A Bias Toward Complexity

When making predictions or decisions in the real world where there are many variables, high levels of uncertainty, and numerous alternative options to chose from, using a simple rule of thumb can be better than developing complex models for predictions. The intuitive sense is that the more complex our model the more accurately it will reflect the real complexity of the world, and the better job it will do with making a prediction. If we can see that there are multiple variables, then shouldn’t our model capture the different alternatives for each of those variables? Wouldn’t a simple rule of thumb necessarily flatten many of the alternatives for those variables, failing to take into consideration the different possibilities that exist? Shouldn’t a more complex model be better than a simple heuristic?

 

The answer to these questions is no. We are biased toward complexity for numerous reasons. It feels important to build a model that tries to account for every possible alternative for each variable, we believe that always having more information is always good, and we want to impress people by showing how thoughtful and considerate we are. Creating a model that accounts for all the different possibilities out there fits those preexisting biases. The problem, however, is that as we make our model more complex it becomes more unstable.

 

In Risk Savvy, Gerd Gigerenzer explains what happens with variance and our models by writing, “Unlike 1/N, complex methods use past observations to predict the future. These predictions will depend on the specific sample of observations it uses and may therefore be unstable. This instability (the variability of these predictions around their mean) is called variance. Thus, the more complex the method, the more factors need to be estimated, and the higher the amount of error due to variance.”  (Emphasis added by me – 1/N is an example of a simple heuristic that Gigerenzer explains in the book.)

 

Our bias toward complexity can make our models and predictions worse when high levels of uncertainty with many alternatives and relatively limited amounts of data exist. If we find ourselves in the opposite situation, where there is low uncertainty, few alternatives, and a plethora of data, then we can use very complex models to make accurate predictions. But when we are in the real world, like making stock market or March Madness predictions, then we should rely on a simple rule of thumb. The more complex our model, the more opportunities for us to misestimate a given variable. Rather than having one error be offset by numerous other point estimates within our model to reduce the cost of a miscalculation, our model ends up creating more variance and a greater likelihood that our prediction will be further from reality than if we had flattened the variables with a simple heuristic.

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