The Bayesian decision theory, which is presented in this book as a novel contribution to the decision theoretical field, is a neo-Bernoullian utility theory which also aims to improve on expected utility theory, as did game theory and prospect theory before it. But in its approach it takes the middle road, just as Daniel Bernoulli himself did when he wrote his St. Petersburg paper (see also Chapter 4), in that it recognizes both the desirability of mathematical first principles as well as the necessity for any mathematical theory of human rationality to be able to stand to the benchmark of “common sense.”
The structure of this chapter on the Bayesian decision is as follows. First a theoretical discussion of the Bayesian decision theory is given. Thereafter, the alternative criterion of choice which is proposed in the Bayesian decision theory is used to discuss and explain why type II events are perceived to be more risky than type I events.
In this section, a theoretical discussion of the Bayesian decision theory is given. This is done by relating the Bayesian decision theory to the expected outcome and expected utility theories that came before it. Expected outcome theory has been around since the seventeenth century, when the rich merchants of Amsterdam bought and sold expectations as if they were tangible goods. As already expounded in this book, the algorithmic ...