Faced with two competing hypotheses, we are likely to choose the most complex one. That’s usually the option with the most assumptions and regressions. As a result, when we need to solve a problem, we may ignore simple solutions — thinking “that will never work” — and instead favor complex ones.
-Farnamstreetblog.com Complexity Bias: Why We Prefer Complicated to Simple
Most of the behavior biases that have been used to explain investment decision frictions have focused on the idea that mistakes are made because investors engage in fast thinking. Rules of thumb are employed in order to make decision shortcuts, but simplified decisions lead to mistakes.
Yet, there is such a thing as complexity bias. With complexity bias, if someone can choose between the simplest answer and the most complex, there may be a bias for something that is more complex. Many have a fascination with stories that seem to have a high-level of complexity. We seem to prefer explanations that are more complex. We often associate complex language with higher level thinking. The idea that we often try to find patterns in random events could be attributed to the idea that we like complexity.
I have reviewed the literature on this bias and am not persuaded on whether this will supplant the classic rules of thumb bias thinking, but I can see how this bias can have an impact on behavior. The richness of our human biases is truly fascinating.
-Farnamstreetblog.com Complexity Bias: Why We Prefer Complicated to Simple
Most of the behavior biases that have been used to explain investment decision frictions have focused on the idea that mistakes are made because investors engage in fast thinking. Rules of thumb are employed in order to make decision shortcuts, but simplified decisions lead to mistakes.
Yet, there is such a thing as complexity bias. With complexity bias, if someone can choose between the simplest answer and the most complex, there may be a bias for something that is more complex. Many have a fascination with stories that seem to have a high-level of complexity. We seem to prefer explanations that are more complex. We often associate complex language with higher level thinking. The idea that we often try to find patterns in random events could be attributed to the idea that we like complexity.
I have reviewed the literature on this bias and am not persuaded on whether this will supplant the classic rules of thumb bias thinking, but I can see how this bias can have an impact on behavior. The richness of our human biases is truly fascinating.
I will, however, use my experience in the trend-following space to conclude that the complexity bias is alive. There seems to be a bias toward complexity in model building. Investors can have a choice between a manager that has a simple model and one that uses "state-of-the-art" statistics and requires a Ph.D. and many investors will go for the complex. A manager comes into an office and talks about his use of complex tools for extract unique and special signals and many will be jumping across the table to invest. Another manager in the extreme says, "I don't try to predict, I follow trends and buy what is going up and sell what is falling" and it is a short meeting as you usher him to the elevator. Investor need to focus on why complexity is needed and if a manager cannot explain the need, lean toward the simple. If two managers have similar returns, choose the simple strategy.
Using better techniques to find improved signals is a worthy goal. Difficult problems may require better tools. Nevertheless, there should be a premium on the combination of results and simplicity; no more rules or techniques than what is necessary. The Occam's Razor of model-building should apply; just the right amount of rules to do what is required, just the most direct technique to find a solution.
Using better techniques to find improved signals is a worthy goal. Difficult problems may require better tools. Nevertheless, there should be a premium on the combination of results and simplicity; no more rules or techniques than what is necessary. The Occam's Razor of model-building should apply; just the right amount of rules to do what is required, just the most direct technique to find a solution.
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