Coaching practices for Superforecasting Skills
Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For Superforecasting Skills, these are the strongest matches in the current practice library.
Does this sound like the set of challenges you might be facing?
- When a prediction of mine blows up, I either spiral into thinking the whole thing is pointless or I just shrug and forget it
- I commit to one confident number
- The question is so big and tangled that I just go with my gut and blurt out a number, and it feels like a guess because it is one
- Once I’ve made up my mind I dig in and won’t budge until I’m absolutely certain I was wrong
- I make calls about how things will go all the time, but I never write them down or check them after
Practices that may help
- Superforecasting
Philip Tetlock’s forecasting tournament research found that a subset of ordinary people — "superforecasters" — consistently outperform domain experts and intelligence analysts at probabilistic prediction. They share measurable cognitive and behavioral habits: they think in probabilities, update frequently on evidence, and actively seek disconfirming information. These habits are learnable. - Treat forecasting accuracy as a skill that improves with practice
Take prediction errors as performance feedback, not as proof that forecasting is futile.
Superforecasting - Reference Class Forecasting
Reference class forecasting, developed by Daniel Kahneman and Amos Tversky and formalized by Bent Flyvbjerg, improves forecast accuracy by anchoring on the statistical distribution of outcomes for similar past projects rather than on the details of the current one. The method reliably corrects the optimism bias that inflates cost, time, and benefit estimates in planning — the evidence base here is real and specific. - Forecast a distribution, not a point estimate
Represent your forecast as a range of likely outcomes, not a single predicted number.
Reference Class Forecasting - Decompose complex questions into sub-questions
Break a hard forecasting question into smaller, estimable pieces and aggregate them.
Superforecasting - Update beliefs frequently and in small increments
When new evidence arrives, adjust your probability estimate — even if the change is small.
Superforecasting - Score your own past predictions to calibrate your outside-view use
Keep a forecast log and score it — you cannot improve calibration without feedback on where you were over- or under-confident.
The Outside View - Distinguish cognitive optimism bias from strategic misrepresentation
Recognize that some forecast inflation is genuine bias and some is deliberate spin — they require different fixes.
Reference Class Forecasting - Conduct post-project debriefs to contribute to the class data
Record actual vs. forecast outcomes honestly — this builds the reference class that future forecasts depend on.
Reference Class Forecasting - Affect Forecasting: Why You Mispredicted How You’d Feel
Daniel Gilbert and colleagues showed that people reliably misjudge both the intensity and duration of their future emotional reactions — overestimating how bad bad events will feel and how good good events will feel. The mechanism is "impact bias": we focus on the event and ignore the adaptive processes that will moderate it. This research is well-replicated and has direct implications for how you make decisions, build habits, and manage expectations.
Related concerns
- How To Improve Forecasting
Reference class forecasting, developed by Daniel Kahneman and Amos Tversky and formalized by Bent Flyvbjerg, improves forecast accuracy by anchoring on the statistical distribution of outcomes for similar past projects rather than on the details of the current one. The method reliably corrects the optimism bias that inflates cost, time, and benefit estimates in planning — the evidence base here is real and specific.
- How To Practice Forecasting
Record actual vs. forecast outcomes honestly — this builds the reference class that future forecasts depend on.
Conduct post-project debriefs to contribute to the class data
- Superforecasting
Philip Tetlock’s forecasting tournament research found that a subset of ordinary people — "superforecasters" — consistently outperform domain experts and intelligence analysts at probabilistic prediction. They share measurable cognitive and behavioral habits: they think in probabilities, update frequently on evidence, and actively seek disconfirming information. These habits are learnable.
- Superforecasting At Work
Philip Tetlock’s forecasting tournament research found that a subset of ordinary people — "superforecasters" — consistently outperform domain experts and intelligence analysts at probabilistic prediction. They share measurable cognitive and behavioral habits: they think in probabilities, update frequently on evidence, and actively seek disconfirming information. These habits are learnable.
- Superforecasting During A Big Change
When new evidence arrives, adjust your probability estimate — even if the change is small.
Update beliefs frequently and in small increments
- Superforecasting During Conflict
Philip Tetlock’s forecasting tournament research found that a subset of ordinary people — "superforecasters" — consistently outperform domain experts and intelligence analysts at probabilistic prediction. They share measurable cognitive and behavioral habits: they think in probabilities, update frequently on evidence, and actively seek disconfirming information. These habits are learnable.
Describe your situation in your own words to search the complete practice library.