论文标题
熵决策
Entropic Decision Making
论文作者
论文摘要
利用神经生物学的结果在感知决策和基于价值的决策中，彩票之间的决策问题是在一个抽象空间中重新制定的，其中不确定的前景映射到相应的主动神经元表示。此映射使我们能够在新空间中最大程度地利用一些限制，而不是实用程序功能。为了与行为数据达成良好的协议，约束必须至少包括刺激的加权平均值及其差异的约束。神经元反应对外部刺激的适应性支持这两种约束。通过类似于热力学和信息引擎，我们通过描述彩票之间和每个彩票各个前景中注意力转移的速率方程式在大脑中同时处理两个彩票之间的选择动态。该模型能够提供有关风险规避和前景理论未考虑的行为异常的新见解。
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.
