Latticework of Mental Models: Complex Adaptive Systems

This article is the first of this new weekly series called ‘Latticework of Mental Models’, which will be authored by my friend and partner in writing the Value Investing Almanack, Anshul Khare. Anshul will write on various mental models – big ideas from various disciplines – which can help you think more rationally while analyzing businesses and making your stock investment decisions.

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Let me share with you two very interesting case studies. The first one is set in the time of British India.

The British government was concerned about the number of venomous cobra snakes in Delhi. The government therefore offered a bounty for every dead cobra. Initially this was a successful strategy as large numbers of snakes were killed for the reward. Eventually, however, enterprising people began to breed cobras for the income. When the government became aware of this, the reward program was scrapped, causing the cobra breeders to set the now-worthless snakes free. As a result, the wild cobra population further increased. The apparent solution for the problem made the situation even worse. (Source – Wikipedia)

Now there is no way to validate the authenticity of above story but the second one is very recent and a true incident set again in the city of Delhi. It’s equally amusing.

Among many bridges built over the river Yamuna in Delhi, one is DND Flyway which is a toll bridge. During rush hour (evening and morning) there is a huge pile up right at the start of the toll where vehicles stop to pay toll charges.

To ease this traffic jam, officials came up with a simple strategy. They made the rush hours toll free. So vehicles didn’t have to pay the toll if they crossed the bridge between 8 to 10 am and 5 to 7 pm. As a result, drivers who were about to cross the bridge even 30 minutes before the free timings started waiting right before the toll until 5 pm evening (or 8 am in the morning). This made the problem worse and increased the traffic.

I couldn’t help but admire the ingenuity of the cobra breeders and the intelligent cost conscious drivers. But think about it for a moment. In spite of good intentions, did the government and the toll-road officials eliminate bad consequences? No.

Outcomes Don’t Always Follow Intentions
Samuel Johnson said, “The road to hell is paved with good intentions.”

By solving one problem, we generate another one and sometimes create an even worse one. The common pattern here is that somebody tried to modify the behaviour of a system by tinkering with it in a small way. But that small action produced significant unintended consequences.

In fact, every action has consequences, intended and unintended. No matter how carefully we plan, we can’t anticipate everything. This brings forth a very important learning about how the world around us works and responds to our actions.

Complex Adaptive Systems
Look around and you’d realize how you are surrounded by complex systems. Your car, TV, smart phone, and the elevator (that you use to avoid stairs) are all complex systems. They involve multiple independent moving parts that interact with each other to produce desired behaviour.

Any system composed of independent components working with each other can be termed as complex system.

Is your body a complex system? Actually it’s more than that. When you exercise, your body builds new muscles and stronger bones. It adapts. That makes our bodies complex adaptive systems. For that matter, every living organism is a complex adaptive system.

Traffic is also a complex adaptive system. When you see traffic in your lane, you change lane or probably take an alternate route to your destination. It is a complex system in which its inhabitants adapt to each others’ actions.

The key element of a complex adaptive system is the social element. The belts and pulleys inside a car do not communicate with one another and adapt their behavior to the behavior of the other parts in an infinite loop. Drivers, on the other hand, do exactly that.

Consider the human society. When you buy a new iPad that is manufactured in China, with materials derived from African soils, and with software developed by programmers from India, you need to realize that those actions are made by autonomous organizations, firms and individuals. These many individual actions are guided by rules and agreements we have developed, but there is no ruler who can control these interactions. These are thus complex adaptive systems.

Systems Thinking
When you are dealing with complex adaptive systems, you have to look at it as a whole system in which actions and reactions are taking place. The system is more than just the sum of its parts.

Charlie Munger understood the risks of unintended consequences while dealing with social systems. When asked about corporate responsibility for social problems, he answered –

I’m all for fixing social problems. I’m all for being generous to the less fortunate. And I’m all for doing things where, based on slight preponderance of the evidence, you guess that it’s likely to do more good than harm…

What I’m against is being very confident and feeling that you know, for sure, that your particular intervention will do more good than harm given that you’re dealing with highly complex systems wherein everything is interacting with everything else.

I assume that if you are looking for ideas that can make you a better investor, then you might be on the verge of losing interest in this post. Please allow me hold your attention for few more seconds by telling you this – you acquire tremendous edge in understanding the behaviour of stock market if you have the mental model of complex adaptive systems in your toolbox.

Stock Market – A Complex Adaptive System
Complex adaptive systems is a useful way to think about the stock market, since the market can be characterised as a weighing machine built on many investors’ individual views and transactions (and their behavioural biases). This weighing machine is continually adapting to new information under conditions of uncertainty and complexity.

Investors are component parts of the system capable of responding to positive and negative feedback from the system. From these interactions (or transactions in stock market terms), patterns emerge which inform the behaviour of agents in the system and, ultimately, the behaviour of the system itself. However, just like studying one worker bee in a hive, analysis of individual investors yields limited insight, since market direction emerges at a higher, aggregate level.

In fact, the efficient market theory (EMT) has its roots in this idea. It assumes that the ability of stock market to instantaneously adapt to any new information or fact is flawless. However EMT fails to take into account that complex adaptive systems don’t always go in the direction of correction. From time to time, they can (and do) become highly unstable by virtue of their ability to respond to any stimuli.

Forecasting, because of these unpredictable dynamics of markets, especially in short term is next to impossible. Fidelity’s former manager Peter Lynch said in One Up on Wall Street:

There are 60,000 economists in the U.S., many of them employed full-time trying to forecast recessions and interest rates, and if they could do it successfully twice in a row, they’d be millionaires by now…As far as I know, most of them are still gainfully employed, which ought to tell us something.

Charlie Munger says –

Economics involves too complex a system…economics should emulate physics’s basic ethos, but its search for precision in physics-like formulas is almost always wrong.

You can’t look at a single investor’s action or one industry performance or an isolated econometric parameter to predict the behaviour of stock market. Howard Marks explains in his must-read book The Most Important Thing –

…investing can’t be reduced to an algorithm and turned over to a computer. Even the best investors don’t get it right every time…no rule always works. The environment isn’t controllable, and circumstances rarely repeat exactly.

There are always people who claim to know the cause-effect relationship in market cycles but those theories are mostly built with the advantage of hindsight.

Don’t believe the financial experts and stock market gurus who say they can forecast unforeseeable variables. Nobody can forecast the stock market, interest rates or currency rates, GDP, market cycles, etc. The more widely publicised forecast, the less reliable it is.

Michael Mauboussin has written a detailed paper on the similarity of stock markets and complex adaptive systems. It’s a must read. Mr. Mauboussin champions a view that the stock market is a complex adaptive system, which, like an ant colony or flock of birds, is a highly organized system with no leader.

Business Analysis and Complex Adaptive Systems
When it comes to understanding an industry or a business, it’s imperative that you look at it from the lens of complex adaptive systems mental model.

Consider this common mistake that many naive business managers are guilty of making. They see sales volumes dropping and in an attempt to correct that, they reduce prices. They reason that what they lose on price will be made up by increased volume and the market share will increase.

However they fail to anticipate all the consequences of cutting prices. What if the increased volume is not supported by current capacity? What if the competitors also cut prices? What if the price cut reduces the brand value perception for customers?

You can’t keep thinking linearly in one routine way and hope to beat the index returns. You need to learn to think about second order effects. A brilliant insight from Howard Marks’ on this –

First-level thinking says, “The outlook calls for low growth and rising inflation. Let’s dump our stocks.” Second-level thinking says, “The outlook stinks, but everyone else is selling in panic. Buy!

First-level thinking says, “I think the company’s earnings will fall; sell.” Second-level thinking says, “I think the company’s earnings will fall less than people expect, and the pleasant surprise will lift the stock; buy.”

First-level thinking is simplistic and superficial…Second-level thinking is deep, complex and convoluted. The second-level thinker takes a great many things into account.

Second-level thinking is a very effective skill to deal with the uncertainties of complex adaptive systems like stock market. When you are about to buy a stock, ask yourself following questions –

1. Why is the seller selling it?
2. How would I reason if I think it through from the viewpoint of the other person?
3. Why would I make a better decision than someone who has all the information?
4. What is the probability that I am right?

It’s easy to forget this in the excitement of a new opportunity. These questions can save you from the unintended consequences of your decision.

Porter’s five force analysis model is another great tool to apply systems thinking into business analysis. It zooms out your vision to look at the industry and takes your focus away from overused and incomplete parameters like P/E ratios.

I hope now you can appreciate the role of complex adaptive systems in providing a useful perspective to demystify the stock market behaviour. And not just the stock market but it gives us an edge in business analysis too.

Becoming a sensible investor is just a small piece of a bigger puzzle. The puzzle called acquisition of worldly wisdom. By sharing my learning with you, today both of us have taken another step forward in this quest to acquire worldly wisdom.

Let’s keep moving and hope that every new learning makes us a better thinker.