Tag: Michael Mauboussin

15 June, 2017

Narendra Modi and the Oil Lottery

light-diesel-oil-250x250

Three weeks ago, the Narendra Modi government completed three years in office. On the occasion, the media went to town discussing the performance of the government. The general opinion among analysts, television anchors and economists, who have a thing or two to say on such matters, was that the government had done well on the economic front, given that that the Indian economy grew by 7.5 per cent per year over the last three years. In coming to this conclusion, these individuals did not take one thing into account: the falling price of crude oil.

When Narendra Modi was sworn in as prime minister in late May 2014, the price of the Indian basket of crude oil was a little over $108 per barrel. As of June 8, 2017, a little over three years later, the price of the Indian basket of crude oil stood at $48 per barrel, around 56 per cent lower.

Lest I be accused of making only a point to point to comparison, take a look at the following figure.

Source: Petroleum Planning and Analysis Cell.

In May 2014, the average daily price of the Indian basket of crude oil was $106.85 per barrel. It rose in June 2014 to $109.05 per barrel. And then the price of oil started to fall. Since June 2014, the overall trend in oil prices, has been on its way down (as can be seen by the red arrow). Even though prices have gone up in the recent past, they are well below where they were between mid-2011 and mid-2014.

This can be best described as the luck of Narendra Modi. At least on the economic front, lower oil prices have made the Modi government look good.

I first made this point in the weekly Letter that I write. The foremost impact of lower oil prices has been on the rate of economic growth. Let’s try and create a counterfactual situation here by trying to figure out how economic growth would have turned out to be if the oil prices in the last three years, were as high as they were during the time when Manmohan Singh was the prime minister.

At its most basic level, the gross domestic product (GDP) is expressed as Y = C + I + G + NX, where:

Y = GDP
C = Private Consumption Expenditure
I = Investment
G = Government Expenditure
NX = Exports minus imports

India imports around four-fifths of the oil that it consumes. To be very precise, in 2016-2017, the actual import dependency or the proportion of crude oil consumed that is imported stood at 82.1 per cent.

Given this, net exports (or NX) in the GDP tends to be a negative number. Higher oil prices essentially ensure that oil imports go up. Oil imports going up leads to the net exports number becoming a larger negative entry. In the process, the GDP number comes down and the GDP growth comes down as well.

The reverse is also true. Hence, when oil prices come down, the NX number comes down, the GDP goes up, and in the process the GDP growth goes up as well. This is precisely what has happened over the last three years.

As per the latest GDP numbers declared on May 31, 2017, the economic growth during the last three years stood at 7.5 per cent per year. This was primarily because the average price of Indian crude oil between April 2014 and March 2017, stood at $59.3 per barrel. In comparison, the average price of crude oil between April 2011 and March 2014 had been $108.5 per barrel.

Now, let’s assume that the average net exports figures were at the same level during the Narendra Modi years as they had been when Manmohan Singh was the prime minister between 2011-2012 and 2013-2014. We are basically trying to figure out as to what would have happened if the price of oil had continued to be at a high level even after 2014.

What impact would have this had on the economic growth? The economic growth during the three-year period that Modi has governed would have been 6.5 per cent per year, and not the 7.5 per cent that it has come to. This is nearly 100 basis points lower.

Let’s compare this to the economic growth during the last two years of Manmohan Singh’s government. (I am using the last two years because in case of the new GDP series launched in January 2015, data starts only from 2011-2012.) The economic growth stood at 5.9 per cent.

While this is lower than the three-year economic growth during Modi’s era, it is not as low as it initially seemed. And that is primarily because of lower oil prices during Modi’s time as the prime minister.

So, lower oil prices have bumped up the economic growth figure. They have also benefited the government in another way. The benefit of lower oil prices hasn’t been passed on to consumers in the form of lower petrol and diesel prices.

As mentioned earlier, between May 2014 and now, the price of the Indian basket of crude oil has fallen by 56 per cent. During the same period, the petrol price in Mumbai has fallen by a mere 1.9 per cent. In case of diesel, the price has fallen by only 3.6 per cent.

Hence, the central government and the state governments have totally managed to capture the fall in oil prices. If we look at the central government, the net excise duty collections of the central government stood at around Rs 1,76,535 crore in 2013-2014. This has jumped by more than 100 per cent to Rs 3,87,369 crore by 2016-2017, primarily because the government chose to capture a bulk of the fall in price of oil by increasing excise duty on petrol as well as diesel.

This helped the government to keep increasing its expenditure without having to bother about a large fiscal deficit.

It is interesting to speculate what would have happened if the government had passed on the fall in the price of crude oil to consumers in the form of lower petrol as well as diesel prices. Consumers would have had more money to spend. And robust consumer spending is always a better way to create economic growth than a terribly leaky government spending.

To conclude, while the Modi government has done better in the last three years than the Manmohan Singh government did in its last two years, the fall in the price of crude oil has been a major reason behind it. Modi has been terribly lucky, and it’s time that analysts and economists acknowledged this reality.

The interesting thing here is that people have a hard time distinguishing between luck and skill. As Michael Mauboussin writes in The Success Equation: “Our minds have an amazing ability to create a narrative that explains the world around us, an ability that works particularly well when we already know the answer.” In Modi’s case, this has meant attributing India’s good official economic growth rate to his skill rather than to the fact that he got lucky.

The column originally appeared on Thinkpragati.com on June 14, 2017.

12 December, 2013

The curious case of Mr Jain

Vivek Kaul

Sometime in late October I went to meet my investment advisor. During the course of our discussion he suggested that my portfolio was skewed towards HDFC Mutual Fund and it would be a good idea to move some money out of it, into other funds.
“Don’t put all your eggs in one basket” is an old investment adage. While, I try to follow it, I also like to believe that if the basket is good enough, it makes sense to put more eggs in that basket than other baskets.
HDFC Mutual Fund has been one of the few consistent performers in the Indian mutual fund space. And a major reason for the same has been Prashant Jain, the chief investment officer of the fund, who has been with it for nearly two decades.
Jain has been a star performer and due to his reputation the fund has seen a huge inflow of money into its various schemes. Some of these schemes HDFC Prudence, HDFC Equity and HDFC Top 200 became very big in that process.
These schemes haven’t done very well over the last three years. Their performance has been significantly worse in comparison to other schemes in their respective categories(Value Research has downgraded them to three star funds from being five star funds earlier). And this has surprised many people. “How can Prashant Jain not perform?” is a question close observers of the mutual fund industry in India have been asking.
One explanation that people seem to have come up with is the fact that the size of the schemes have become big, making it difficult for Jain to generate significant return. This is a theory that is globally accepted, where the size of a scheme is believed to be inversely proportional to the return it generates.
As Jason Zweig points out in the commentary to Benjamin Graham’s all time investment classic, The Intelligent Investor, “As a (mutual) fund grows, it fees become more lucrative – making its managers reluctant to rock the boat. The very risk that managers took to generate their initial high returns could now drive the investors away — and jeopardise all that fee income. So the biggest funds resemble a herd of identical and overfed sheep, all moving in sluggish lockstep, all saying “Baaaa” at the same time.”
While this may be a reason for the underperformance of the schemes managed by Jain, it is not easy to prove this conclusively. Jain feels there is no correlation between size and performance of a scheme, or so he told the Forbes India magazine in a recent interview. He pointed out that there are no large mutual fund schemes in India, and the largest scheme is less than 0.2% of the market capitalisation, which I guess is a fair point to make.
So how does one explain the fact that Prashant Jain is not doing as well as he used to in the past. John Allen Paulos possibly has an explanation for it in his book A Mathematician Plays the Stock Market. As he writes “A different argument points out to the near certainty of some stocks, funds, or analysts doing well over an extended period of time.”
Paulos offers an interesting thought experiment to make his point. As he writes “Of 1000 stocks (or funds or analysts), for example, roughly 500 might be expected to outperform the market next year simply by chance, say by the flipping of a coin. Of these 500, roughly 250 might be expected to do well for a second year. And of these 250, roughly 125 might be expected to continue the pattern, doing well three years in a row simply by chance. Iterating in this way, we might reasonably expect there to be a stock (or fund or analyst) among the thousand that does well for ten consecutive years by chance alone.”
But one day this winning streak comes to an end. And the same seems to have happened to Prashant Jain. In fact, William Miller who ran the Legg Mason Value Trust fund in the United States, beat the broader market every year from 1991 to 2005. In 2006, his luck finally ran out. Miller once explained his winning streak by saying “As for the so-called streak…We’ve been lucky. Well, maybe it’s not 100% luck—maybe 95% luck.”
If Miller was lucky so was Jain. Any significant deviation from the norm does not last forever. As Nassim Nicholas Taleb writes in Fooled by Randomness “In real life, the larger the deviation from the norm, the larger the probability of it coming from luck rather than skills…The “reversion” for the large outliers is what has been observed in history and explained as regression to the mean. Note the larger the deviation, the more important its effect.”
This is not to suggest that Jain’s performance has only been because of luck. Not at all. But it was luck that pushed him up to the top of the charts. Luck was the “icing” on the cake.
Michael Mauboussin discusses a very interesting concept called the paradox of skill in his book The Success Equation – Untangling Skill and Luck in Business, Sports, and Investing. “As skill improves, performance becomes more consistent, and therefore luck becomes more important,” is how Mauboussin defines the paradox of skill.
The Olympic marathon is a very good example of the same. Men run the race today about 26 minutes faster than they did 80 years back. Also, in 1932, the difference between the man who won the race and the man who came in twentieth was 40 minutes. Now its less than 10 minutes.
Now the question is how does this apply to investing? “As the market is filled with participants who are smart and have access to information and computing power, the variance of skill will decline. That means that stock price changes will be random and those investors who beat the market can chalk up their success to luck. And the evidence shows that the variance in mutual fund returns has shrunk over the past 60 years, just as the paradox of skill would suggest,” says Mauboussin. “I want to be clear that I believe that differential skill in investing remains, and that I don’t believe that all results are from randomness. But there’s little doubt that markets are highly competitive and that the basic sketch of the paradox of skill applies,” he adds.
And that is what best explains the curious case of Prashant Jain and the recent non performance of the mutual fund schemes that he manages.
The column originally appeared in the Wealth Insight magazine edition of December, 2013

(Vivek Kaul is the author of Easy Money. He tweets @kaul_vivek)

1 December, 2012

In short run, even Playboy bunnies can beat fund managers

Michael J. Mauboussin is Chief Investment Strategist at Legg Mason Capital Management in the United States. He is also the author of bestselling books on investing like Think Twice: Harnessing the Power of Counterintuition and More Than You Know: Finding Financial Wisdom in Unconventional Places. His latest book The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing (Harvard Business Review Press, Rs 995) has just come out.In this freewheeling interview with Vivek Kaul, he talks about how short term outcomes have a good dose of luck attached to them and how as people get more skillful at doing a particular thing, luck becomes the deciding factor. This is the second and the concluding part of the interview.
What is the undersampling of failure?
Undersampling failure means that when we review the past to see what may work in the future, we have a tendency to dwell on success and not examine failure. Think of it this way. Say companies could choose one of two strategies, risky or safe. Of the companies that choose the risky strategy, some succeed wildly and others fail. Of the companies that choose the safe strategy, some are mildly successful and others mildly disappointing, but all stay in business. In other words, the outcomes for the risky strategy have high variance and the outcomes for the safe strategy have a low variance. Now you’re a new company coming along and want to be successful. So you study the records of companies and see that the wildly successful ones are those that selected the risky strategy. You don’t see the companies that chose the risky strategy and failed because they are dead. That is undersampling failure. The question is not: which strategy did the successful companies pursue? The question is: what are the outcomes of all of the companies that chose this strategy? A related point is that the success of a strategy only occurs with some probability. Major variables, including the tastes of consumers, the actions of competitors, and technological change, are simply impossible to fully anticipate.
Can you give us an example?
Michael Raynor, a consultant at Deloitte, offers the example of Sony’s MiniDisc. He argues that Sony’s strategy was brilliantly conceived, yet the product totally flopped. There are no sure things.

You write that “organizations tends to overestimate the degree to which the star’s skills are transferrable”…
This argument is an extension of the work done by Boris Groysberg, a professor at Harvard Business School. Groysberg has studied many cases in which stars switch organizations and found that in most cases their performance deteriorates. So skill is not as portable as we tend to think. One example he provides is that that of executives from General Electric. GE is well known to have among the best management training programs in the world. Further, rising to the ranks of GE management undoubtedly requires skill. Groysberg studied 20 executives who left GE over a two decade span and who took leadership positions at other companies. What he found was that those who went to firms organizationally very similar to GE tended to do quite well, while those that went to firms that were different fared poorly. So it’s not just skills that matter, but the match between skills and the environment.

You talk about the Playboy Playmates selecting stocks that generated greater returns than the broader market. Could you take our readers through that story?

As a promotional stunt, a trading company asked former Playboy Playmates to pick 10 stocks at the beginning of 2006. The best of the Playmates did much better than the market, and a higher percentage of Playmates outperformed the market than did professional money managers. Right away, this should cause you to ponder an important question. How can a handful of presumably untrained individuals outperform diligent and dedicated professionals? In how many fields can amateurs beat the professionals?

What is the broader point you were trying to make?

The broader point I was making is that in fields where there is a good dose of luck, short-term outcomes do little to reveal differential skill. Over a longer period, you would most certainly expect the pros to do better than the amateurs. But it is common in business and investing to use evaluation periods that are simply too short to allow for any kind of concrete conclusions about differences in skill.

What is the paradox of skill?

The paradox of skill says that as skill improves in an activity that includes both skill and luck, then luck becomes more important in determining outcomes. So more skill leads to more luck, the paradox.The paradox of skill says that as competitors in a field become more skillful, luck becomes more important in determining results. The key to this idea is what happens when skill improves in a field. There are two effects. First, the absolute level of ability rises. And second, the variance of ability declines.

Could you explain through an example?

Let me give one example from athletics and then turn to investing. The paradox of skill makes a specific, testable prediction in sports that are measured against a clock. You should see absolute times improve, bumping into the limits of human physiology, and you see relative times cluster, which means that the finishers are all bunched. This is precisely what we see in swimming and marathons. Men today run the race about 26 minutes faster than they did 80 years ago. But in 1932, the time difference between the man who won and the man who came in 20^th was close to 40 minutes. Today that difference is well under 10 minutes.

And the investing example?

The idea applies well to investing and has been the subject of discussion for decades. For example, Charles Ellis wrote a famous essay in 1975 called “The Loser’s Game,” which makes the same essential point. Ellis argued that in the 1950s and 1960s, institutional investors could outwit individuals because there was a wide range of skill. But as the markets became dominated by institutions, the difference in skill narrowed making the game harder to win.In investing, the idea is that skilled investors are very efficient at reflecting information into asset prices. So only new information, which is by definition random, should affect stock prices. Hence, stocks follow a “random walk.” This is a statement of the efficient market hypothesis. Now, the efficient market hypothesis is not accurate. Stock price movements do not follow random walks, and there is differential skill. But the basic point remains true. Because prices capture the skill of investors, luck is very important in determining results—especially in the short term.

You write “people who work in businesses where social influence operates are often paid for good luck, although they generally don’t suffer symmetrically from bad luck.” Can you explain this statement in the context of the financial crisis.

I think this idea is why some many people were so upset by the financial crisis. The basic idea is that gains are privatized when times are good and socialized when times are bad. In other words, executives make lots of money in good times and taxpayers have to bail out companies in bad times. That feels deeply unfair.

This is also relevant in equity based compensation. Stock price moves reflect changes in the expectations of a company’s prospects plus macro factors such as interest rates, tax rates, regulation, the perceived equity risk premium, and so forth. Ideally, you want to pay executives for superior performance, but the macro factors can swamp the company-specific factors. In bull markets, that means executives are getting paid handsomely for good luck. In bear markets, it means that even those executives who are skillful fare poorly. Neither outcome serves the core purpose. So the ideal is to figure out how to pay executives for good skill. Indexing options or restricted stock units is a solid first step in achieving this goal.

“Poor quality makes a company uncompetitive but so does quality that is too high. The relationship between quality and value is not all clear,” you write. Why do you say that?

This boils down to what a company’s governing objective should be. What’s essential is that a company cannot optimize multiple factors at the same time. Quality is a good example. If the quality of a company’s goods or services is too low, customers won’t want to do business. On the other hand, if quality is too high, leading to prices that are too high, then customers won’t want to do business. So the trick for managers is to find a balance between price and value that makes the customers happy and creates value for the company.

In the book, I give the example of a company that was determined to win a prestigious quality award and succeeded. The problem was that the expense the company incurred for the award was greater than the price increases it could charge its customers. So while the customers happily enjoyed the higher quality, the company’s finances suffered. It eventually filed for bankruptcy.

That sounds very similar to what happened to Kingfisher Airlines in India. Moving on, can you tell us what is the luck-skill continuum?

Imagine a continuum where on one side results are determined solely through luck, think roulette wheels and lotteries, and on the other side solely by skill, such as chess matches and running races. Most activities in life are between these extremes, and knowing where an activity lies can be very helpful. For example, as you move from the skill to the luck side, you need an increasingly large sample size in order to detect skill because luck dilutes the signal.Another way to think about this is cause and effect. On the skill side of the continuum, outcomes correlate exactly with skill. Think of a great performance by a pianist. Just hearing the music indicates the skill of the musician. Cause and effect are tightly linked, and feedback is clear.

By contrast, outcomes correlate with skill only loosely on the luck side of the continuum. Success or failure comes with an attached probability. For example, even if you play your cards right in a card game, luck ensures that you’ll lose some of the time. This means that feedback is much more difficult since a good process can lead to a bad outcome or a bad process can lead to a good outcome. The way to deal with that is to focus on the process, not the outcome, when luck is involved. A good process provides the best chance for a good result over time.

You point out that one can reduce the influence of luck by effectively tying cause to effect. Could you explain through an example?
There’s an old saying in advertising that half of a company’s advertising budget is wasted, it’s just that no one knows which half it is. The broader point is that no one has been able to effectively measure the effect of advertising spending. That is changing, and that is what I meant when I discussed better understanding cause and effect. Take online advertising as an example. One large retailer tested their advertising using a control group. In other words, they showed advertising to some people and compared the results to a demographically similar group that didn’t see the ads. By comparing results, they could see the effectiveness of the ads. This type of analysis is spreading fast, in large part because technology today allows it. It has also spread to realms including politics. Politicians in the U.S. used to spend money without knowing the payoff, measured in votes. Political scientists now have ways to measure the efficacy of spending much more accurately.
The interview originally appeared on www.firstpost.com on December 1, 2012.

(Vivek Kaul is a writer. He can be reached at [email protected])

28 November, 2012

Luck vs Pluck: The man who could have been Bill Gates

The first line in your book goes “my career was launched by a trash can”. Can you take our readers through that story?
When I was a senior in college, I didn’t really know what I wanted to do with my career, but I knew I needed a job. Drexel Burnham Lambert, an investment bank that was very successful at the time, came on campus to interview and I did well enough to be invited to New York City for a final round. So I put on my best suit and made the trip.
The day of the interviews, we candidates were told that we would have six long interviews and just 10 minutes with the executive who ran the division. My interviews went fine, and then it was my turn to meet with the executive. Upon walking into his office, I noticed that he had a trash can that carried the emblem of the Washington Redskins, a professional American football team. Being a sports fan and having spent my last four years in Washington, D.C., I complimented him on the trash can. That comment hit in an emotional spot, and he launched into a discussion of his time in D.C., the virtues of sports, and the link between athletics and business. I sat and nodded, as 10 minutes stretched to 15.
You got the job?
I got the job, and accepted it. Indeed, my time at Drexel Burnham was extremely formative. After about six months into the program, one of the leaders pulled me aside. “You’re doing fine in the programme,” he started, “but I have to tell you something. The six interviewers voted against hiring you. But the top guy came down and insisted that we bring you in. I don’t know what you said, but it sure worked.” So I like to say that my career was launched by a trash can, and that was pure luck.
What was the broader point that you were trying to make through that example?
The broader point is that luck permeates many aspects of our lives and we’re frequently unaware of its role. So this book is about skill and luck, and includes the definition of each term, tools and methods to quantify the role of each, and what to do about it.
“Most of the successes and failures we see are a combination of skill and luck that can prove maddeningly difficult to tease apart,” you write. Can you explain that in detail?
I open a chapter with the story of an entrepreneur who was born near Seattle who was a brilliant programmer and wrote code that effectively launched the personal computer revolution. He started a company that by 1980 had a dominant market share in the software that ran on the Intel chip. But the company’s fate was sealed in 1981 when IBM came calling and sealed a deal. Now if you know a little about Bill Gates, you can see how that series of facts fits him pretty well. But then I share the end of the story: this tech pioneer walked into a bar in California in 1994 and hit his head bluntly as a result of a fight or a fall—the details were never clear. He died three days later. His name was Gary Kildall, and he has a floppy disk etched on his tombstone. Chances are you’ve never heard of Gary Kildall but you have heard of Bill Gates.
That’s very interesting…
When IBM executives first approached Microsoft about supplying an operating system for company’s new PC, Gates actually referred them to Digital Research (Kildall’s company). There are conflicting accounts of what happened at the meeting, but it’s fairly clear that Kildall didn’t see the significance of the IBM deal in the way that Gates did.
And what happened then?
IBM struck a deal with Gates for a lookalike of Kildall’s product, CP/M-86, that Gates had acquired. Once it was tweaked for the IBM PC, Microsoft renamed it PC-DOS and shipped it. After some wrangling by Kildall, IBM did agree to ship CP/M-86 as an alternative operating system. IBM also set the prices for products. No operating system was included with the IBM PC, and everyone who bought a PC had to purchase an operating system. PC-DOS cost $40. CP/M-86 cost $240. Guess which won. But IBM wasn’t the direct source of Microsoft’s fortune. Gates did cut a deal with IBM. But he also kept the right to licence PC-DOS to other companies. When the market for IBM PC clones took off, Microsoft rocketed away from competition.
So what is the point?
The fact is, Kildall played his cards much differently than Gates did, and hence did well but enjoyed financial success vastly more modest than Gates. But it’s tantalizing to consider the possibility that with a few tweaks, Kildall could have been Gates.Now the book acknowledges that untangling skill and luck can be imperfect, but even some sense of the relative contributions of the two can really help you understand history and, more importantly, make better predictions of the future.

You write that “most people have a general sense that luck evens out over time. That may be true in the grand scheme of things. But the observation doesn’t old for any individual, and the timing of luck can have a large cumulative effect.” What do you mean by that?

In some activities, the outcomes are largely independent: what happened before doesn’t affect what happens next. This is true, of course, in classic games of chance such as dice throwing or roulette wheels, but it also applies to relatively stable systems like sports. You can model the batting average of baseball players, for example, using a simple, independent model. In these cases, luck does tend to even out over time. In other activities, the outcomes are path dependent. What happens next is affected by what happened before. This is relevant in realms that are socially driven such as sales of books, music, and movies. That fact is if you ran the universe over again, it is very unlikely that the same products would be commercial smash hits. We know this through examining the results of some clever sociological experiments.

Can you explain it through an example?

One example I give in the book is the income of college graduates. It turns out that men who graduate during times of relative prosperity earn more than those who graduate during more challenging conditions. That is not so surprising. What is more surprising is that that effect remains in place for 15 years (and perhaps more) following graduation. So two students of identical ability can have substantially different incomes over a long period by dint of when they graduated. So luck is not evening out in these cases. There is a large cumulative and apparently irreversible effect.

Why do we vastly underestimate the role of luck in what we see happening around us?

Especially when social processes are at play, it’s really hard to know how things are going to unfold. In other words, luck is a very large variable. You often hear executives in the entertainment industry lament how hard it is to manufacture hits. And that is true. When social processes are at play, there’s an inherent lack of predictability and generally high inequality.
Any examples?

One story that captures this is that of a young singer named Carly Hennessey. Music executives were looking for the next Brittney Spears, and Hennessey had everything they were looking for—a great voice, charisma, and drive. They spent millions on her first album, which ended up a commercial flop. In the first three months the album sold a grand total of 378 copies, earning less than $5000. There is simply no easy formula to predict success. The flipside is true as well. When Michael Eisner, then CEO of Disney, saw the pilot of the show “Lost,” he gave it a 2 on a scale from 1 to 10, with 10 being the highest. He later called it “terrible.” But “Lost” was a huge success and was very profitable for Disney. When the top executive at Disney has no idea what’s going on, it’s easier to accept that it’s really hard to anticipate hits.

Why are we so bad at distinguishing luck from skill?

There’s a module in your left hemisphere that neuroscientists call “the interpreter.” Its job is to create a narrative that explains cause and effect. For most of mankind’s existence, cause and effect was a pretty straightforward affair: a rustle in the bushes likely signaled danger, for instance. This module has conferred extraordinary advantage, and some scientists argue it is at the core of what distinguishes humans from other species. Now, here’s the fascinating component. Studies of split-brain patients reveal that the interpreter will fabricate a cause whenever it sees an effect, even when the cause makes no sense. For example, researchers would feed information into the right hemisphere—largely absent of language—and ask the subject to explain what is going on. Since the hemispheres in these subjects are severed, there’s no way to communicate. The interpreter simply makes up a story.

So here’s the answer to your question. The interpreter doesn’t know anything about luck. When it sees an effect, it searches for a plausible cause. Once a cause is found, your mind puts the issue to rest. In fact, you start to believe your own story and dismiss any other possibility, a concept psychologists call “creeping determinism.” So once something has happened, we tend to grossly underestimate the role of luck.

Could you give us an example?

I’ll mention quickly a paper by Professor Andrew Lo at MIT. He studied about 20 accounts of the recent financial crisis—half of them by journalists and the other half by academics. He found that there was no common explanation for the crisis, and in fact some of the explanations contradicted one another.

The interview originally appeared on www.firstpost.com on November 28, 2012.
(Vivek Kaul is a writer. He can be reached at [email protected])