Keep a decision journal to score your EV estimates
Log your probability estimates and payoff predictions, then compare them to what happened.
Why it works
EV thinking is only as good as the quality of the probability and outcome estimates feeding it. Without feedback, estimates drift toward whatever is emotionally convenient. A decision journal creates accountability: you record your estimates before the outcome, then revisit after. This feedback loop is the same mechanism that makes calibration practice effective — seeing systematic errors allows deliberate correction.
How to do it
- Before each significant decision, write: the scenarios, the probabilities, the estimated values, and the EV result.
- Note which option you chose and whether it matched the highest EV.
- After the outcome resolves, record what actually happened.
- Quarterly, review your estimates for systematic biases: optimism, recency, specific domain overconfidence.
Evidence
Decision journaling is a widely endorsed practice in forecasting and decision quality communities. Its feedback mechanism is identical to calibration practice, which has observational evidence for improving predictive accuracy in expert forecasters. (mechanistic)
The journal practice relies on honest retrospective evaluation; post-outcome motivated reasoning can contaminate even written records unless the pre-outcome note is detailed.
Sources
- Tetlock & Gardner (2015), Superforecasting, Crown Publishers
Common mistake
Recording only the decisions that went well, or rewriting the original prediction after the outcome is known — both eliminate the feedback the journal was designed to provide.
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More practices for Expected Value Thinking: Deciding Under Uncertainty
- Enumerate scenarios and their probabilities before deciding
Write down each meaningful outcome, assign a probability, and compute the weighted total.
- Judge decisions by the process, not the result
A good decision that produces a bad outcome is still a good decision.
- Calculate the expected value of gathering more information
Before researching further, ask whether the additional information is actually worth the cost to obtain.
- Adjust raw expected value for risk aversion on large stakes
A 50% chance of losing everything is not equivalent to a certain 50% loss — adjust for your actual risk tolerance.
- Accept positive-EV decisions even when they feel uncomfortable
If the expected value is clearly positive, take the decision — even if most individual outcomes are losses.
- Look for decisions with asymmetric upside — large potential gain, small defined loss
Seek situations where the worst case is bounded and small while the best case is large and open-ended.
Related concepts
- Bayesian Thinking: How to Update Beliefs Rationally
Holding beliefs as probabilities and updating them when evidence arrives
- Thinking, Fast and Slow, Made Usable
Two systems, the biases they create, and when to slow down
- Mental Models: Charlie Munger’s Latticework Approach
Building the multi-disciplinary toolkit that lets you see what single-discipline thinkers miss
- Opportunity Cost Thinking: What You Give Up When You Choose
The hidden price of every choice — and the practices that make it visible