Level 2 chaos

chaoslevel2There exists two kinds of chaos in the world. If you’ve read Sapiens, you already know and can skip straight to paragraph 5, however, if you haven’t let me walk you through it.

The essence of chaos is that there are innumerable uncontrolled forces at work and their unpredictable interactions are so complex that even extremely small variations in the strength of the forces and the way they interact can produce huge differences in outcomes.

Level 1 chaos is the kind of chaos in which predictions have no impact on the outcome of the chaos and it will continue to chart along the predicted path. Let us take for example the weather. The weather depends on a multitude of complicated factors. We learn through trial and error about factors that will help predict the weather and get better at prediction over time. The weather does not care about our predictions. It will continue to do as it wishes irrespective of whether we are prepared or aren’t.

On the other hand, we have level 2 chaos. Level 2 chaos very much cares about our predictions. These predictions in turn change the outcome of the chaos since the associated factors change based on predictions. Let us take the common example of commodity or stock prices. Based on the many factors that determine price, an analyst predicts that fuel prices will increase by $50 next week. Given the predicted increase in price, you now have every citizen lined up to refuel his or her vehicle(s). Consequently, instead of waiting for a week for prices to rise, they change within hours and no one has any idea what will happen the next week.

Why is this important for HR professionals to know? Because everything we do falls under level 2 chaos. Call it level 2 chaos or self-fulfilling bias – it is the primary reason why despite our best efforts we fail at predictive analysis ever so often. Should we stop our attempts at prediction? Of course not. Analysts continue to predict the stock market on a daily basis and these predictions are constantly influencing decisions of billions worldwide. As long as we are aware and know how to use this to our advantage, we should stride ahead.

Just don’t confuse predictive analytics indicating who leaves the organization with predicting the weather.

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