I’m currently reading Thinking Fast & Slow by Daniel Kahneman. Today was an interesting read about how we can overcome our predictive biases by incorporating a baseline as an anchor.
Here’s an example to see it in action: How would you accurately predict the GPA of a student in college who began reading fluently at the age of four. Our judgments about the intelligence of a person who reads fluently at age four are quite pronounced. So much so that we’re likely to over-exaggerate the life achievements of such individuals. In the reverse, we’re likely to over-exaggerate the failures of individuals for whom reading comes slowly in comparison to the average.
Kahneman suggests that we can be more accurate fortune tellers if we start with a baseline to derive our predictions. In this particular question, a baseline could be an estimation of the average college GPA. Next, consider the average GPA of exceptional students followed by your belief of the correlation between early reading fluency and GPA and finally calculate your GPA prediction by moving “x” percentage points from the baseline to the exceptional GPA using your percentage correlation.
I found this methodology to be quite interesting but also doubtful how many times I would actually use it day-to-day. It does help to regulate our predictions and prevents our irrational judgments from taking control of our decisions. For example, in predicting the growth of a particular stock you would look at the industry average. Consider the growth of exceptional stocks. Determine the correlation between a piece of information to stock growth potential, and move “x” correlative percentage points from your baseline towards your high-performance rate to arrive at a controlled growth rate for your investment. This could protect you from going all in and potentially losing a lot of money. I hope that some of you find this useful.