Coaching practices for Superforecasting During Conflict
Describe almost anything you are working through and IX Coach finds the practices whose real-world fit is closest. For Superforecasting During Conflict, these are the strongest matches in the current practice library.
Does this sound like the set of challenges you might be facing?
- I commit to one confident number
- 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 catch myself shaving down the estimate I’m about to present
- Whenever my team decides anything, the loudest or most senior person’s view just takes over the room and everyone quietly falls in line
- 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
- 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. - Forecast a distribution, not a point estimate
Represent your forecast as a range of likely outcomes, not a single predicted number.
Reference Class Forecasting - Treat forecasting accuracy as a skill that improves with practice
Take prediction errors as performance feedback, not as proof that forecasting is futile.
Superforecasting - 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 - 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. - Use structured team forecasting to aggregate diverse views
Combine independent estimates from multiple people before the group talks — aggregation beats any single expert.
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 - Update beliefs frequently and in small increments
When new evidence arrives, adjust your probability estimate — even if the change is small.
Superforecasting - Decompose complex questions into sub-questions
Break a hard forecasting question into smaller, estimable pieces and aggregate them.
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.
Related concerns
- 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 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 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 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 Skills
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.
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