Coaching practices for Superforecasting When Starting Out
Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For Superforecasting When Starting Out, 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 build my estimate from my own optimistic story first and only glance at how long these things usually take at the very end as a sanity check
- Once I’ve made up my mind I dig in and won’t budge until I’m absolutely certain I was wrong
- Each project wraps up and I move straight on without ever writing down what I’d guessed versus what actually happened
- I’m stepping into something completely new to me, and I’ve done so much reading that I feel like an expert
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 - Anchor on the base rate before adding inside-view details
Start your forecast from the class median, then adjust — do not start from your narrative and adjust to the base rate.
Reference Class Forecasting - Update beliefs frequently and in small increments
When new evidence arrives, adjust your probability estimate — even if the change is small.
Superforecasting - 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 - Defer heavily to base rates when entering a domain where you lack experience
In unfamiliar territory, the class distribution should almost entirely govern the forecast.
The Outside View - 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 - Decompose complex questions into sub-questions
Break a hard forecasting question into smaller, estimable pieces and aggregate them.
Superforecasting - Update your forecast incrementally as new evidence arrives
Treat your forecast as a probability that should shift with each new piece of evidence, not a commitment that survives contradiction.
Reference Class Forecasting - Forecast a distribution, not a point estimate
Represent your forecast as a range of likely outcomes, not a single predicted number.
Reference Class Forecasting
Related concerns
- Superforecasting In A New Job
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 On A Budget
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 Practice
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 Techniques
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 With My Partner
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 With My Team
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.
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