Complexity Overview
Complexity Science is an emerging form of science that deals with the study of complex systems. Complex systems do not have a formal definition, but they are generally described as having many parts that interact with each other in such a way that each part is dependent on the behavior of others. Metaphorically, it would look something like this:
However, in large systems, it might be thousands or millions of little gears, and each gear has a relationship with every other gear- what each gear represents depends on what system you are trying to model.
Here are a few more characteristics of complex systems:
- Feedback Loops
- Strongly Interdependent Variables
- Chaotic Behavior
- Mulitple Stable States
- Non-Gaussian Distribution of Events
(Don’t worry if you don’t know what any of these mean, we’ll cover these characteristics in depth in later posts.)
The resulting action or activity of such a system is nonlinear, meaning that the final behavior is not the result of the sum of the components. In other words, the outcome is often unpredictable. The description/definition above may be confusing to understand, but a few examples should hopefully clear things up.
IT networks, ecosystems, brains, and markets are all examples of complex systems. Let’s take the brain, for example. The brain is made up of water, fat, neurons, neurotransmitters, hormones, etc… If we just take the sum of the components, we would simply just get a mix of brain parts and fluids. However, the way the components interact gives rise to a complex system. This complex system is able to interpret sounds, absorb information, make decision, react to visual cues, etc… In addition, we have no idea how the brain will react if given a certain stimuli, since the reaction is dependent on past memories, personal biases, etc… All of these factors are impossible to know from just looking at the basic components: Can you tell what your friend is thinking by just looking at their brain?
The financial markets provide another example of a complex system. The prices of the securities traded in a constatnt state of flux and any attempt to predict the prices consistently nearly always fails. However, the prices do tend to move in accordance with certain factors or news. For example, oil price may fall on news that there has been an unexpected inventory buildup or that stock prices rise when a central bank decides to cut key interest rates. To top it off, with the rise of globalization, financial markets have become interconnected with other markets- how the American market performs will undoubtedly have an effect on markets in Japan, Europe, etc… And vice versa. As shown by the LTCM meltdown and the 2007-08 financial crisis, such an intertwined and complex system has led to the near collapse of the entire financial/banking structure.
In any case, in order to understand how complex systems work, it is necessary to know what the individual componenets are and how they might interact with each other. The financial markets provide the perfect training ground to dive into complexity, as there are limitless components that factor into the markets. This blog will attempt to cover the basic aspects of complexity, but will not be confined to the study of the markets. Personally, complexity science is an attempt to grasp the whole picture and to uncover as much information as possible. It is a new way of looking at life.
As for the financial markets, complexity science is by no means an easy path to riches. It is impossible to analyze every part and definitely impossible to uncover how each part may influence one another. Yet, as an investor, every decision we make is shrouded by uncertainty and imperfect knowledge (a concept trumpeted by billionaire investor George Soros). If it were possible to predict the markets’ every move, there would be no incentive to play. Where’s the thrill in knowing everything? However, knowing more about the intricate workings of the financial markets will undoubtedly help any investor make a more informed decision and hopefully increase the chances of making the right choice.
Thanks for reading!
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You might also like my discourses on value investing…
- Mr. Market and Stoicism (I)
- Why Value Investing Works (II)
- Intelligently Searching for Value Investing Opportunities (III)
- Crisis Investing (IV)
- A Mean-Reverting Walk Down Wall Street (V)
Or some of my other musings…
