Coaching practices for Superforecasting as a Parent
Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For Superforecasting as a Parent, 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
- Once I’ve made up my mind I dig in and won’t budge until I’m absolutely certain I was wrong
- I’m guessing how this goal of mine will play out purely from my own picture of it, and it feels sure to work
- New information keeps arriving that should obviously move my estimate, but I just cling to the date I first committed to
- 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
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 - Update beliefs frequently and in small increments
When new evidence arrives, adjust your probability estimate — even if the change is small.
Superforecasting - 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 - 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 - Decompose complex questions into sub-questions
Break a hard forecasting question into smaller, estimable pieces and aggregate them.
Superforecasting - 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 - Plan behavior management strategies in advance
Decide how you will respond to predictable problems before they happen.
Triple P Parenting (Matthew Sanders) - 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
Related concerns
- 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
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 As A Caregiver
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 For My Teenager
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.
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