As much as we’d like to say we don’t predict, any [[Decision-Making|decision]] is a prediction: it is a bet on the future state of the world. Prediction is unavoidable. The simplest tasks like sitting in a chair involve predicting that the chair will hold. The key is to focus on measuring the right variables and then mapping them to future states to understand the causal feedback loops. And then do it over and over again, because no process can improve without iteration. ## Why Engineers are Great at Predicting They focus on small things that they can predict with high certainty. They don't put a lot of energy into making a grand prediction, instead they focus on what is obvious or what may be hidden from plain sight. Think of building a bridge: you know with high confidence if you don't do the proper calculations the bridge will fail. You may be able to build the bridge and it may hold for some time, but if it is structurally inferior it will fail at some point. Engineers tend to work in high-cost environments like bridge building, or airplane manufacturing where their errors could result in a large loss of human life. As such they approach their problems with a level of professionalism that we do not see in the financial markets. In the markets highly flawed statistical techniques are recognized but used regularly to make flawed predictions. Forecaster will say in the same breath how the market are too irregular to predict and then turn around and offer their market prediction! I have run across two engineers that have left their field to focus on either gambling or the financial markets which I believe are two very similar domains: 1. [Warren Sharp](https://www.sharpfootballanalysis.com/) 2. [Matt Hollerbach](https://breakingthemarket.com/) 3. [Agustin Lebron](https://twitter.com/AgustinLebron3?s=20) Once they identify the high probability outcome their focus shifts to the causal chain reaction that may result from that outcome. Back to the bridge analogy, one support wire may be left out for cost reasons but that small detail places additional load on the entire structure and makes it fragile which increases the probability of failure. This is where the red meat of prediction is: understanding the causal process and how small details compound into large reactions. **Case Studies:** - [[CHG Issue 195 A Machine for Discontent]] - [[CHG Issue 119 When Reality Doesn't Meet Expectations]] - [[CHG Issue 115 Prediction]] - [[CHG Issue 104 Ego]] - [[CHG Issue 57 Preparation Not Prediction]] - [[CHG Issue 20]] Explore Further: [[Learning]] | [[Expert Problem]] Tags: #evergreen Your support for Cedars Hill Group is greatly appreciated <form action="https://www.paypal.com/donate" method="post" target="_top"> <input type="hidden" name="hosted_button_id" value="74PGN8ZXHQVHS" /> <input type="image" src="https://www.paypalobjects.com/en_US/i/btn/btn_donate_LG.gif" border="0" name="submit" title="PayPal - The safer, easier way to pay online!" alt="Donate with PayPal button" /> <img alt="" border="0" src="https://www.paypal.com/en_US/i/scr/pixel.gif" width="1" height="1" /> </form>