7 Debiasing and Better Decisions Free Online FlashCards

When are heuristics most likely to mislead?

Heuristics can work well in familiar, predictable situations, but are more likely to mislead when evidence is sparse, outcomes are uncertain, or a situation only seems unique.

What makes expertise dependable?

Expertise is most dependable when the environment has learnable regularities and people receive useful feedback. Confidence or years of experience alone do not guarantee accuracy.

What does debiasing do—and not do?

Debiasing uses habits and procedures to reduce predictable errors; it does not guarantee correctness, and methods should be matched to the decision.

What should defining a consequential decision include?

State what must be chosen, by when, and what would count as a good outcome. Judge the reasoning available at the time separately from how events turn out.

How can considering alternatives counter confirmation bias?

Identify at least one credible alternative and the evidence that would distinguish it. This helps counter confirmation bias, the tendency to favor evidence supporting an existing belief.

How do you use the outside view?

Compare the plan with genuinely similar past cases: how often they succeeded, how long they took, and what they cost. Adjust the baseline for important differences.

How should a decision-maker make uncertainty explicit?

List key assumptions, unknowns, plausible outcomes, and consequences. Estimate probabilities when possible instead of relying on vague labels such as “likely.”

What does a premortem reveal, and what does it not prove?

Imagine the decision has failed, list plausible reasons, then identify which risks could be prevented, monitored, or planned for. A premortem surfaces risks; it does not prove failure is likely.

How should reversibility affect a decision’s review plan?

Set what new evidence would change the choice and when to review it. A small trial may suit a reversible, low-cost choice; an irreversible or high-impact choice calls for stronger evidence and independent review.

What does it mean for a forecaster to be calibrated?

A forecaster is calibrated when events assigned a given probability occur at about that frequency across many comparable forecasts.

How are forecast accuracy and calibration different?

Accuracy concerns whether forecasts are correct; calibration concerns whether stated probabilities match event frequencies over many comparable forecasts. A forecaster can rank likelihoods well yet assign probabilities that are systematically too high or low.

What should a decision journal record?

Record the question, date, probability estimate, reasons, key assumptions, and the outcome that will resolve the forecast. Later compare estimates with outcomes.