不确定性下的投资选择:期望效用理论解析

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"经济学第三章不确定性下的个体选择理论,探讨了在不确定性环境下,如投资者决策,引入了彩票和期望效用的概念,分析期望效用函数的性质、最大化规则及挑战,涉及确定性等价和风险厌恶系数,以及一阶和二阶随机占优的比较。预备知识包括集合论、线性代数、微积分和概率统计。讨论了风险与不确定性的区别,并在分析中将两者视为等同。期望效用理论源于贝努利,由多位学者发展成公理化体系,用于不确定性和风险决策问题。" 在经济学中,不确定性是决策者面临的一个核心问题。当决策结果不是单一确定时,不确定性就产生了。通常,如果结果的可能性和相应的概率是已知的,这被定义为风险,例如,赌博中的概率。然而,当概率分布未知时,这就被称为不确定性,如奈特所强调的。在实际分析中,为了简化问题,通常会将不确定性等同于风险,通过引入主观概率来量化不确定性的各个可能结果。 期望效用理论是处理这种不确定性的关键工具。它假设决策者有一个效用函数,可以衡量他们对不同结果的偏好。这个函数结合了结果的可能性(概率)和效用值,计算出期望效用。理论中的一个重要假设是决策者会最大化期望效用,即选择那些具有最高期望效用的选项。 期望效用函数的存在性和性质是理论的基础。它需要满足一定的公理,如完备性、传递性、非饱和性等,确保决策者的偏好是连贯的。此外,函数的凹性或凸性反映了决策者的风险态度:凹函数表示风险厌恶,凸函数表示风险喜好。 确定性等价是衡量决策者对待风险的另一种方式,它是一个假设所有风险都被消除时,决策者愿意接受的确定性金额。风险厌恶系数则是衡量决策者相对于期望效用对波动性的敏感度。 一阶和二阶随机占优是评估资产收益和风险特征的工具。一阶随机占优意味着一个投资组合的期望收益高于另一个,同时所有可能结果的风险(如标准差)都不高于后者。二阶随机占优则更加强烈,不仅要求期望收益更高,而且整个风险分布也更优。 期望效用理论在实践中有着广泛应用,比如金融市场的投资决策、保险业的风险管理等。然而,这个理论也存在局限性,如无法解释某些决策者可能对某些特定损失表现出过度敏感的行为(即损失厌恶),以及在极端不确定情况下可能的规避行为。 经济学中的不确定性与风险决策理论提供了一种理解人类在信息不完全情况下的行为方式,帮助我们构建模型来预测和解释复杂的经济现象。然而,它也需要不断适应和修正,以更精确地反映现实世界的决策过程。
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Many important problems involve decision making under uncertainty -- that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.