Samuel Johnson said, “The road to hell is paved with good intentions.”
Sometimes, by trying to solve one problem, we generate another one and end up creating an even bigger and problem. The pattern that emerges from such situations is that somebody tries to modify the behaviour of a system by tinkering with it in a small way but that small action produces significant unintended consequences.
In fact, every action has consequences, intended and unintended. No matter how carefully we plan, we can’t anticipate everything.
So multidisciplinary way to address this problem is to find a big idea from an unrelated subject and see if it can help us think about it. And that big idea is Complex Adaptive Systems.
The Multidisciplinary Model
Any system composed of independent components working with each other can be termed as complex system.
So 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 behaviour to the behaviour of the other parts in an infinite loop. Drivers, on the other hand, do exactly that.
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.
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.
Forecasting, because of these unpredictable dynamics of markets, especially in short term is next to impossible. 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.
As an investor, you have to learn to think about the unintended consequences too. When you are about to buy a stock, ask yourself following questions –
Why is the seller selling it?
How would I reason if I think it through from the viewpoint of the other person?
Why would I make a better decision than someone who has all the information?
What is the probability that I am right?
To deal with problems in business, the key insight from complex adaptive systems is that one needs to learn the second level thinking.
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.
That’s an example of first level thinking. But the second level thinker understands that in a complex adaptive system the consequences of an action don’t follow a straight path. The second-level thinker doesn’t stop at the obvious. He takes a great many things into account, like -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?