Utility functions are mathematical representations that quantify an individual's preferences over a set of choices, assigning a real number to each possible option based on the level of satisfaction or value it provides. These functions help in modeling decision-making under uncertainty by illustrating how different outcomes are valued, which is crucial when individuals or entities must choose among uncertain prospects. In the context of machine learning approaches to game-theoretic problems, utility functions are essential for designing algorithms that can predict and adapt to various strategic interactions based on preferences.
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