macro indicators, but the major benchmark bond indices, the major currency27:46across rates, the major policy rates, which are the short term central bank rates.27:50And a basket of commodities– oil, gold, and a few others– they are the things we watch.27:55We use these neural networks I described, but they’re not just kind of linear or conventional28:06equilibrium models.28:07They’re based on the science I describe.28:09So all that good science, bringing it to a new field, which is capital markets, using28:13what’s called fuzzy cognition, neural networks, populating with Watson, this is what we do.28:19We’re very excited about it, getting great results.28:21And this is what I use.28:23When I give a speech or write a book or write an article, and I’m making forecast.28:28This is what’s behind it.28:32So we talked earlier about business cycles, recessions, depressions.28:37And that’s conventional economic analysis.28:40My definition of depression is not exactly conventional, but that’s really thinking in28:44terms of growth, trend growth, below trend growth, business cycles, et cetera.28:50Collapse or financial panic is something different.28:52A financial panic is not the same as a recession or a turn in the business cycle.28:57They can go together, but they don’t have to.28:59So let’s talk about financial panics as a separate category away from the business cycle29:04and growth, which we talked about earlier.29:06Our science, the science I use, the science that we use with Raven, at our company, Meraglim,29:12involves complexity theory.29:14Well, complexity theory shows that the worst thing that can happen in a system is an exponential29:21function of scale.29:23Scale is just how big is it.29:24Now you have to talk about your scaling metrics.29:27We’re talking about the gross notional value derivatives.29:28We’re talking about average daily volume on the stock market.29:31We’re talking about debt.29:33We could be talking about all of those things.29:35This is new science, so I think it will be years of empirics to make this more precise.29:39But the theory is good, and you can apply it in a sort of rough and ready way.29:43So you go to Jamie Dimon, and you say, OK, Jamie, you’ve tripled your gross notional29:50value derivatives.29:51You’ve tripled your derivatives book.29:53How much did the risk go up?29:54Well, he would say, not at all, because yeah, gross national value is triple, but who cares?29:59It’s long, short, long, short, long, short, long, short.30:01You net it all down.30:02It’s just a little bit of risk.30:04Risk didn’t go up at all.30:06If you ask my 87-year-old mother, who is not an economist, but she’s a very smart lady,30:10say, hey mom, I tripled the system, how much did the risk go up?30:14She would probably use intuition and say, well, probably triple.30:17Jamie Dimon is wrong, my mother is wrong.30:21It’s not the net, it’s the gross.30:22And it’s not linear, it’s exponential.30:24In other words, if you triple the system, the growth went up by a factor of 10, 50,30:28et cetera.30:29There’s some exponential function associated with that.30:32So people think, well gee, in 2008, we learned our lesson.30:36We’ve got debt under control, we’ve got derivatives under control.30:39No.30:40Debt is much higher.30:41Debt to GDP ratios are much worse.30:44Total notional value, gross notional values of derivatives is much higher.30:47Now people look at the BIS statistics and say, well, the banks, actually, gross national30:52value derivatives has been going down, which it has, but that’s misleading because they’re30:57taking a lot of that, moving it over to clearing houses.30:59So it’s never been on the balance sheet, it’s always been off balance sheet.31:02But even if you use the footnotes, that number has gone down for banks, but that’s only because31:07they’re putting it over clearing houses.31:09Who’s guaranteeing the clearing house?31:10The risk hasn’t gone away, it’s just been moved around.31:12So given those metrics– debt, derivatives, and other indices, concentration, the fact31:21that the five largest banks in America have a higher percentage of total banking assets31:26than they did in 2008, there’s more concentration– that’s another risk factor.31:31Taking that all into account, you can say that the next crisis will be exponentially31:37worse than the last one.31:38That’s an objective statement based on complexity theory.31:41So you either have to believe that we’re never going to have a crisis.31:44Well, you had one in 1987, you had one in 1994, you had one in 1998.31:49You had the dotcom crash in 2000, mortgage crash in 2007, Lehman in 2008.31:55Don’t tell me these things don’t happen.31:56They happen every five, six, seven years.31:59It’s been 10 years since the last one.32:02Doesn’t mean it happens tomorrow, but nobody should be surprised if it does.32:05So the point is this crisis is coming because they always come, and it will be exponentially32:11worse because of the scaling metrics I mentioned.32:14Who’s ready for that?32:15Well, the central banks aren’t ready.32:17In 1998, Wall Street bailed out a hedge fund long term capital.32:23In 2008, the central banks bailed out Wall Street.32:25Lehman– but Morgan Stanley was ready to fail, Goldman was ready to fail, et cetera.32:31In 2018, 2019, sooner than later, who’s going to bail out the central banks?32:35And notice, the problem has never gone away.32:37We just get bigger bailouts at a higher level.32:40What’s bigger than the central banks?32:42Who can bail out the central banks?32:43There’s only one institution, one balance sheet in the world they can do that, which32:46is the IMF.32:48The IMF actually prints their own money.32:51The SDR, special drawing right, SDR is not the out strawberry daiquiri on the rocks,32:55it’s a special drawing right.32:56It’s world money, that’s the easiest way to think about it.32:58They do have a printing press.33:00And so that will be the only source of liquidity in the next crisis, because the central banks,33:07if they don’t normalize before the crisis– and it looks like they won’t be able to, they’re33:11going to run out of runway, and they can expand the balance sheet beyond the small amount33:17because they’ll destroy confidence, where does the liquidity come from?33:20The answer, it comes from the IMF.33:23So that’s the kind of global monetary reset, the GMR, global monetary resety.33:28You hear that expression.33:31There’s something very new that’s just been called to my attention recently, and I’ve33:36done some independent research on it, and it holds up.33:39So let’s see how it goes.33:42But it looks as if the Chinese have pegged gold to the SDR at a rate of 900 SDRs per33:51ounce of gold.33:52This is not the IMF.33:53The IMF is not doing this.33:55The Federal Reserve, the Treasury is not doing it.33:58The ECB is not doing it.33:59If they were, you’d see it.34:00It would show up in the gold holdings.34:02You have to conduct open market operations in gold to do this.34:05But the Chinese appear to be doing it, and it starts October 1, 2016.34:11That was the day the Chinese Yuan joined the SDR.34:15The IMF admitted the Yuan to the group was four, now five currencies that make up the34:21SDR.34:22So almost to the day, when the Yuan got in the SDR, you see this a horizontal trend where34:29first, gold per ounce is trading between 850 and 950 SDRs.34:37And then it gets tighter.34:38Right now, the range is 875 to 925.34:41Again, a lot of good data behind this.34:44So it’s a very good, it’s another predictive indicator.34:47If you see gold around 870 SDRs per ounce, that’s a strong SDR, weak gold.34:54Great time to buy gold, because the Chinese are going to move back up to 900.34:58So that’s an example of science, observation, base and statistics, inference, all the things35:04we talked about that can be used today in a predictive analytic way.35:08A crisis is coming, because they always do.35:10I don’t have a crystal ball, this is plenty of history to back it up.35:13It’ll be exponentially worse.35:15That’s what the science tells us.35:16The central banks will not be prepared, because they haven’t normalized from the last one.35:20You’re going to have to turn to the IMF, and who’s waiting there but China with a big pile35:24of gold.
If you are worried that ISIS might strike the United States and want to prevent the loss of American lives, consider urging Congress to invest in diabetes and Alzheimer’s research.
Terrorism is effective in doing what its name says: inspiring profound fear. But despite unremitting coverage of the Paris attacks, an objective examination of the facts shows that terrorism is an insignificant danger to the vast majority of people in the West.
You, your family members, your friends, and your community are all significantly more at risk from a host of threats that we usually ignore than from terrorism. For instance, while the Paris attacks left some 130 people dead, roughly three times that number of French citizens died on that same day from cancer.
In the United States, an individual’s likelihood of being hurt or killed by a terrorist (whether an Islamist radical or some other variety) is negligible.
Consider, for instance, that since the attacks of Sept. 11, 2001, Americans have been no more likely to die at the hands of terrorists than being crushed to death by unstable televisions and furniture. Meanwhile, in the time it has taken you to read until this point, at least one American has died from a heart attack. Within the hour, a fellow citizen will have died from skin cancer. Roughly five minutes after that, a military veteran will commit suicide. And by the time you turn the lights off to sleep this evening, somewhere around 100 Americans will have died throughout the day in vehicular accidents – the equivalent of “a plane full of people crashing, killing everyone on board, every single day.” Daniel Kahneman, professor emeritus at Princeton University, has observed that “[e]ven in countries that have been targets of intensive terror campaigns, such as Israel, the weekly number of casualties almost never [comes] close to the number of traffic deaths.”
No one in the United States will die from ISIS’s —or anyone’s — terrorism today.
What accounts for the fear that terrorism inspires, considering that its actual risk in the United States and other Western countries is so low? The answer lies in basic human psychology. Scholars have repeatedly found that individuals have strong tendencies to miscalculate risk likelihood in predictable ways.
For instance, individuals’ sense of control directly influences their feeling about whether they are susceptible to a given risk. Thus, for instance, although driving is more likely to result in deadly accidents than flying, individuals tend to feel that the latter is riskier than the former. Flying involves giving up control to the pilot. The resulting sense of vulnerability increases the feeling of risk, inflating it far beyond the actual underlying risks.
When people dread a particular hazard, and when it can harm large numbers at once, it’s far more likely that someone will see it as riskier than it is–and riskier than more serious hazards without those characteristics. For instance, people have been found to estimate that the number killed each year by tornadoes and floods are about the same as those killed by asthma and diabetes. But the latter (diabetes, in particular) account for far more deaths each year than the former. In fact, in the year that study was conducted, actual annual diabetes deaths were estimated in the tens of thousands while fewer than 1,000 people died in tornadoes.
Islamist terrorism has all three of these characteristics, inspiring excessive fear — surely by design. For instance, the Paris attacks harmed large numbers; its victims could have done very little to escape it, since the timing and location of such attacks are unpredictable; and the idea of being shot or blown up by a mysterious set of masked extremists is incredibly dreadful.
When we miscalculate risks, we sometimes behave in ways that are riskier than those we are trying to avoid. For instance, in the months following the 9/11 terrorist attacks, millions of Americans elected not to fly. A significant proportion decided to drive to their destinations instead. Driving is more dangerous than flying. And so one scholar of risk, Gerd Gigerenzer, calculated that more people died from the resulting automobile accidents than the total number of individuals who were killed aboard the four hijacked planes Sept. 11.
Kahneman believes that the news media’s disproportionate focus on cases of Western terrorism reinforces such mistaken perceptions. As he explains in his book “Thinking, Fast and Slow,” “extremely vivid image[s] of death and damage” resulting from terrorist attacks are “reinforced by media attention and frequent conversation,” leaving us with highly accessible memories of such events. When people who have been exposed to such coverage later assess how likely more terrorism is, such events come readily to mind — and so they are likely to assign probabilities biased upward.
America’s panicked obsession with Islamist terrorism is understandable but may skew public policies in costly ways. In particular, a serious public policy problem emerges when unsubstantiated fear fuels excessive public spending. More than a decade after the Sept. 11 terrorist attacks, the U.S. government has committed trillions of dollars to fighting the war on terror. Certainly, some – perhaps even most – of this funding is warranted.
Consider, however, that federal spending on improving vehicular safety and research for Alzheimer’s and diabetes pales in comparison. Yet traffic deaths, Alzheimer’s and diabetes account for hundreds of thousands of deaths each year in the United States.
Whether diverting counterterrorism funding to research in Alzheimer’s and diabetes research would save more American lives depends on the respective marginal benefits. But our government is unlikely to objectively evaluate its investments as long as most Americans have outsized fears of the threat of Islamist terrorist attacks.
To be clear, I’m not suggesting that the United States and other Western countries are facing no risk of more terror. Quite the contrary: We will almost certainly be attacked by terrorists again during the coming years and decades.
But people will also die from other unlikely events during this same period: a number of unlucky individuals will die after falling out of bed. Others will die of head injuries from coconuts falling from trees. The likelihood that you or those you love will be directly affected by any of this in your lifetime is exceedingly small.
And so perhaps the best way to counter terrorists is to do just as the French pianist who played “Imagine” in public outside the Bataclan did after the attack, or as did the widower whose wife died in the attack, and whose open letter to the terrorists included this: “I will insult you with my happiness.” We can refuse to give them the fear they so desperately want from us.
Catastrophes, natural or man-made, can make or break leaders. They offer the ultimate opportunity to show the qualities that people seek in those whom they have chosen to take command: courage, empathy, serenity, fortitude, decisiveness. Under extreme circumstances, true leadership comes to the fore; if one does not possess the requisite qualities, their lack is immediately evident to all and sundry.
Few such leaders of modern times come to mind more readily than Winston Churchill, in the face of Hitler’s aerial onslaught against Great Britain, during the Second World War. As odd as it may seem to mention Rudy Giuliani in the same paragraph as Churchill, when Giuliani was the mayor of New York, he behaved well, even heroically, during the terrorist attacks of September 11, 2001. His actions earned him a measure of public respect that, his latter-day transmogrification into Donald Trump’s chortling henchman notwithstanding, has endured, at least among certain Americans.
.. Trump behaved with negligent condescension toward the disaster from the beginning. He had made two visits to Texas in the days after Hurricane Harvey hit that state, gushing fulsomely over the handling of catastrophe and “great turnout” for his visits.
.. In a press conference, he appeared to issue a scolding for the cost of the assistance, saying, “Now, I hate to tell you, Puerto Rico, but you’ve thrown our budget a little out of whack,” and he minimized the island’s tragedy by drawing comparisons between its reportedly low death toll and the “hundreds” of people who had died in Katrina.
.. His rosy rendition stands in direct contradiction to the opinion of most Puerto Ricans, eighty per cent of whom view his response unfavorably
.. Trump is so vain he thinks this is about him. NO IT IS NOT.”
.. what is more egregious, in Puerto Rico’s case, is the obviousness of the double-standard that he has applied to the island—an unincorporated U.S. territory—and the suspicion that it is racist in nature. Trump’s sign-off on his tweet denying the death toll was, “I love Puerto Rico!” That felt about as convincing as his proclamations of “I love Hispanics!”during the 2016 Presidential campaign.
.. “After the storm, it is evident that the treatment that was given, say, Florida or Texas, was very different than the treatment given in Puerto Rico. We are second-class U.S. citizens. We live in a colonial territory. It is time to eliminate that, and I implore all the elected officials, particularly now in midterm elections, to have a firm stance. You’re either for colonial territories or against them. You’re either for giving equal rights to the U.S. citizens that live in Puerto Rico, or you’re against it.”
.. In the past, referenda have shown Puerto Ricans to be split roughly into three groups—the smallest being in favor of independence, the next largest in favor of the current relationship, and an apparently growing majority in favor of statehood.
I feel like the world has changed so much. So, in the mid-’90s, late-’90s, having been a journalist, coming out of divinity school — so this was the Moral Majority — this was this moment where a lot of very loud, strident religiosity had claimed its place and was everywhere. And actually, religion was in the headlines. And then, in the years I was creating the show, we went through September 11. We had an evangelical president in the White House. So there was a lot of religiosity in the headlines, and a lot of new curiosity about it, but also, a lot of religious people getting quiet because they didn’t want to be associated with —
MS. PERCY: With the loud voices.
MS. TIPPETT: And journalists, I felt, colluding with handing over the microphones and cameras to the loudest voices.
MS. PERCY: What time period would this be? This is the early 2000s?
MS. TIPPETT: This would be like mid- to late-’90s…
MS. PERCY: Got it.
MS. TIPPETT: …and then, into the turn of the century. And I just felt that this is such an important part of life, this huge part of life which we call religion — where religion happens, spirituality, moral imagination, and that we didn’t have any places where we were talking about the sweep of that. And even when these voices hit the news, you didn’t get the spiritual content of this part of life, much less the intellectual content of this part of life, and the nuance and really, the breadth of the ways this is lived. And so that was my desire, to do that, and I thought public radio would be a place to do that.
But I think what we started doing, from the very beginning, was drawing out a different kind of conversation, voices that weren’t being heard. It was very focused on religion per se, and then we moved through the backlash to that, which is what I think the New Atheist was, New Atheist movement. What was interesting to me about all of that, this kind of very strident anti-religion — coming through all of that, this new conversation that’s happening across these lines; across religious lines, across boundaries of religious and non-religious, all kinds of scientific inquiry, and theology and spiritual inquiry. And so, when you ask me what this is and what it’s become, it’s been so fluid and evolving.
.. here we are in 2018, in a fractured world, in a hurting world — and yet, we’re in this moment of passage, and we’re in this moment of generational change. And I think we’re in a moment where there’s huge culture shift happening, and right now the destructive aspects of that are really on display and better-covered; but there’s a lot that’s new that’s being created; there’s a lot of denial that’s dying. There’s a lot of generative possibility and people living into it, and I think the Impact Lab is just gonna equip us that much more intentionally and practically to meet that.
.. we’re really exploring this, in some ways, very old-fashioned word of “formation,” of becoming the kind of people that we are meant to be, in some way; that we are called to be, especially in this moment, and thinking about what are the spiritual technologies that can help us develop those virtues.
MS. TIPPETT: And a way of even being with strangers.
MR. TER KUILE: Absolutely. We had these long tables where people sat down at meals, and it made me realize, dinner is one of our spiritual technologies.
.. MS. PERCY: I love that you mention, Erinn, two things, which is hospitality and, also, community. As a Hispanic person, that is the tenets of being Hispanic, is — eating, as well; so it’s community, hospitality, and food. But I think those are two key things to everything that we do at The On Being Project. And community, in particular, is something that I feel so proud of, that we engage with our community in the way that we do.
.. Jean Vanier, the founder of L’Arche. And somewhere he writes that if a community is only for itself, it will die — which I thought was so striking, because I think that’s one of the things that I’m most passionate about, as we think about building community and building relationships, is that it isn’t just for itself; it’s for a world transformed in some way.