This framework excels in environments with structured uncertainty — pricing experiments, clinical trials, capital allocation under risk. Its main cost is requiring the decision-maker to make implicit assumptions explicit: priors, likelihoods, and utility functions all have to be written down.
Glossary
Bayesian Decision-Making
Odds, evidence and stakes, together.
In short
A formal framework for choosing among actions by combining prior probabilities, observed evidence, and the value or cost of each possible outcome. Rather than treating beliefs as fixed, a Bayesian decision-maker updates them as new data arrives and selects the action that maximizes expected value under the updated distribution.
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Related terms.
Term
Artifact Shape
The structural form an advisory answer takes, chosen to fit the question rather than forced into one house template.
Definition TermBase Rates
The underlying frequency at which an outcome occurs across a reference class — for example, the percentage of seed-stage startups that reach Series B, or the share of acquisitions that destroy shareholder value.
Definition TermBayesian Reasoning
A systematic method for updating beliefs based on new evidence, derived from Bayes' theorem in probability theory.
Definition TermBCG Matrix
A portfolio-analysis framework developed by the Boston Consulting Group in 1970 that classifies business units along two axes — market growth rate and relative market share — producing four quadrants: Stars, Cash Cows, Question Marks, and Dogs.
Definition TermBlameless Retrospective
A retrospective format — popularized by Etsy's John Allspaw and the broader Site Reliability Engineering community — that examines a failure or near-miss with the explicit ground rule that no individual will be blamed.
Definition GuideDecision intelligence, explained
The complete guide to making strategic calls with AI that disagrees on purpose.
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