Machine learningGame-theoretic
随机效用模型
随机效用模型(Random Utility Model)通过假设决策者从备选方案中获得不确定的效用,并选择效用最高的选项来解释离散选择行为。该模型由Daniel McFadden于1974年提出,将效用分解为系统性(可观测)和随机性(个体特有)两部分,从而实现概率性选择预测。Logit模型作为一种参数化设定,能够产生封闭形式的选择概率,广泛应用于市场营销、交通运输和环境估价等领域。
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如何引用本页
ScholarGate. (2026, June 3). Random Utility Model with Probabilistic Choice. ScholarGate. https://scholargate.app/zh/game-theory/random-utility-model
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