A textbook definition of lollapalooza is: “A person or thing that is particularly impressive or attractive.” When Charlie Munger uses the word “lollapalooza” he often attaches the word “effects” (as in “lollapalooza effects”) which means that multiple factors are acting together in ways that are feeding back on each other. The lollapalooza effects phenomenon is typified by feedback creating a complex adaptive system, that can be either positive or negative in terms of output or outcome.
Munger is clearly fascinated by the lollapalooza effects phenomenon:
“I’ve been searching for lollapalooza results all my life, so I’m very interested in models that explain their occurrence. Often results are not linear. You get a little bit more mass, and you get a lollapalooza result. Adding success factors so that a bigger combination drives success, often in non-linear fashion, as one is reminded by the concept of breakpoint and the concept of critical mass in physics.”
“Really big effects, lollapalooza effects, will often come only from large combinations of factors. For instance, tuberculosis was tamed, at least for a long time, only by routine, combined use in each case of three different drugs. Other lollapalooza effects, like the flight of an airplane, follow a similar pattern.”
What intrigues Munger so much is the often unexpected and spectacular output of a complex adaptive system, which Michael Mauboussin describes as follows:
“A complex adaptive system has three characteristics. The first is that the system consists of a number of heterogeneous agents, and each of those agents makes decisions about how to behave. The most important dimension here is that those decisions will evolve over time. The second characteristic is that the agents interact with one another. That interaction leads to the third—something that scientists call emergence: In a very real way, the whole becomes greater than the sum of the parts. The key issue is that you can’t really understand the whole system by simply looking at its individual parts. “You can’t make predictions in any but the broadest and vaguest terms.” Complex adaptive systems effectively obscure cause and effect” “Complexity doesn’t lend itself to tidy mathematics in the way that some traditional, linear financial models do.” “Increasingly, professionals are forced to confront decisions related to complex systems, which are by their very nature nonlinear… ”
To better understand complex adaptive systems, Munger has recommended that people read a book entitled Deep Simplicity: Bringing Order to Chaos and Complexity by John Gribbon.
Some people unfortunately confuse what is genuinely “complex” with what is “complicated.” Wendell Jones explains the difference:
“Complicated linear and determined systems produce controllable and predictable outcomes. Complex adaptive systems can produce novel, creative, and emergent outcomes. In complicated systems, the elements and their connections are equally important. In a 747 the yolk and the engine and the flaps and the connections between them are all critical for the proper operation of the airplane. Secondly, simple algorithms (rules) produce simple and predictable responses. Every time the pilot pulls the yoke back, the airplane climbs. The response of the component and of the whole system is fully determined. In complex systems, the connections are critical, but individual agents are not. So the connections between the birds are critical, but if one bird gets injured and falls behind, it does not affect the rest of the flock. Simple rules result in complex and adaptive responses — they are not predictable. Each of the agents has a choice of responses within the confines of the rules. So their individual behavior is not determined exactly, as it is in complicated determined systems…. the marching band is a human system that behaves very much like a linear determined system… with the jazz ensemble there is no hierarchical direction and no mechanical loyalty to a set of prescribed actions. Instead, members agree to subscribe to only general rules and are free to improvise widely. Similar to the flock of birds, the general characteristics of the music can be anticipated, but each rendering will be different.”
In no small part because he believes in complex adaptive systems, Munger has concluded that biology presents a better metaphor for business and an economy than a machine or physics: “I find it quite useful to think of a free market economy – or partly free market economy – as sort of the equivalent of an ecosystem….” “Common stock investors can make money by predicting the outcomes of practice evolution. You can’t derive this by fundamental analysis — you must think biologically.” For example, changing interest rates does not alter the economy like pulling on the control yolk of an airplane. The economy is genuinely complex and not just complicated. There are second and higher order effects of everything in an economy. Everything impacts everything. An economy is more a living creature than it is a machine.
The global economy is, of course, getting more interconnected at a exponential rate which increases the impact of feedback, which means we are increasingly living in what Nassim Taleb calls Extremistan. Anything social has particularly strong Extremistan effects. The proliferation of connected sensors powered by machine learning based artificial intelligence will be an increasing accelerator of Extremistan. There will be more Black Swans and seemingly random events. The need for a margin of safety to protect against negative Black Swans is now much greater. The ability to profit from positive Black Swans and the advisability of riding any profitable wave has never been greater.
All businesses, economies, families, ecosystems, immune systems and most importantly the brain are genuinely complex. A change in one part of a complex adaptive system can, through the many connections that exist, influence all other related parts, but not in any uniform or predictable way. Complex adaptive systems are very dependent on initial conditions. Changes in the inputs or rules are not correlated in a linear manner with outcomes.
Munger’s views on complexity help explain his strong desire to “be smart by not being stupid” instead of trying to make short term predictions about the future. Anyone who says they understand complex adaptive systems and is not humble about their ability to make short-term predictions about the behavior of large groups of humans does not really understand complexity. Success in investing is found when you avoid situations where the odds are not substantially in your favor and complexity means that not only is the probability of future states often not known but sometimes even the potential future states themselves are not known. Munger says that people should “bet big when they have the odds. And the rest of the time, they don’t. It’s just that simple.” If you do not know the odds, by definition you cannot bet the odds. You can bet based on optionality but that is venture investing, which Munger does not consider to be within his circle of competence.
So what should an investor do?
“The game of investing is one of making better predictions about the future than other people. How are you going to do that? One way is to limit your tries to areas of competence. If you try to predict the future of everything, you attempt too much.”
“We have the same problem as everyone else: It’s very hard to predict the future.”
I get this question from some people: “Well Buffett must predict something in order to invest.” The answer is composed of two parts plus some differences in taxonomy. First, circle of competence. Munger says:
“You have to figure out where you’ve got an edge. And you’ve got to play within your own circle of competence. If you want to be the best tennis player in the world, you may start out trying and soon find out that it’s hopeless—that other people blow right by you. However, if you want to become the best plumbing contractor in Bemidji, that is probably doable by two-thirds of you.”
Second, what is it that you predict? Munger again:
“Berkshire is in the business of making easy predictions. If a deal looks too hard, the partners simply shelve it.” “We’re the tortoise that has outrun the hare because it chose the easy predictions.”
What’s an easy prediction? For example, that over long periods of time the economy will get better and the economy will grow or certain phenomenon will return to the mean is an easy prediction. But is that a prediction or an assumption?
Munger uses the word prediction in ways that Buffett may not agree with. Buffett seems to argue that some of what Munger might call predictions are not predictions at all but rather assumptions. Is a prediction that never changes better described as an assumption? Buffett and Graham certainly make assumptions. Buffett says: “I have no idea what the stock market’s going to do tomorrow or next week or next month or next year.” Buffett’s major influence, of course, is Ben Graham: “The last time I made any market predictions was in the year 1914, when my firm judged me qualified to write their daily market letter based on the fact that I had one month’s experience. Since then I have given up making predictions.” In The Intelligent Investor, Benjamin Graham wrote: “The function of the margin of safety is, in essence, that of rendering unnecessary an accurate estimate of the future.”
Predictions that require being accurate about the behavior of humans in the short term are particularly shunned. James Montier explains: “We need to stop pretending that we can divine the future, and instead concentrate on understanding the present, and preparing for the unknown. There is a simple, although not easy alternative [to forecasting]… Buy when an asset is cheap, and sell when an asset gets expensive…. Valuation is the primary determinant of long-term returns, and the closest thing we have to a law of gravity in finance.” A law of gravity sounds like an assumption of value investing and not a prediction. As long as the investors agree on what to do, the definitions they use can vary.
In short, whether something is an assumption or a prediction is taxonomy and subject to arguments and differences. Even Buffett and Munger seem to use slightly different definitions. What they do agree on, and what Berkshire avoids like the plague, is what they call forecasts. Munger makes that point here:
“Gigantic macroeconomic predictions are something I’ve never made any money on, and neither has Warren. ”
“People have always had this craving to have someone tell them the future. Long ago, kings would hire people to read sheep guts. There’s always been a market for people who pretend to know the future. Listening to today’s forecasters is just as crazy as when the king hired the guy to look at the sheep guts. It happens over and over and over.”
That the business environment is a nest of complex adaptive systems explains why business will always be an art rather than a science. Adding Greek letters to academic formulas does not transform business into a science. Running a business is a bit like flying an older mostly analog airplane in turbulence. Ben Horowitz writes in his book The Hard Thing About Hard Things:
“Management turns out to be really dynamic and situational and personal and emotional. So it’s pretty hard to write a formula or instruction book on it.” “There isn’t one lesson that solves everything.” “Any advice you give is based on your [experience]; it’s not general advice. People try to generalize it — and I try to generalize it, too — but without knowing where it comes from it’s not nearly as useful.”“Nobody is born knowing how to be a CEO. It’s a learned skill and unfortunately you learn it on the job.” “The only thing that prepares you to run a company, is running a company.”
Munger is often suspicious of people who claim that something is a solution to a problem since
“A special version of this ‘man with a hammer syndrome’ is terrible, not only in economics but practically everywhere else, including business. It’s really terrible in business. You’ve got a complex system and it spews out a lot of wonderful numbers that enable you to measure some factors. But there are other factors that are terribly important, [yet] there’s no precise numbering you can put to these factors. You know they’re important, but you don’t have the numbers. Well practically everybody (1) overweighs the stuff that can be numbered, because it yields to the statistical techniques they’re taught in academia, and (2) doesn’t mix in the hard-to-measure stuff that may be more important.”
A classic example of a lollapalooza for Munger would be would be:
“An investment decision in the common stock of a company frequently involves a whole lot of factors interacting … the one thing that causes the most trouble is when you combine a bunch of these together, you get this lollapalooza effect.”
Because many things are feeding back on each other in a complex system precise predictions are nearly impossible to make accurately, especially in the short term. Munger says:
“consequences have consequences, and the consequences of the consequences have consequences, and so on. It gets very complicated. When I was a meteorologist I found this stuff very irritating. And economics makes meteorology look like a tea party.” “If you try and talk like this to an economics professor, and I’ve done this three times, they shrink in horror and offense because they don’t like this kind of talk.It really gums up this nice discipline of theirs, which is so much simpler when you ignore second and third order consequences.”
An example of a lollapalooza effect with a negative outcome would be the 2007 financial crisis, which Munger has said “was a lollapalooza event – a confluence of causes that is how complex systems work.” What were the factors interacting in that case according to Munger? All of the following were involved says Munger:
“1) Abusive practices in consumer credit. People who couldn’t handle credit were deliberately seduced. People who did it justified it by saying competitors would do it if they didn’t. That is not proper. Sometimes you should let others proceed and not copy them. It is abusive folly. I talked to a plastic surgeon last night who used to let people write checks against a line of credit on their house. Now his clients are finding those credit lines harder to get. A multiple credit card borrower is dangerous. He can look great right up until he goes bankrupt. Banks have abused their prerogatives and have stuck it in too hard. I have a fundamental theory that in some way the world is just, and if you do something immoral or stupid there will likely be a whirlwind someday where you get clobbered.
2) Mortgage brokers – often these are scum of the earth rejoicing in “rooking” the borrowers with flim-flam tricks, which often can happen with minorities in poor neighborhoods. On first and second mortgages – they built a huge balloon bound to create horrible mess, and the mess finally happened.
3) Wall Street went crazy. Any way of earning money short of armed robbery was ok. The last mortgage broker Merrill Lynch bought were a bunch of sleazy crooks even on the face of it. When people behave like that you get a tremendous mess.
4) Regulatory apparatus that allowed all this was also foolish. The regulators and legislators were in two categories. Legislators wanted poor people to have houses, but this is a bad idea since you want credit practices to be sound just like you want your engineering practices to be sound. People making money just rationalized what they did. Accounting systems spit it out as okay, even though in substance it wasn’t right. It was ghastly and there was huge envy in the thing. If Joe made $3m, I’m better than Joe and so I deserve $3.5m.
5) Credit system was the repo system, one of best ways to grant unlimited credit ever invented. Then banks offered access to the repo system to hedge funds. It went to enormous excess. Some of it was due to democratic legislators hoping to help the poor, and some also was due to Republicans who overdosed on Ayn Rand. For Republicans, it was like legalizing armed robbery for anyone under 25. It was like letting the financial class prey on the poor. If it was unreasonable for the buyer, you got 9% for selling it. Ethos was of the “buyer beware”. The vendors in America should care about selling good stuff to the customer.
6) Then the other issue was in terms of dizzy leverage on stock indices and CDS – where anyone could bet someone would go broke, even if they had no economic interest in the outcome. Then you could help that person along to ruin. We prohibited this in life insurance. I can’t buy insurance if I don’t have economic interest in the person (spouse, etc). These wise rules were thrown out in CDS markets. Then the people who did the accounting used mark to model. Both sides would allow profits. Anyone with engineering cast of mind will feel like throwing up into the aisle. Well go ahead, it will be a memorable moment if you do [laughter].
7) Accounting was phony because all the customers wanted it phony.”
Among the most positive examples of a lollapalooza effect in operation are Buffett and Berkshire:
“A confluence of factors in the same direction caused Warren’s success. It’s very unlikely that a lollapalooza effect can come from anything else. “If that success in investment isn’t the best in the history of the investment world, it’s certainly in the top five. It’s a lollapalooza.”
“Buffett’s decision to limit his activities to a few kinds and to maximize his attention to them, and to keep doing so for 50 years, was a lollapalooza. Buffett succeeded for the same reason Roger Federer became good at tennis.”
At different times Munger has referred to a Berkshire itself, Tupperware party, open outcry auctions, Alcoholics Anonymous, the Coke brands, and cults as lollapaloozas. Munger is saying that looking for them as you go through life is fun and potentially profitable.
The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers http://www.amazon.com/The-Hard-Thing-About-Things/dp/0062273205
Munger on Academic Economics http://www.tilsonfunds.com/MungerUCSBspeech.pdf
Michael Mauboussin: https://hbr.org/2011/09/embracing-complexity/ar/1