Modeling Complexity

“If a tree falls in a forest and no one is around to hear it, does it make a sound?… This riddles raises some interesting questions about observation and the knowledge and understanding of reality.   Philosopher George Berkeley postulated that ” …the objects of sense exist only when they are perceived

Likewise, we may ask: “If we do not reveal the complexity of a system, is it really complex? ” This conundrum suggests that we need to perceive and understand a system’s behavior before we can appreciate its complexity.  Understanding a system’s complexity has been a focus of the field of mathematics since the mid-thirties when British mathematician Alan Turing came up with the so-called Turing machine as a way to describe the behavior of algorithms in a computer.

Photo by Spike 55151 on Flickr

Our models of reality always fall short of describing accurately the behavior of complex systems.  We are hoping that we can capture and explain the most significant behavior of the system so that we can predict the system’s behavior with relevant accuracy under changing conditions.  Different people or organizations with differing vantage points may create different models of the complex system, with different rules, yielding differing results.  Planetary warming is a good example of a complex system where different modelers arrive at different results.

As our knowledge, mathematical sophistication and ability to develop detailed representations grow, our models of complex systems become more accurate and thus more closely describe the behavior of their real counterparts.   “Around 650 B.C.”, for instance, “the Babylonians tried to predict short-term weather changes based on the appearance of clouds and optical phenomena such as haloes.” (NASA)   “With the formation of regional and global meteorological observation networks in the nineteenth and twentieth centuries, more data were becoming available for observation-based weather forecasting.”  Today, weather observations are collected and used by large computer programs using physics and fluid dynamics to simulate temperature, moisture and wind, and accurately predict weather changes.  In 650 BC, the Babylonians used their understanding of the relationships between cloud appearance and future weather to predict the future.   To them, weather variety could not be understood and modeled accurately, and changes in weather could not be predicted effectively.  Today, we understand better the complexity of weather dynamics (changes in temperatures, wind and humidity)  because of our ability to represent this natural phenomenon more accurately.

Now it is time for lunch…

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1 Response to Modeling Complexity

  1. DIY Investor says:

    Good luck on your blog …I look forward to following it. I think worth noting at the beginning is that the financial markets are a different type of complex system from others. I think some people think it’s like heart surgery…you study it, model it, get reams of data and you can do better than others. That’s not the case… a monkey might pick a portfolio at random and beat all of the experts. That, of course, doesn’t happen with heart surgery or building a Boeing 747 for example. That’s the essence of market efficiency.


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