Coaching practices for Superforecasting with Friends

Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For Superforecasting with Friends, 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
• Whenever my team decides anything, the loudest or most senior person’s view just takes over the room and everyone quietly falls in line
• I’m guessing how this goal of mine will play out purely from my own picture of it, and it feels sure to work
• I’m running endless mental simulations of how this big choice will feel and convincing myself I already know
• Once I’ve made up my mind I dig in and won’t budge until I’m absolutely certain I was wrong

Practices that may help

1. 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.
2. Treat forecasting accuracy as a skill that improves with practice
   Take prediction errors as performance feedback, not as proof that forecasting is futile.
   Superforecasting
3. Use structured team forecasting to aggregate diverse views
   Combine independent estimates from multiple people before the group talks — aggregation beats any single expert.
   Superforecasting
4. Use reference classes to ground personal estimates in base rates
   Before estimating how your situation will unfold, find similar past situations and check what happened.
   Bayesian Thinking: How to Update Beliefs Rationally
5. 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.
6. Ask people who’ve been there instead of imagining it
   Other people’s actual experiences predict your future feelings more accurately than your own imagination.
   Affect Forecasting: Why You Mispredicted How You’d Feel
7. Update beliefs frequently and in small increments
   When new evidence arrives, adjust your probability estimate — even if the change is small.
   Superforecasting
8. Forecast a distribution, not a point estimate
   Represent your forecast as a range of likely outcomes, not a single predicted number.
   Reference Class Forecasting
9. 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.
10. 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

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