Subject

Decision Science

Deciding under uncertainty, and keeping a record you can learn from.

Tutorials

Glossary terms

  • Base Rate

    How common something is in the whole relevant population, before looking at any specific evidence.

  • Brier Score

    The average squared difference between forecast probabilities and what happened. Lower is better; always saying 50% scores 0.25.

  • Calibration

    Events forecast at p% happen about p% of the time. Calibration is separate from accuracy: an overconfident forecaster is not calibrated.

  • Confirmation Bias

    Looking for, noticing and remembering evidence that supports what we already believe, and discounting what contradicts it.

  • Decision Record

    A short, dated note made at the time of a decision: options, evidence, assumptions, the choice and what would change it.

  • Explainable Scoring

    Returning, with every score, the reasons behind it: which rules fired, what each criterion contributed and what data were missing.

  • Framing

    Choosing which aspects of a situation to emphasise so as to suggest a particular problem, cause or remedy. The same facts can be framed in different ways.

  • Hindsight Bias

    Once an outcome is known, believing it was predictable and that you knew it all along. It makes the past look more certain than it was.

  • OODA Loop

    John Boyd's model of decision-making as a repeating cycle of observing, orienting, deciding and acting. Orientation, how we interpret what we see, is where bias enters.

  • Outcome Bias

    Judging a decision by how it turned out instead of by how well it was made with what was known at the time.

  • Pre-mortem

    A planning exercise: imagine the project has already failed and write down why, before starting. Treating failure as certain makes people name risks they would otherwise keep quiet about.

  • Proxy Metric

    A measurable stand-in for something you cannot measure directly. Proxies work until they become targets: then the number stops tracking what it was meant to represent.

  • Weighted Scoring Model

    Rates each candidate on several criteria, multiplies each rating by an agreed weight and adds them up. Trade-offs become explicit, and the answer depends on the weights.