Judge decisions by the process, not the result
A good decision that produces a bad outcome is still a good decision.
Why it works
Outcome bias — judging decision quality by what happened rather than by what was known before the decision — is pervasive and damaging. It causes people to reverse correct processes that got unlucky and to repeat incorrect processes that happened to succeed. Expected value thinking separates these: a decision is good if it correctly used available information to maximize expected payoff, regardless of which outcome the probability dice rolled.
How to do it
- After any significant outcome, separately evaluate: (1) what was known at decision time, (2) whether the process used that information well, and (3) what the outcome was.
- Resist the urge to infer decision quality from outcome quality alone.
- Keep a decision journal: record your reasoning before the outcome, so you can compare process to result honestly later.
- Ask: "Given what I knew then, would I make the same decision again?" If yes, a bad outcome is a bad roll, not a bad decision.
Evidence
Outcome bias is well documented: people give lower evaluations to the same decision when it is paired with a bad outcome, even when the outcome was due to chance. Baron and Hershey (1988) provide the foundational experimental evidence. (rct)
Distinguishing process from outcome is easy in principle and genuinely hard under social pressure; colleagues who judge by results may not share your framework.
Sources
- Baron & Hershey (1988), outcome effects in decision evaluation, Journal of Personality and Social Psychology
Common mistake
Feeling retrospectively foolish about a correct decision that went badly, and adjusting future decisions toward "safer" options that lower expected value to reduce regret.
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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.
- 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.
- Keep a decision journal to score your EV estimates
Log your probability estimates and payoff predictions, then compare them to what happened.
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