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Decision theory or the theory of rational choice is a branch of probability, economics, and analytic philosophy that uses the tools of expected utility and probability to model how individuals should behave rationally under uncertainty. [1][2] It differs from the cognitive and behavioral sciences in that it is prescriptive and concerned with ...
Economics. In decision theory, the von Neumann–Morgenstern (VNM) utility theorem demonstrates that rational choice under uncertainty involves making decisions that take the form of maximizing the expected value of some cardinal utility function. This function is known as the von Neumann–Morgenstern utility function.
The expected utility hypothesis is a foundational assumption in mathematical economics concerning decision making under uncertainty. It postulates that rational agents maximize utility, meaning the subjective desirability of their actions. Rational choice theory, a cornerstone of microeconomics, builds this postulate to model aggregate social ...
Regret (decision theory) Measure of value difference between best possible decision and made decision. In decision theory, on making decisions under uncertainty —should information about the best course of action arrive after taking a fixed decision—the human emotional response of regret is often experienced, and can be measured as the ...
Uncertainty or incertitude refers to epistemic situations involving imperfect or unknown information. It applies to predictions of future events, to physical measurements that are already made, or to the unknown. Uncertainty arises in partially observable or stochastic environments, as well as due to ignorance, indolence, or both. [1]
Wald's maximin model. In decision theory and game theory, Wald's maximin model is a non-probabilistic decision-making model according to which decisions are ranked on the basis of their worst-case outcomes – the optimal decision is one with the least bad worst outcome. It is one of the most important models in robust decision making in ...
The "Markov" in "Markov decision process" refers to the underlying structure of state transitions that still follow the Markov property. The process is called a "decision process" because it involves making decisions that influence these state transitions, extending the concept of a Markov chain into the realm of decision-making under uncertainty.
Info-gap decision theory. Info-gap decision theory seeks to optimize robustness to failure under severe uncertainty, [1][2] in particular applying sensitivity analysis of the stability radius type [3] to perturbations in the value of a given estimate of the parameter of interest. It has some connections with Wald's maximin model; some authors ...