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Decision Quality vs Outcome

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Decision Quality vs Outcome

A good decision can produce a bad outcome, and a bad decision can produce a good outcome. Howard Marks returns to this idea through Nassim Taleb's "alternative histories," C. Jackson Grayson's Decisions Under Uncertainty, and Annie Duke's Thinking in Bets: the things that reasonably could have happened but did not.

The quality of a decision should be judged by the process and probabilities available before the outcome, not by hindsight alone. Outcomes are single draws from a distribution; they reveal what happened, not necessarily what was most likely or what was wise.

Evaluation Checklist

When judging a decision, ask:

  1. What information was available at the time?
  2. What alternatives existed?
  3. What probabilities could reasonably be assigned?
  4. What were the possible payoffs and losses?
  5. Was the process sound even if the result was bad?
  6. Was the result lucky even if the process was weak?

Why It Matters

Outcome bias makes people over-credit winners and over-blame losers. In investing, this can cause managers to abandon good processes after temporary losses, imitate lucky winners, or mistake a single success for skill.

This concept overlaps with active-management-as-error-detection because active investors must distinguish sound reasoning from lucky outcomes, and with ergodicity because survival depends on paths, not just average results.

In "You Bet!" (2020), Marks connects this to games: blackjack, backgammon, gin, bridge, poker, and betting all force decisions without complete information. The skilled player does not seek certainty. They know probabilities, update as evidence changes, size bets appropriately, and accept that even good process can lose on a single hand.

In "Nobody Knows II" (2020), he applies the same logic to COVID. The correct response was not to pretend to know the path of the virus or the market, but to separate facts from inferences and guesses, then act in stages as price improved relative to value.

Taleb's Alternative Histories

Taleb makes the same idea more severe in fooled-by-randomness. A decision must be evaluated against alternative-histories: the plausible paths that could have happened but did not. A trader who made money may have been exposed to a ruinous path that simply did not arrive during the observed sample.

This means outcome review should include the invisible distribution:

  • What could have gone wrong?
  • How bad was the tail?
  • Was the successful result robust or lucky?
  • Would the same process survive many repetitions?

Sources