We don't really understand randomness

Our brains have evolved to draw simple linear correlations between events. It helps simplify our interpretation of the world, but we must understand the limitations of that model.

Our brains hate randomness. Evolution has caused our brains to focus on simple, linear narratives to explain causation. We use intuition and mental models to reduce the complexity of our surroundings for better understanding. 

Yet, so much of our world is driven by pure chance. Drawing linear narratives greatly simplifies interpretation, but we must understand the limits of those models. If you don’t accommodate for randomness, you risk being a sucker. 

For example, you may spot the trend of a loose pattern and become overconfident in what the future will hold. Be wary of patterns that could be random, or simply unique but nonrepeating. Don’t spot the 1990s dot-com bubble pattern repeating, for example- there were other factors in play, and it was just a random event.       

Barking up the wrong tree.

Our brains are insulated from the true randomness of the events that happen in our lives. What do our brains use to determine causation? A regular sequence or a randomly spaced sequence of events. For example, if we take a long sequence of coin tosses and observe a run, we naturally fall into the trap of thinking the one event that is not in the run is causation — that is, we don’t account for randomness in our thinking as much as we should.

Our brains especially do not like randomness that goes against a regular pattern.

If you want an example of random behavior, data mining won’t help you. In randomness, there is no discernable pattern. You may find no correlation between any inputs, and this random sequence will feel wasteful or without purpose.

This becomes an obstacle when we go to look at the world and predict future behavior or come up with causal relationship ideas. Even more so when we look back on the past. We assume that the past is the “causal story”, but it never is. We construct it as such.    

There is an inevitable bias, which one of avoidance, as we have a lot of computation resources in seeking causal stories and thus avoid shark infested waters, quite like the animal kingdom going to a watering hole.

How do we break out of this feeling of certainty and discover the truth? When looking for cause, try to disprove rather than prove the correlation. Ask what could go against the pattern of the event you seek — where you have randomness.

Next, ask what else could be going on. It could be the X factor. Look for luxury variables are you are looking for a correlation.

You want a situation in which the uncertain variable is real and not happenstance. It’s dangerous to seduce the idea that there is a causation relationship where there is none.

People fall into the trap of false pattern recognition all the time. This can be harmless, such as spotting a mythical dragon in a collection of common shapes. Their are also cases where false pattern recognition can be dangerous.    

For example, Sam L. Savage was convinced he could use statistics to accurately predict fruit prices, which was undoubtedly unwise. He hired a commodities trading company, but lost $300 million in a matter of weeks.

He incorrectly labeled the fall.