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An optimal sequential information acquisition model subject to a heuristic assimilation constraint

机译:启发式同化约束的最优顺序信息获取模型

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Purpose - The purpose of this paper is to study the optimal sequential information acquisition process of a rational decision maker (DM) when allowed to acquire n pieces of information from a set of bi-dimensional products whose characteristics vary in a continuum set. Design/methodology/approach - The authors incorporate a heuristic mechanism that makes the n-observation scenario faced by a DM tractable. This heuristic allows the DM to assimilate substantial amounts of information and define an acquisition strategy within a coherent analytical framework. Numerical simulations are introduced to illustrate the main results obtained. Findings - The information acquisition behavior modeled in this paper corresponds to that of a perfectly rational DM, i.e. endowed with complete and transitive preferences, whose objective is to choose optimally among the products available subject to a heuristic assimilation constraint. The current paper opens the way for additional research on heuristic information acquisition and choice processes when considered from a satisficing perspective that accounts for cognitive limits in the information processing capacities of DMs. Originality/value - The proposed information acquisition algorithm does not allow for the use of standard dynamic programming techniques. That is, after each observation is gathered, a rational DM must modify his information acquisition strategy and recalculate his or her expected payoffs in terms of the observations already acquired and the information still to be gathered.
机译:目的-本文的目的是研究理性决策者(DM)的最佳顺序信息获取过程,该过程允许从一组特征连续地变化的二维产品中获取n条信息。设计/方法/方法-作者采用启发式机制,使DM面临的n观测场景变得易于处理。这种启发式方法使DM可以吸收大量信息,并在一致的分析框架内定义获取策略。引入数值模拟来说明获得的主要结果。发现-本文中建模的信息获取行为与完全理性的DM相对应,即具有完全和可传递的偏好,其目标是在受到启发式同化约束的情况下从可用产品中进行最佳选择。当从令人满意的角度考虑DM信息处理能力中的认知限制时,本论文为启发式信息获取和选择过程的其他研究开辟了道路。原创性/价值-提出的信息获取算法不允许使用标准动态编程技术。也就是说,在收集每个观察结果之后,理性的决策者必须修改其信息获取策略,并根据已经获得的观察结果和仍要收集的信息重新计算其预期收益。

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