Everything is Obvious.

This book is a great piece of combined knowledge and attacks our incessant use of common sense wherever we can. Common sense is a sort of devil in disguise, it is genuinely useful in everyday circumstances and works all the time. In fact, it is so good at conquering everyday problems that we want to use it for everything.

Watts points to a study conducted in 1947 called ‘’The American Soldiers’’. This study was commissioned by the U.S war department and had a massive sample size of 600,000 soldiers. Something that is rarely done today even with the internet. They asked them what life in the army was like. Paul Lazarsfeld who conducted the study displayed results to the public and stood back to see their reactions.

He started by saying that men from rural backgrounds tend to fare better than men from cities and tried to imagine what the responses would be. At first glance the public would see this answer as obvious because people from rural backgrounds are used to that sort of environment, used to physical labour, spending time outside etc… So why would we need this expensive study to tell us what we already know?

Well in reality the results where the complete opposite. It turns out people from cities actually fared better in the army and had an easier time adapting to the environment. However, in hindsight this answer was also obvious, the public response was: of course they are because they are used to structure work environments, wearing uniforms, hierarchical organizations etc…

And this is exactly the problem with common sense. It is usually constructed in hindsight and we tend to find justifications for everything after the fact. ‘’When every conclusion and it’s opposite appear equally obvious (once you know the answer), there is a problem with the concept of obviousness (common sense)’’ – Lazarsfeld

The real problem is that common sense does not lend itself well in complex, multifaceted systems filled with nuance. We use it to try and predict the future in all fields of business and politics. We continue to use because in hindsight everything seems obvious. Now looking back we can easily say that it must have been obvious that these policies would backfire and that we would face problems but at the time no one was talking about it.

There is a lot of money in predicting accurately, if someone was able to create a reliable models for predicting they would likely be the next Amazon or Apple. So why can’t we do it? What is so hard about being able to predict the future?

Well Duncan Watts claims that the best way to go about it is to try and get as many opinions in as possible. We like to bash experts for not being accurate or that their advice is often wrong, but the problem really is in relying on too few of them. Accurate predictions require input from as many different areas as possible. The more complex the field the more information is required.

The other method is about taking a pragmatic and functional approach. This isn’t always possible but in some fields it is. The example given was about a clothing company called Zara. They are one of the top fashion/clothing retailers in the world and have been for a while but how is it that they are able to consistently predict the fast-past change in trends of the fashion market?

They simply don’t. They have accepted and acknowledged the fact that they will never be able to reliably predict what clothes will be in vogue next season and which won’t. So, what they have done instead is created a model of trial and error. They essentially have a huge product development department that pumps out as many different styles of clothes as they can, and they field trial all of them. Meaning that they have created a system of mass production that lends itself well to making lots of different kinds of products. They then analyse the sales figures and determine what products are selling and are able to upscale extremely rapidly. In less than 2 weeks they can take a product that had a handful of samples made per month and start making and selling hundreds of thousands. The models that don’t sell get discontinued and replaced with more trial’s pieces.

This method of measuring and reacting seems to be very common in companies that are successful. Ikea adopted a similar model although maybe not as rapid. They have a similar capability of upscaling production because their supply chain is designed to be flexible enough to adapt without incurring huge costs.

What does this mean for entrepreneurs and start-ups? This type of model can be extremely expensive to build, and proper research can take a huge amount of resources to complete. In the end it is down to luck in a lot of cases, but it also means that small scale failure is perfectly normal. Start-ups need to have the will to continue trying even in the face of failure. Having many different options and offerings open can help in determining what the market wants.

I found this book really useful and although I am tempted to say that a lot of it was self-evident or obvious at times, I have officially lost all ability or will to want to engage myself in any sort of hindsight analysis as an attempt to display my brilliance. Because everything is obvious

If anything, this book has been a humbling experience and I hope that it invokes the same kind of thought in others who read it. It will definitely make me more sceptical about any predictions and always be open to change and the possibility of failure. Of course, I am maybe less afraid of failure in the sense that now I wont attribute the same level of confidence to my own predictions or observations and will try to get as much input as possible.