Coaching practices for Domain Experience Forecasting
Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For Domain Experience Forecasting, these are the strongest matches in the current practice library.
Does this sound like the set of challenges you might be facing?
- I’m stepping into something completely new to me, and I’ve done so much reading that I feel like an expert
- I’m convinced my project is special and the usual horror stories don’t apply to me
- Each project wraps up and I move straight on without ever writing down what I’d guessed versus what actually happened
- I commit to one confident number
- New information keeps arriving that should obviously move my estimate, but I just cling to the date I first committed to
Practices that may help
- 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 - Identify the right reference class for your situation
Find a well-defined set of past situations that are structurally similar to yours and collect their outcome data.
Reference Class Forecasting - 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. - 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 - Forecast a distribution, not a point estimate
Represent your forecast as a range of likely outcomes, not a single predicted number.
Reference Class Forecasting - 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 - 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 - 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. - Run prototyping conversations before committing to a plan
Talk to people already living your Plan B or C before you decide whether to pursue it.
Odyssey Plans: Designing Three Alternative Futures - Decompose complex questions into sub-questions
Break a hard forecasting question into smaller, estimable pieces and aggregate them.
Superforecasting
Related concerns
- Range Based 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.
- Reference Class Forecasting After A Loss
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.
- Reference Class Forecasting As A Caregiver
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.
- Reference Class Forecasting At Work
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.
- Reference Class Forecasting For My Teenager
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.
- Reference Class Forecasting On A Budget
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.
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