Decision theory is a branch of probability, economics, and analytic philosophy that uses expected utility and probability to model how individuals would behave rationally under uncertainty. It provides mathematical foundations for fields including sociology, economics, criminology, cognitive science, moral philosophy, and political science. The practical application of its prescriptive branch is called decision analysis, which develops tools and software to help people make better decisions.
Who founded decision theory and when did it originate?
Decision theory has roots in probability theory developed by Blaise Pascal and Pierre de Fermat in the 17th century. Daniel Bernoulli advanced the field in 1738 with his paper Exposition of a New Theory on the Measurement of Risk, and John von Neumann and Oskar Morgenstern formalized expected utility theory in the 1940s. The phrase "decision theory" itself was introduced by E. L. Lehmann in 1950.
What is the difference between normative and descriptive decision theory?
Normative decision theory identifies optimal decisions for an idealized, fully rational agent able to calculate with perfect accuracy. Descriptive decision theory, by contrast, documents how people actually make decisions by identifying consistent rules or frameworks that govern real behavior, such as Amos Tversky's elimination-by-aspects model.
What is prospect theory and how does it relate to decision theory?
Prospect theory was developed by Daniel Kahneman and Amos Tversky to describe how people actually make decisions when outcomes carry risk. It found three key regularities: losses feel larger than equivalent gains, people focus on changes in their situation rather than absolute utility levels, and probability estimates are distorted by anchoring. Prospect theory modified expected utility theory to incorporate these psychological factors.
What is the gambler's fallacy in decision theory?
The gambler's fallacy is the mistaken belief that an isolated random event is affected by previous random events. A fair coin always has a 0.5 probability of landing tails on any given flip, regardless of previous outcomes. People commit the fallacy when they use the heuristic that outcomes should balance out and predict that a result is "due" after a run in the opposite direction.
What is the ludic fallacy in decision theory?
The ludic fallacy is the criticism that decision theory based on a fixed universe of possibilities only addresses "known unknowns" and cannot account for "unknown unknowns," events that fall entirely outside the model. The argument holds that significant real-world events are sometimes precisely those no model anticipated, and that unquestioning reliance on models blinds analysts to their own limits.