When my kids were teenagers, if something was random, that was a good thing. A really good thing, in fact. Something funny was random. A good party was random. Being more than a bit of a fussbudget, I objected to such usage. I didn’t think it was correct.
But I was wrong. Continue reading
Regular readers of this site know that I reference and write about what Nassim Taleb calls the narrative fallacy often. It is our tendency to look backward and create a pattern to fit events and to construct a story that explains what happened along with what caused it to happen. We all like to think that our decision-making is a rationally based process — that we examine the evidence and only after careful evaluation come to a reasoned conclusion as to what the evidence suggests or shows.
But we don’t. Continue reading
I have often noted (for example, here) that we generally suck at math, to our great detriment. I have also noted that we are especially poor at dealing with probabilities. If a weather forecaster says that there is an 80 percent chance of rain and it remains sunny, instead of waiting to see if it rains 80 out of 100 times when his or her forecast called for an 80 percent chance of rain, we race to conclude — perhaps based upon that single instance — that the forecaster isn’t any good. Data trumps our lyin’ eyes, but we don’t routinely see it. Continue reading
Hurricane/Superstorm Sandy rocked the eastern seaboard last week to devastating effect. In a significant instance of good planning, markets and schools were closed, states of emergency declared and mandatory evacuations begun well before the storm made landfall. Yet nearly until the storm reached land in New Jersey last Monday, I heard lots of grousing about alleged hysteria and overreaction with respect to the precautions and preparations being undertaken to mitigate potential damage (see below for a prominent example).
Some went so far as to defy evacuation orders, and some people paid for doing so with their lives. Once the storm actually hit and caused serious damage – albeit no longer officially as a hurricane, but as a “superstorm” – the complaining stopped. Fortunately, the governmental disaster preparedness organization seems to have performed well overall. You can read about these events in many venues, including here, here and here.
The pre-crisis grousing and the refusal of so many to evacuate are worth thinking about because of what is thereby revealed about us as humans and the cognitive biases that beset us. I offer three “take-away” thoughts that are broadly applicable as well as specifically applicable to the investment world.
1. We don’t deal well with probabilities. When a weather forecast says that there is a 70 percent chance of sun, we tend to think that the forecaster screwed up if it rains. But that’s not how we should evaluate probabilities. Instead, we should consider how often it rains when the forecast calls for a 70 percent chance of sun. When the forecast is spot-on perfect, it will rain 30 percent of the time when it calls for a 70 percent chance of sun. The odds favor sun, but because complex systems like the weather (and financial markets) encompass so many variables, nothing approaching certainty is possible. We don’t handle that kind of thinking very well (a very current and interesting example in a political context is examined here).
To illustrate the level of complexity I’m talking about, consider that we can construct a linear, one-dimensional chain with 10 different links in 3,628,800 different ways. For 100 different links, the possibilities total 10158. If those are the possibilities for making a simple chain, imagine the possibilities when we’re talking about complex systems where wild randomness rules.
Perhaps the key argument of Nobel laureate Daniel Kahneman’s brilliant book, Thinking Fast and Slow, is that without careful and intentional deliberation (and often even then), we suffer from probabilistic irrationality. Remember back in 2009 when New England Patriots coach (and my former New Jersey neighbor) Bill Belichick famously decided to go for a first down on fourth-and-two in Patriots territory rather than punt while up six points late against Peyton Manning and the Indianapolis Colts? When Wes Welker was stopped just short of the first down and the Colts went on to score the winning touchdown, the criticism was overwhelming even though Belichick’s decision gave the Pats a better chance of winning. Those withering attacks simply demonstrate our difficulties with probabilities. Doing what offers the best chance of success in no way guarantees success. As analyst Bill Barnwell, who was agnostic on whether Belichick was right or wrong, wrote: “you can’t judge Belichick’s decision by the fact that it didn’t work” (bold and italics in the original). We can (and should) hope for the best while preparing for the worst.
The world is wildly random. With so many variables, even the best process (when we are able to overcome our probabilistic irrationality) can be undermined at many points, a significant number of which are utterly out of anyone’s control. As Nate Silver reports in his fine new book, The Signal and the Noise, the National Weather Service is extremely good at weather forecasting in a probabilistic sense. When the NWS says there is a 70 percent chance of sun, it’s sunny just about 70 percent of the time. Because we don’t think probabilistically (and crave certainty too), we tend to assume that the forecasts on the days it rains – 30 percent of the time – are wrong. Accordingly, when a probabilistic forecast of a dangerous hurricane is generally inconsistent with our experience (“I didn’t have a problem last time”) and isn’t what we want to hear (think confirmation bias), we can readily focus on the times we remember weather forecasts being “wrong” and discount the threat. As mathematician John Allen Paulos tweeted regarding the trouble that so many seem to have election probabilities:
Many people’s notion of probability is so impoverished that it admits of only two values: 50-50 and 99%, tossup or essentially certain.
In a fascinating research study, economists Emre Soyer and Robin Hogarth showed the results of a regression analysis to a test population of economics professors. When they presented the results in the way most commonly done in economics journals (as a single number accompanied by some error measures), the economists — whose careers are largely predicated upon doing just this sort of analysis! — did an embarrassingly poor job of answering a set of questions about the probabilities of various outcomes. When they presented the results as a scatter graph, the economists got most of the questions right. Yet when they presented the results both ways, the economists got most of the questions wrong again. As Justin Fox emphasizes, there seems to be something about a single-number probability assessment that lures our primitive brains in and leads them astray.
Due to complexity and the wild randomness it entails, the investment world — like weather forecasting — offers nothing like certainty. As every black jack player recognizes, making the “right” play (probabilistically) in does not ensure success. The very best we can hope for is favorable odds and that over a long enough period those odds will play out (and even then only after careful research to establish the odds). That we don’t deal well with probabilities makes a difficult situation far, far worse.
2. We’re prone to recency bias too. We are all prone to recency bias, meaning that we tend to extrapolate recent events into the future indefinitely. Since the recent experience of residents of the eastern seaboard (Hurricane Irene) wasn’t nearly as bad as expected (despite doing significant damage), that experience was extrapolated to the present by many. When confirmation bias (we tend to see what we want to see) and optimism bias are added to the mix, it’s no wonder so many didn’t evaluate storm risk (and don’t evaluate investment risk) very well.
3. We don’t deal well with low probability, high impact events. In the aggregate, hurricanes are low-frequency but high impact events. As I have explained before, when people calculate the risk of hurricane damage and make decisions about hurricane insurance, they consistently misread their prior experience. This conclusion comes from a paper by Wharton Professor Robert Meyer that describes and reports on a research simulation in which participants were instructed that they were owners of properties in a hurricane-prone coastal area and were given monetary incentives to make smart choices about (a) when and whether to buy insurance against hurricane losses and (b) how much insurance to buy.
Over the course of the study (three simulated hurricane “seasons”), participants would periodically watch a map that showed whether a hurricane was building as well as its strength and course. Until virtually the last second before the storm was shown to reach landfall, the participants could purchase partial insurance ($100 per 10 percent of protection, up to 50 percent) or full coverage ($2,500) on the $50,000 home they were said to own. Participants were advised how much damage each storm was likely to cause and, afterward, the financial consequences of their choices. They had an unlimited budget to buy insurance. Those who made the soundest financial decisions were eligible for a prize.
The focus of the research was to determine whether there are “inherent limits to our ability to learn from experience about the value of protection against low-probability, high-consequence events.” In other words — whether experience can help us deal with tail risk. Sadly, we don’t deal with this type of risk management very well. Moreover, as Nassim Taleb has shown, such risks — while still not anything like frequent — happen much more often than we tend to think (which explains why the 2008-09 financial crisis was deemed so highly unlikely by the vast majority of experts and their models).
The bottom line here is that participants seriously under-protected their homes. The first year, they sustained losses almost three times higher than if they had bought protection rationally. The key problem was a consistent failure to buy protection or enough protection even when serious and imminent risk was obvious (sounds like people refusing to evacuate, doesn’t it?). Moreover, most people reduced the amount of protection they bought whenever they endured no damage in the previous round, even if that lack of damage was specifically the result of having bought insurance.
Experience helped a little. Participants got better at the game as season one progressed, but they slipped back into old habits when season two began. By season three, these simulated homeowners were still suffering about twice as much damage as they should have. As Meyer’s paper reports, these research results are consistent with patterns seen in actual practice. For example, the year after Hurricane Katrina there was a 53% increase in new flood-insurance policies issued nationally. But within two years, cancellations had brought the coverage level down to pre-Katrina levels.
We simply don’t do a very good job dealing with low-probability, high-impact events, even when we have experience with them. Since those in the northeast have so little experience with hurricanes, their discounting of hurricane risk is (again) even more understandable. Given what happened to the vast majority of investment portfolios in 2008-09, the alleged market “professionals” often don’t manage tail risk very well either. That said, when a low-frequency event is treated as a certainty or near-certainty as a matter of policy, that overreaction can be disastrous and the costs too high to bear, as a Navy SEAL Commander here in San Diego once took great pains to explain to me in the context of fighting terrorism.
Taleb goes so far as to assert that we should “ban the use of probability.” I disagree, but we ought to use probabilities with care and be particularly careful about how we convey probability assessments. For example, a potential range of outcomes is better than a single number (as with the scatter graphs noted above). Similarly, an outlook that shows the weighing of probabilities together with costs and potential outcomes will also help (this discussion makes a start in that direction). Despite the risks of being perceived as “crying wolf,” we intuitively understand that when and as the potential negative outcomes are greater, lower likelihood events should generally be treated more seriously and that the progression is typically non-linear.
In virtually every endeavor, our cognitive biases are a consistent problem and provide a constant challenge. In terms of investing, they can and often do rock us like a hurricane – or at least a superstorm. As Cullen Roche points out, consistent with the research noted above, we can and should learn from our investment errors, cognitive or otherwise. Sadly, we do so far less often than we ought, as last week’s events amply demonstrate.
Tom Stoppard’s Rosencrantz and Guildenstern are Dead presents Shakespeare’s Hamlet from the bewildered point of view of two of the Bard’s bit players, the comically indistinguishable nobodies who become headliners in Stoppard’s play. The play opens before our heroes have even joined the action in Shakespeare’s epic. They have been “sent for” and are marking time by flipping coins and getting heads each time (the opening clip from the movie version is shown above). Guildenstern keeps tossing coins and Rosencrantz keeps pocketing them. Significantly, Guildenstern is less concerned with his losses than in puzzling out what the defiance of the odds says about chance and fate. “A weaker man might be moved to re-examine his faith, if in nothing else at least in the law of probability.”
The coin tossing streak depicted provides us with a chance to consider these probabilities. Guildenstern offers among other explanations the one mathematicians and investors should favor —“a spectacular vindication of the principle that each individual coin spin individually is as likely to come down heads as tails and therefore should cause no surprise each individual time it does.” In other words, past performance is not indicative of future results.
Even so, how unlikely is a streak of this length?
The probability that a fair coin, when flipped, will turn up heads is 50 percent (the probability of any two independent sequential events both happening is the product of the probability of both). Thus the odds of it turning up twice in a row is 25 percent (½ x ½), the odds of it turning up three times in a row is 12.5 percent (½ x ½ x ½) and so on. Accordingly, if we flip a coin 10 times (one “set” of ten), we would only expect to have a set end up with 10 heads in a row once every 1024 sets {(½)10 = 1/1024}.
Rosencrantz and Guildenstern got heads more than 100 consecutive times. The chances of that happening are: (½)100 = 1/7.9 x 1031. In words, we could expect it to happen once in 79 million million million million million (that’s 79 with 30 zeros after it) sets. By comparison, the universe is about 13.9 billion years old, in which time only about 1017 seconds (1 with 17 zeros after it) have elapsed. Looked at another way, if every person who ever lived (around 110 billion) had flipped a 100-coin set simultaneously every second since the beginning of the universe (again, about 13.9 billion years ago), we could expect all of the 100 coins to have come up heads two times.
If anything like that had happened to you (especially in a bet), you’d agree with Nassim Taleb that the probabilities favor a loaded coin. But then again, while 100 straights heads is less probable than 99, which is less probable than 98, and so on, any exact order of tosses is as likely (actually, unlikely) as 100 heads in a row: (½)100. We notice the unlikelihood of 100 in a row because of the pattern and we are pattern-seeking creatures. More “normal” combinations look random and thus expected. We don’t see them as noteworthy. Looked at another way, if there will be one “winner” selected from a stadium of 100,000 people, each person has a 1 in 100,000 chance of winning. But we aren’t surprised when someone does win, even though the individual winner is shocked.
The point here is that the highly improbable happens all the time. In fact, much of what happens is highly improbable. This math explains why we shouldn’t be surprised when the market remains “irrational” far longer than seems possible. But we are.
Much of that difficulty arises because we neglect the limits of induction. Science never fully proves anything. It analyzes the available data and, when the force of the data is strong enough, it makes tentative conclusions. But these conclusions are always subject to modification or even outright rejection based upon further evidence gathering. Instead, we crave and claim certainty, even when we have no basis for it.
In his brilliant book, On Being Certain, neurologist Robert Burton systematically and convincingly shows that certainty is a mental state, a feeling like anger or pride that can help guide us, but that doesn’t dependably reflect anything like objective truth. One disconcerting finding he describes is that, from a neurocognitive point of view, our feelings of certainty about things we’re right about is largely indistinguishable from our feelings of certainty about things we’re wrong about (think “narrative fallacy” and “confirmation bias”).
As Columbia’s Rama Cont points out, “[w]hen I first became interested in economics, I was surprised by the deductive, rather than inductive, approach of many economists.” In the hard sciences, researchers tend to observe empirical data and then build a theory to explain their observations, while “many economic studies typically start with a theory and eventually attempt to fit the data to their model.” As noted by Emanuel Derman:
In physics it’s fairly easy to tell the crackpots from the experts by the content of their writings, without having to know their academic pedigrees. In finance it’s not easy at all. Sometimes it looks as though anything goes.
I suspect that these leaps of ideological fancy are a natural result of our constant search for meaning in an environment where noise is everywhere and signal vanishingly difficult to detect. Randomness is difficult for us to deal with. We are meaning-makers at every level and in nearly every situation. Yet, as I have noted before, information is cheap and meaning is expensive. Therefore, we tend to short-circuit good process to get to the end result – typically and not so coincidentally the result we wanted all along.
As noted above, science progresses not via verification (which can only be inferred) but by falsification (which, if established and itself verified, provides relative certainty only as to what is not true). Thank you, Karl Popper. In our business, as in science generally, we need to build our investment processes from the ground up, with hypotheses offered only after a careful analysis of all relevant facts and tentatively held only to the extent the facts and data allow. Yet the markets demand action. There is nothing tentative about them. That’s the conundrum we face.
Even after 100 heads in a row, the odds of the next toss being heads remains one-in-two (the “gambler’s fallacy” is committed when one assumes that a departure from what occurs on average or in the long-term will be corrected in the short-term). We look for patterns (“shiny objects”) to convince ourselves that we have found a “secret sauce” that justifies our making big bets on less likely outcomes. In this regard, we are dumber than rats – literally.
In numerous studies (most prominently those by Edwards and Estes, as reported by Philip Tetlock in Expert Political Judgment), the stated task was predicting which side of a “T-maze” holds food for the subject rat. Unbeknownst both to observers and the rat, the maze was rigged such that the food was randomly placed (no pattern), but 60 percent of the time on one side and 40 percent of the time on the other.
The rat quickly “gets it” and waits at the “60 percent side” every time and is thus correct 60 percent of the time. Human observers kept looking for patterns and chose sides in rough proportion to recent results. As a consequence, the humans were right only 52 percent of the time – they (we!) were much dumber than rats. Overall, we insist on rejecting probabilistic strategies that accept the inevitability of randomness and error.
As I described yesterday, the great gambler Billy Walters uses a probabilistic sports betting model that is correct roughly 57 percent of time. He expects and plans for being wrong 43 percent of the time. Since he can’t predict the timing of his successes and failures, he has to be prepared for long losing streaks (although he obviously hopes that none are long as Guildenstern’s). Common gambling practice had been (and often still is) to make fewer bets – to bet only on those games one is most sure of. But that approach is not thinking probabilistically. Walters makes as many bets as he can within the confines of his model (when he thinks the point spread is off by at least one-and-one-half points).
For investors, the lessons to be gained here relate to diversification, a carefully delineated and bounded process, clear execution rules, and stick-to-itiveness over the long haul. This doesn’t mean that quants should control everything. Old school analysis and judgment still matter, perhaps more than ever since the pile of available data has gotten so large. But it does mean that our conclusions need to be consistent with and supported by the data, no matter how bizarre the numbers or how long the streak.
Even 100 in a row.
The photograph above, taken by German photographer Thomas Hoepker, is one of the iconic images of 9.11. The picture was taken at the Brooklyn waterfront on the afternoon of that infamous day eleven years ago. In Hoepker’s words, he saw “an almost idyllic scene near a restaurant — flowers, cypress trees, a group of young people sitting in the bright sunshine of this splendid late summer day while the dark, thick plume of smoke was rising in the background.” By his reckoning, even though he had paused but for a moment and didn’t speak to anyone in the picture, Hoepker was concerned that the people in the photo “were not stirred” by the events at the World Trade Center — they “didn’t seem to care.” Even though he published many images from that day, Hoepker withheld this picture for over four years because, in his view, it ”did not reflect at all what had transpired on that day.”
In 2006, the image was published in David Friend’s book, Watching the World Change. Comments from Hoepker were included. Frank Rich then wrote a 9.11 fifth anniversary column in The New York Times about the photo, calling it “shocking.” Rich suggested that the five New Yorkers were “relaxing” and were already “mov[ing] on” from the attacks. Rich described those in the photo as being on ”what seems to be a lunch or bike-riding break, enjoying the radiant late-summer sun and chatting away as cascades of smoke engulf Lower Manhattan in the background.”
Here is more of the explanatory narrative Rich created:
Mr. Hoepker’s photo is prescient as well as important — a snapshot of history soon to come. What he caught was this: Traumatic as the attack on America was, 9/11 would recede quickly for many. This is a country that likes to move on, and fast. The young people in Mr. Hoepker’s photo aren’t necessarily callous. They’re just American.
Others reacted similarly. It was a plausible interpretation based upon the information available (the picture). More importantly, it framed Rich’s desired narrative perfectly.
Daniel Plotz quickly came forward with an alternative interpretation that disputed Rich, calling Rich’s reading of the image a “cheap shot.” In Plotz’s view the five had not ignored or moved beyond 9.11 but had “turned toward each other for solace and for debate.” To his credit, Plotz emphasized that he didn’t “really know” what the pictured people were doing and feeling and called upon them to contact him so as to set the record straight. Two did, and they repudiated Rich’s narrative in the strongest of terms.
The first to respond was Walter Sipser, a Brooklyn artist and the man on the far right in the shot. “A snapshot can make mourners attending a funeral look like they’re having a party,” he wrote. “Had Hoepker walked fifty feet over to introduce himself he would have discovered a bunch of New Yorkers in the middle of an animated discussion about what had just happened.” Chris Schiavo, a professional photographer, Sipser’s then-girlfriend and second from the right above, also responded. She criticized both Rich and Hoepker for their “cynical expression of an assumed reality.” As a “third-generation native New Yorker, who knows and loves every square inch of this city,” whose “mother even worked for Minoru Yamasaki, the World Trade Center architect,” she stated that “it was genetically impossible for [her] to be unaffected by this event.”
So much for Rich’s narrative explanation.
We like to think that we carefully gather and evaluate facts and data before coming to a conclusion. Instead, we tend to suffer from confirmation bias and thus reach a conclusion first. Only thereafter do we gather facts and see those facts in such a way as to support our pre-conceived conclusion. When it fits with our desired narrative, so much the better, because narratives are crucial to how we make sense of reality. They help us to explain, understand and interpret the world around us. They also give us a frame of reference we can use to remember the concepts we take them to represent. Perhaps most significantly, we inherently prefer narrative to data — often to the detriment of our understanding. Keeping one’s analysis and interpretation of the data reasonably objective – since analysis and interpretation are required for data to be actionable – is really, really hard even in the best of circumstances.
An interesting piece by John Allen Paulos for The New York Times sheds additional light on the stories versus statistics dialectic. “There is a tension between stories and statistics, and one under-appreciated contrast between them is simply the mindset with which we approach them. In listening to stories we tend to suspend disbelief in order to be entertained, whereas in evaluating statistics we generally have an opposite inclination to suspend belief in order not to be beguiled.”
As this tale of the unfortunate Mr. Rich demonstrates, we are all too prone to confirmation bias and to its corollary, what Nassim Taleb calls the “narrative fallacy” (looking backward and creating a pattern to fit events and constructing a story that explains what happened along with what caused it to happen). This story has some very serious and practical implications related to life in general and to investing in particular.
1. Be skeptical about the data you collect, the patterns you detect, your interpretations thereof and the conclusions you draw.
2. Keep looking for more data — especially data that questions or contradicts your assumptions, hypotheses and conclusions.
3. Be especially skeptical of the story you think the data tells.
4. Rinse and repeat #1, #2 and #3 as appropriate (pretty much all the time).
We are wildly overconfident about our abilities generally. The more we repeat and reiterate one of our explanatory narratives, the harder it is to recognize evidence that ought to cause us to re-evaluate our previous conclusions. By making it a careful habit skeptically to re-think our prior interpretations and conclusions, we at least give ourselves a fighting chance to correct the mistakes that we will inevitably make.
As with everything in science, every conclusion we draw must be tentative and subject to revision as the facts demand. As John Maynard Keynes famously stated, “When the facts change, I change my mind. What do you do, sir?” Indeed, what do you do?
Note: This post is a reprint. It first appeared here.
In what is now a ubiquitous concept, a “black swan” is an extreme event that lies beyond the realm of our normal expectations and which has enormous consequences (e.g., Donald Rumsfeld’s “unknown unknowns”). It is by definition an outlier. Examples include the rise of Hitler, winning the lottery, the fall of the Berlin Wall and the ultimate demise of the Soviet bloc, the development of Viagra (which was originally designed to treat hypertension before a surprising side effect was discovered) and of course the 9.11 atrocities.
As Nassim Taleb famously pointed out in his terrific book outlining the idea, most people (at least in the northern hemisphere) expect all swans to be white because that is consistent with their personal experience. Thus a black swan (native to Australia) is necessarily a surprise. Yet, once discovered, we tend to concoct explanations for black swans which make them appear more predictable and less random than they actually are. This tendency is called the “narrative fallacy.” Our minds are designed to retain, for efficient storage, past information that fits into a compressed narrative. This distortion, the “hindsight bias,” prevents us from adequately learning from what has gone on before.
Black swans also have extreme effects, both positive and negative. Even though I think that Taleb overstates their overall significance somewhat, just a few explain a surprising amount of our history, from the success of some ideas and discoveries to events in our personal lives. Moreover, their influence seems to have grown beginning in the 20th century (on account of globalization and growing interconnectedness), while ordinary events — the ones we typically study, discuss and learn about in history books or from the news — seem increasingly inconsequential. A fascinating discussion of these ideas within the context of the 2008-09 financial crisis between Taleb and Nobel laureate Daniel Kahneman is available on video here.
Higher levels of complexity lead to systems that are increasingly fragile and susceptible to sudden, spectacular collapse. Indeed, John Casti’s X-Events argues that today’s highly advanced and overly complex societies have grown highly vulnerable to extreme events that may ultimately result in the collapse of our civilization. Examples could include a global internet or technological collapse, transnational economic meltdown or even robot uprisings.
Per Andrew Zolli in the Harvard Business Review, CalTech system scientist John Doyle calls such systems Robust-Yet-Fragile. While they are good at dealing with anticipated threats, they are quite poor at dealing with unanticipated ones. Accordingly, as the complexity of these systems grows, both the sources and severity of possible disruptions increases. Meanwhile, the size required for potential ‘triggering events’ decreases. Thus it may only take a tiny event, at the wrong place or at the wrong time, to spark a calamity. While the chances of any of these possibilities actually happening is individually remote, our general susceptibility to that type of catastrophe is surprisingly real.
Taleb applies these concepts to the investment world by trying to be extremely risk-averse where the risks are high and the potential gains are small and extremely aggressive where the costs are low and the potential gains are high. He thus worries less about small failures and more about large, potentially terminal ones. He worries more about conventional investments and less about the truly speculative ones. In essence, Taleb wants to gain exposure to positive Black Swans — when a failure would be of small moment — and avoid those situations where he is under threat from a negative Black Swan. Taleb’s forthcoming book will seek to examine these ideas in more detail.
To be sure, it can be hard to distinguish between black swan “positioning” and long-range forecasting. Current long-term investment approaches focusing on food, farmland and timber probably fall into this category. Moreover, any number of potential extreme events are simply too extreme to deal with in more than a rudimentary way. For example, if the entire economic system melts down, it won’t likely matter how short the market you are at the time. Even so, there are lessons to be learned and actions that can be considered and taken to mitigate these (seemingly increasing) risks.
Some tentative conclusions follow.
The full series on risk is available here.
The photograph above, taken by German photographer Thomas Hoepker, is one of the iconic images of 9.11. The picture was taken at the Brooklyn waterfront on the afternoon of that infamous day. In Hoepker’s words, he saw “an almost idyllic scene near a restaurant — flowers, cypress trees, a group of young people sitting in the bright sunshine of this splendid late summer day while the dark, thick plume of smoke was rising in the background.” By his reckoning, even though he had paused but for a moment and didn’t speak to anyone in the picture, Hoepker was concerned that the people in the photo “were not stirred” by the events at the World Trade Center — they “didn’t seem to care.” Even though he published many images from that day, Hoepker withheld this picture for over four years because, in his view, it ”did not reflect at all what had transpired on that day.”
In 2006, the image was published in David Friend’s book, Watching the World Change. Comments from Hoepker were included. Frank Rich then wrote a 9.11 fifth anniversary column in The New York Times about the photo, calling it “shocking.” Rich suggested that the five New Yorkers were “relaxing” and were already “mov[ing] on” from the attacks. Rich described those in the photo as being on ”what seems to be a lunch or bike-riding break, enjoying the radiant late-summer sun and chatting away as cascades of smoke engulf Lower Manhattan in the background.”
Here is more of the explanatory narrative Rich created:
Mr. Hoepker’s photo is prescient as well as important — a snapshot of history soon to come. What he caught was this: Traumatic as the attack on America was, 9/11 would recede quickly for many. This is a country that likes to move on, and fast. The young people in Mr. Hoepker’s photo aren’t necessarily callous. They’re just American.
Others reacted similarly. It was a plausible interpretation based upon the information available (the picture). More importantly, it framed Rich’s desired narrative perfectly.
Daniel Plotz quickly came forward with an alternative interpretation that disputed Rich, calling Rich’s reading of the image a “cheap shot.” In Plotz’s view the five had not ignored or moved beyond 9.11 but had “turned toward each other for solace and for debate.” To his credit, Plotz emphasized that he didn’t “really know” what the pictured people were doing and feeling and called upon them to contact him so as to set the record straight. Two did, and they repudiated Rich’s narrative in the strongest of terms.
The first to respond was Walter Sipser, a Brooklyn artist and the man on the far right in the shot. “A snapshot can make mourners attending a funeral look like they’re having a party,” he wrote. “Had Hoepker walked fifty feet over to introduce himself he would have discovered a bunch of New Yorkers in the middle of an animated discussion about what had just happened.” Chris Schiavo, a professional photographer, Sipser’s then-girlfriend and second from the right above, also responded. She criticized both Rich and Hoepker for their “cynical expression of an assumed reality.” As a “third-generation native New Yorker, who knows and loves every square inch of this city,” whose “mother even worked for Minoru Yamasaki, the World Trade Center architect,” she stated that “it was genetically impossible for [her] to be unaffected by this event.”
So much for Rich’s narrative explanation.
We like to think that we carefully gather and evaluate facts and data before coming to a conclusion. Instead, we tend to suffer from confirmation bias and thus reach a conclusion first. Only thereafter do we gather facts and see those facts in such a way as to support our pre-conceived conclusion. When it fits with our desired narrative, so much the better, because narratives are crucial to how we make sense of reality. They help us to explain, understand and interpret the world around us. They also give us a frame of reference we can use to remember the concepts we take them to represent. Perhaps most significantly, we inherently prefer narrative to data — often to the detriment of our understanding. Keeping one’s analysis and interpretation of the data reasonably objective – since analysis and interpretation are required for data to be actionable – is really, really hard even in the best of circumstances.
An interesting piece by John Allen Paulos for The New York Times sheds additional light on the stories versus statistics dialectic. “There is a tension between stories and statistics, and one under-appreciated contrast between them is simply the mindset with which we approach them. In listening to stories we tend to suspend disbelief in order to be entertained, whereas in evaluating statistics we generally have an opposite inclination to suspend belief in order not to be beguiled.”
As this tale of the unfortunate Mr. Rich demonstrates, we are all too prone to confirmation bias and to its corollary, what Nassim Taleb calls the “narrative fallacy” (looking backward and creating a pattern to fit events and constructing a story that explains what happened along with what caused it to happen). This story has some very serious and practical implications related to life in general and to investing in particular.
1. Be skeptical about the data you collect, the patterns you detect, your interpretations thereof and the conclusions you draw.
2. Keep looking for more data — especially data that questions or contradicts your assumptions, hypotheses and conclusions.
3. Be especially skeptical of the story you think the data tells.
4. Rinse and repeat #1, #2 and #3 as appropriate (pretty much all the time).
We are wildly overconfident about our abilities generally. The more we repeat and reiterate one of our explanatory narratives, the harder it is to recognize evidence that ought to cause us to re-evaluate our previous conclusions. By making it a careful habit skeptically to re-think our prior interpretations and conclusions, we at least give ourselves a fighting chance to correct the mistakes that we will inevitably make.
As with everything in science, every conclusion we draw must be tentative and subject to revision as the facts demand. As John Maynard Keynes famously stated, “When the facts change, I change my mind. What do you do, sir?” Indeed, what do you do?
In the aftermath of the unfortunate death of Steve Jobs, many have highlighted his brilliant 2005 commencement speech at Stanford. I did too. Jobs framed his speech around three stories from his life.
We’re all familiar with how these things are supposed to go. Brilliant business executive explains his success by focusing on his success (it’s far too often a “he”) and encouraging us that we can make it too.
But Steve Jobs was anything but typical.
We all tend to be enamored with success and those that achieve it. We are all prone to what Nassim Taleb calls the “narrative fallacy.” We want to see agency in the world. We want to understand everything in terms of intent, but sometimes (many times) the “cause” is pure noise and worthless as a teacher.
The three stories Jobs chose to focus on at Stanford were difficult, heart-wrenching and painful: dropping out of college; getting fired from Apple in 1985; and being diagnosed with cancer. Success came from them, as Jobs explains, but only much later and after considerable struggle and difficulty. His approach was counterintuitive, but correct. Thus, as usual, Steve Jobs was ahead of the curve.
By spending so much time looking at success, we learn the wrong lessons. It is true, as Taleb concedes, that “chance favors the prepared.” However, as Taleb also points out, those successes have as much to do with randomness and noise as with talent, plan and execution. That’s a major reason why we are such bad forecasters. As a consequence, we should be much more focused on failure than success, consistent with the philosophy of science developed by Karl Popper.
During World War II, England sent regular bombing raids into Germany. Many planes never returned and those that did were often riddled with damage from German anti-aircraft guns and fighters. Wanting to improve survivability, the English Air Ministry examined the locations of the bullet holes on the returned aircraft and proposed that reinforcement be added to those areas that showed the most damage.
The mathematician Abraham Wald, however, suggested otherwise (see his research here or read about it here).
Wald’s unique insight was that the holes from flak and bullets on the bombers that returned represented the areas where planes were able to absorb damage and survive. Since the data showed that there were similar areas on each returning B-29 showing no damage from enemy fire, Wald concluded that those areas (around the main cockpit and the fuel tanks) were the real weak spots and that they must be reinforced.
The more useful data was in the planes that were shot down, not the ones that survived, and had to be “gathered” by induction in that instance. This insight lies behind what we now call survivorship bias – our tendency to include only successes in statistical analysis, skewing the results.
In most cases we’d be better served by looking closely at the stories of those who failed and why instead of the success stories, even though such people are unlikely to get great book contracts and six-figure advances. Similarly, we’d be better served examining our personal investment failures than our successes. That’s where the best data is and where the best insight may be inferred.
As I have noted before, investing is a loser’s game much of the time – with outcomes dominated by luck rather than skill, and high transaction costs. If we avoid mistakes we will generally win. By examining failure more closely, we’ll have a better chance of doing precisely that.
Press on!
Perspectives on capital markets and personal finance
Perspectives on capital markets and personal finance
Perspectives on capital markets and personal finance
Perspectives on capital markets and personal finance
a forum for news, ideas and commentary on the art and science of long-term investing
Perspectives on capital markets and personal finance
Perspectives on capital markets and personal finance
"In individuals, insanity is rare; but in groups, parties, nations and epochs it is the rule" - Nietzsche
Perspectives on capital markets and personal finance
Perspectives on capital markets and personal finance
Perspectives on capital markets and personal finance
Perspectives on capital markets and personal finance
Perspectives on capital markets and personal finance
Perspectives on capital markets and personal finance
