Expected Utility Theory
Expected Utility Theory is a foundational concept in economics, decision theory, and game theory that models how rational agents make choices under uncertainty. It posits that individuals evaluate decisions by calculating the expected utility of outcomes, weighting the utility of each possible outcome by its probability, and then selecting the option with the highest expected utility. This theory provides a mathematical framework for understanding risk preferences, such as risk aversion, risk neutrality, or risk seeking.
Developers should learn Expected Utility Theory when working on applications involving decision-making algorithms, such as in finance (e.g., portfolio optimization), artificial intelligence (e.g., reinforcement learning or game AI), or data science (e.g., predictive modeling under uncertainty). It is essential for designing systems that simulate or support human-like rational choices, optimizing resource allocation, or analyzing user behavior in scenarios with probabilistic outcomes.