熵决策
Entropic Decision Making
摘要：
利用神经生物学的结果，感性决策和价值为基础的决策，彩票之间的决策问题被重新制定在一个抽象的空间，不确定的前景映射到相应的积极的神经元表示。这种映射允许我们在新的空间中最大化非广延熵，并带有一些约束，而不是效用函数。为了实现与行为数据的良好协议，约束必须至少包括对刺激的加权平均值及其方差的约束。这两种限制都得到了神经元响应对外部刺激的适应性的支持。通过与热力学和信息引擎的类比，我们讨论了两种彩票之间的选择动力学，因为它们在大脑中同时被处理，通过速率方程描述了彩票之间的注意力转移和每张彩票的各种前景。该模型能够提供新的见解风险规避和行为异常不占前景理论。
英文摘要：
Using results from neurobiology on perceptual decision making and value-based decision making, the problem of decision making between lotteries is reformulated in an abstract space where uncertain prospects are mapped to corresponding active neuronal representations. This mapping allows us to maximize non-extensive entropy in the new space with some constraints instead of a utility function. To achieve good agreements with behavioral data, the constraints must include at least constraints on the weighted average of the stimulus and on its variance. Both constraints are supported by the adaptability of neuronal responses to an external stimulus. By analogy with thermodynamic and information engines, we discuss the dynamics of choice between two lotteries as they are being processed simultaneously in the brain by rate equations that describe the transfer of attention between lotteries and within the various prospects of each lottery. This model is able to give new insights on risk aversion and on behavioral anomalies not accounted for by Prospect Theory.
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。
