chapter seven
7 Beyond Bayesian inference: Raiffa, Schlaifer, and rational decision-making
This chapter covers
- Raiffa and Schlaifer’s Applied Statistical Decision Theory (1961) and how it extended Bayes’ Theorem from updating beliefs to guiding decisions
- How expected utility and decision trees turn posterior probabilities and preferences into rational choices
- How value-of-information analysis determines whether additional evidence is worth acquiring
- How conjugate priors made Bayesian updating practical by replacing complex integration with repeatable parameter updates
- Why Raiffa and Schlaifer’s framework remains foundational, especially in managerial decision analysis
Thomas Bayes established a method for revising beliefs when new evidence becomes available. Rather than treating a probability as fixed, Bayes’ Theorem showed how a prior belief could be combined with observed data to produce a posterior probability. The result was a coherent framework for learning from evidence. It remained focused on inference, however. A posterior probability could describe how likely something was without indicating which action should follow or how the consequences of that action should be evaluated.