This post is based on Shane Parrish's Knowledge Project interview with Philip Tetlock, co-principal investigator of The Good Judgement Project, author of 'Superforecasting: The Art and Science of Prediction', and a professor at the Wharton School.
7 Billion Forecasters
We make implicit forecasts on a daily basis. Whenever we choose to cross the road, whenever we buy a good, whenever we say something, we make an implicit forecast; we forecast the likelihood of being hit by a vehicle as we cross the road, we forecast our future use of the good and ascribe a present value to it, and we forecast the response we expect to elicit.
We make implicit forecasts on a daily basis. Whenever we choose to cross the road, whenever we buy a good, whenever we say something, we make an implicit forecast; we forecast the likelihood of being hit by a vehicle as we cross the road, we forecast our future use of the good and ascribe a present value to it, and we forecast the response we expect to elicit.
Implicit forecasts are convenient and serve us well when we are crossing roads, buying certain goods, and talking to people. Of course, you wouldn't want to spend hours by the roadside calculating the probability of death should you choose to cross the road at a certain moment. However, implicit forecasts are less useful when we are dealing with complex, nuanced situations (or making important investment decisions).
Explicit Forecasts
Implicit forecasts made with words are vague and can't be falsified. Explicit forecasts (with specific probabilities attached), by contrast, are easier to fault. This is why they enable us to hold ourselves accountable and evaluate past investment decisions. This evaluation is important because both luck and skill are involved in forecasting, and it is important to be able to consider alternative histories and attempt to distinguish lucky winners from good decisions, and unlucky losses from mistakes.
Implicit forecasts made with words are vague and can't be falsified. Explicit forecasts (with specific probabilities attached), by contrast, are easier to fault. This is why they enable us to hold ourselves accountable and evaluate past investment decisions. This evaluation is important because both luck and skill are involved in forecasting, and it is important to be able to consider alternative histories and attempt to distinguish lucky winners from good decisions, and unlucky losses from mistakes.
Good Forecasting
Forecasting is roughly 70% skill and 30% luck according to Tetlock's research. And, good forecasters are made not born. Good forecasters have specific knowledge in their field, are open-minded, and believe that it is worth estimating probabilities of real world events. We can make good forecasts by reading about our subject (in the case of investing, this may be the relevant company), and cultivate the necessary qualities for consistent outperformance by encouraging debate in our organisations and adding probabilistic thinking to our decision-making process. Most importantly, by second-guessing successes and evaluating failures, we can become better forecasters.
Forecasting is roughly 70% skill and 30% luck according to Tetlock's research. And, good forecasters are made not born. Good forecasters have specific knowledge in their field, are open-minded, and believe that it is worth estimating probabilities of real world events. We can make good forecasts by reading about our subject (in the case of investing, this may be the relevant company), and cultivate the necessary qualities for consistent outperformance by encouraging debate in our organisations and adding probabilistic thinking to our decision-making process. Most importantly, by second-guessing successes and evaluating failures, we can become better forecasters.
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