Outcome Blind

Outcome-blind evaluation is judging a decision by the quality of the process and the information available at the time, not by the outcome that followed. A good decision can lead to a bad result; a bad decision can lead to a good result. Luck and uncertainty ensure that outcomes are an imperfect signal of decision quality. The discipline is to separate "did we decide well?" from "did it work out?" and to reward and learn from the former while using outcomes to update beliefs and improve process.

In deciding and judging, the model corrects two errors. The first is outcome bias: judging decisions by results. We tend to call a decision good when it works and bad when it fails, even when the same process could have produced the opposite result. That distorts learning and incentives: people are rewarded or punished for luck, and we draw the wrong lessons from history. The second error is process worship: ignoring outcomes entirely and only caring about procedure. That is also wrong — outcomes contain information. The right stance is outcome-blind for evaluation of the decision (was the process sound? were the right inputs considered?) and outcome-aware for learning (what did we learn from what happened? how do we update?).

The model is central to probabilistic thinking and to building a decision-making culture that does not over-reward or over-punish luck. In investing, a single outcome tells you little about whether the thesis was right; you need many trials or an explicit outcome-blind review of the thesis and process. In strategy, a failed initiative may have been the right bet; a successful one may have been a bad bet that got lucky. Outcome-blind evaluation asks: conditional on what we knew and the process we used, was this a good decision?