情景不确定性与学习下的最优停止和撤资时机
Optimal stopping and divestment timing under scenario ambiguity and learning
AI总结：
本研究针对情景模糊与学习环境，提出将最优撤资决策建模为模糊厌恶下的最优停止问题，并通过极小极大结果简化求解，应用于股票出售和燃煤电厂撤资实例。
摘要：
为了分析环境转型对资产价值及资产搁浅的影响，我们研究了一个经济主体的最优停止和撤资时机决策，该主体的未来收入取决于从一组给定的可能未来情景中实现的情景。由于未来情景未知，且各个预期情景的概率具有模糊性，我们采用Klibanoff等人（2005）的模糊厌恶下决策的平滑模型，将最优撤资决策构建为模糊厌恶下带学习的最优停止问题。随后，我们证明了一个极小极大结果，将该问题简化为一系列带学习的标准最优停止问题。该理论通过两个例子加以说明：具有模糊漂移的股票最优出售问题，以及转型情景模糊下燃煤电厂的最优撤资问题。
英文摘要：
Aiming to analyze the impact of environmental transition on the value of assets and on asset stranding, we study optimal stopping and divestment timing decisions for an economic agent whose future revenues depend on the realization of a scenario from a given set of possible futures. Since the future scenario is unknown and the probabilities of individual prospective scenarios are ambiguous, we adopt the smooth model of decision making under ambiguity aversion of Klibanoff et al (2005), framing the optimal divestment decision as an optimal stopping problem with learning under ambiguity aversion. We then prove a minimax result reducing this problem to a series of standard optimal stopping problems with learning. The theory is illustrated with two examples: the problem of optimally selling a stock with ambiguous drift, and the problem of optimal divestment from a coal-fired power plant under transition scenario ambiguity.
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