When making any decision, explicitly state the probabilities of potential outcomes rather than relying on implicit expectations, as this self-conscious process allows for better learning and improvement.
When making predictions, always start by considering statistical base rates or the ‘outside view’ before adjusting your estimate based on the specific, idiosyncratic details or the ‘inside view’ of the situation.
Apply Fermi-style thinking by breaking down seemingly intractable problems into as many tractable, smaller components as possible, which helps to flush out ignorance and make the problem more manageable.
Make highly granular assessments of uncertainty, distinguishing even small differences (e.g., 55-45 bets from 45-55 bets), as this precision pays off in real-world judgments.
Participate in Kahneman-style debiasing exercises (e.g., a 50-minute module) to improve forecasting accuracy by approximately 10% over a year.
Engage in simulated problems (e.g., medical, economic, military diagnoses) with simulated data to practice and improve your ability to update beliefs appropriately in response to new evidence, following normative models like Bayes’ theorem.
When seeking predictions, aggregate diverse views by taking the average of forecasts from a group, as this ‘wisdom of the crowd’ often yields more accurate results than individual predictions.
Improve aggregated forecasts by giving more weight to individuals with better track records or specific attributes (e.g., intelligence, frequent updaters), creating weighted averages that outperform simple averages.
If multiple independent forecasters arrive at the same probability estimate (e.g., 0.7), and they have diverse, non-overlapping information, extremize that probability (e.g., to 0.85 or 0.9), as this suggests a stronger likelihood.
Beyond analyzing failures, critically examine forecasting successes by asking if luck played a role, if outcomes could have been different, or if you were ‘almost wrong,’ to avoid overlearning from potentially spurious correlations.
Be open-minded and willing to keep score of your predictions, as this allows for tracking accuracy and learning, despite the psychological resistance to being proven wrong.
Be willing to make and share estimates, even if they might appear ‘stupid’ to others, as this transparency allows for feedback and refinement of the decomposed problem.
When working in teams, foster an environment where members have mutual respect but are also willing to push each other hard, which is optimal for problem decomposition and forecasting.
Consider establishing small, incentivized groups within an organization to engage in ‘pure accuracy’ forecasting tournaments, with their probability estimates feeding up to senior executives to guide decision-making.
When making predictions for complex real-world problems, prioritize deliberate, analytical ’thinking’ over relying solely on rapid ‘blink’ intuition, as the latter is less reliable in messy, ill-defined domains.
Understand that history ‘rhymes’ with subtle and conditional patterns rather than repeating exactly, and be cautious not to overlearn or overgeneralize from past events.
To improve forecasting ability, avoid spending time trying to predict or model purely random events (like roulette wheel spins), as this is not a productive use of effort for becoming a super forecaster.
Generally, prioritize making predictions for shorter time ranges, as these are usually easier to foresee accurately than longer-term outcomes, though exceptions exist.