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Decision quality when attention is limited People often assume that better decisions require more information. In practice, information competes for a limited attention budget. A founder can watch ten interviews about hiring, an investor can read twenty market reports, and a student can collect dozens of lectures, yet still fail to identify the few ideas that change the decision. Attention is not an unlimited container. Every additional source has an opportunity cost: the time spent consuming it cannot be used to test an assumption, compare alternatives, or rest. A useful system therefore does more than shorten content. It preserves decision-relevant evidence and makes the cost of further attention visible. The first distinction is between reversible and irreversible decisions. A reversible decision can be changed cheaply after new evidence appears. Choosing the order of two experiments or trying a meeting format for one week usually belongs in this group. An irreversible decision is expensive or impossible to undo, such as signing a long contract, publishing sensitive data, or closing a product line. Reversible decisions should move quickly with a modest evidence threshold. Irreversible decisions deserve more analysis, more independent evidence, and an explicit review of downside. Treating every choice as irreversible creates paralysis; treating every choice as reversible creates avoidable damage. Expected value provides a common language for comparing options. It combines the size of a possible outcome with the probability that the outcome occurs. A small chance of a very large loss can matter more than a high chance of a minor benefit. Expected value is not a promise and it is not a substitute for judgment. The probabilities may be uncertain and some outcomes cannot be reduced to money. Its role is to expose hidden assumptions. When two people disagree, they can ask whether they disagree about the possible outcomes, the probability estimates, the time horizon, or the value assigned to each result. Base rates protect the decision from a compelling but unrepresentative story. A team may hear about one company that doubled growth after a rebrand and assume the same action will work for them. The base rate asks what usually happens across comparable cases. Specific evidence still matters, especially when the current case differs in a meaningful way, but it should update a baseline rather than replace it. Starting from the outside view reduces overconfidence. The strongest analysis states both the base rate and the concrete reasons why this case might perform better or worse than the reference group. A pre-mortem reveals risks before commitment. The team imagines that the decision has failed six months in the future and asks what caused the failure. This framing makes it socially easier to mention concerns that sound disloyal during an enthusiastic planning session. A useful pre-mortem produces specific failure mechanisms, not vague pessimism. For each mechanism, the team identifies an early warning signal and a mitigation. If a product launch might fail because onboarding is confusing, the warning signal could be a low completion rate in a small pilot, and the mitigation could be a guided setup tested before public release.