Complexity Bias: Why We Prefer Complicated to Simple

Originally circulated on 29 November 2019

Hi fellow Ruminants

This week I have received quite a few ideas from friends and colleagues which are all interesting and I have selected one. Thanks for the inspiration. Today’s topic comes from a very interesting blog site entitled Farnam Street (fs). The topic is complexity bias https://fs.blog/2018/01/complexity-bias/. Complexity bias is a logical fallacy that leads us to give undue credence to complex concepts.

‘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 favour complex ones.’

“Most geniuses—especially those who lead others—prosper not by deconstructing intricate complexities but by exploiting unrecognized simplicities.” — Andy Benoit. This brings us to complex models very much beloved by many organisations. The following quote from George Box the famous British modelling guru and statistician is relevant:

“Since all models are wrong the scientist cannot obtain a “correct” one by excessive elaboration. On the contrary following William of Occam he should seek an economical description of natural phenomena. Just as the ability to devise simple but evocative models is the signature of the great scientist so over-elaboration and over-parameterization is often the mark of mediocrity”.

However we will keep building elaborate forecasting and economic models. The back of the envelope calculation is neither impressive nor convincing. The very thick report prepared by highly paid experts (preferably by Bain, McKinsey or BCG) based on an extremely complex model which nobody really understands is much more convincing.

Please keep the submission ideas flowing.

Regards

Bruce