在信息获取成本下的决策制定：可逆与不可逆决策的范式
Decision Making under Costly Sequential Information Acquisition: the Paradigm of Reversible and Irreversible Decisions
AI总结：
本文研究了在信息获取成本下决策制定的问题，通过可逆与不可逆决策的范式，探讨了决策者在面对不确定信息时的最优策略及模型分析。
摘要：
在现代随机系统中，包括电子商务平台、金融市场和医疗系统，决策制定已发展为一个复杂的多方面过程，结合了信息获取和自适应信息源。本文开始研究此类集成设置，其中这些元素不仅是基本的，而且以复杂且随机交织的方式相互作用。我们引入了一个相对简单的模型，但能够捕捉到涉及的新型元素。决策者（DM）可以在已知价值的现有产品A和价值未知的新产品B之间进行选择。同时，DM会观察到关于B未知价值的信号，并可以选择在最初选择B后将其转换为产品A。数学上，该模型导致了具有不同信息制度（购买产品B之前和之后）的连续最优停止问题，这些制度由初始较粗略的信号和随后更准确的信号区分。我们通过主要使用粘性解技术详细分析了底层问题，从而脱离了现有信息获取文献中基于传统最优停止论的文献。更广泛地说，本文引入的建模方法提供了一个新的框架，通过一系列嵌套障碍问题，开发了更复杂互动的决策、信息源和信息成本在随机环境中的模型。
英文摘要：
Decision making in modern stochastic systems, including e-commerce platforms, financial markets and healthcare systems, has evolved into a multifaceted process that combines information acquisition and adaptive information sources. This paper initiates a study on such integrated settings, where these elements are not only fundamental but, also, interact in a complex and stochastically intertwined manner. We introduce a relatively simple model, which, however, captures the involved novel elements. A decision maker (DM) may choose between an established product $A$ of known value and a new product $B$ whose value is unknown. In parallel, the DM observes signals about the unknown value of product $B$ and can, also, opt to exchange it for product $A$ if $B$ is initially chosen. Mathematically, the model gives rise to sequential optimal stopping problems with distinct informational regimes (before and after buying product $B$), differentiated by the initial, coarser signal and the subsequent, more accurate one. We analyze in detail the underlying problems using predominantly viscosity solution techniques, departing from the existing literature on information acquisition which is based on traditional optimal stopping arguments. More broadly, the modeling approach introduced herein offers a novel framework for developing more complex interactions among decisions, information sources and information costs in stochastic environments, through a sequence of nested obstacle problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
