多项logit过程和偏好发现：黑箱内外
Multinomial logit processes and preference discovery: inside and outside the black box
摘要：
我们在广泛使用的softmax表示\[ p_{t}\left( a,A\right) =\dfrac{e^{\frac{u\left( a\right) }{λ\left( t\right) }+α\left( a\right) }}{\sum_{b\in A}e^{\frac{u\left( b\right) }{λ\left( t\right) }+α\left( b\right) }}% \]中提供了选择概率对最后期限的依赖性的两个表征，一个是公理化的，另一个是神经计算的，其中$p_{t}\left( a,A\right) $是备选方案$a$从可行备选方案的集合$A$中被选择的概率，如果$t$是可用于决定的时间，$λ$是测量信息的单位成本的时间相关噪声参数，$u$是一个与时间无关的效用函数，而$α$是一个选择特定的偏差，它决定了反映先验信息和记忆锚定的初始选择概率。我们的公理分析提供了softmax的行为基础（当$α$为常数时，也称为多项式Logit模型）。我们的神经计算推导提供了一种生物启发算法，可以解释softmax在选择行为中的出现。联合起来，这两种方法提供了一个彻底的了解软最大化的内部原因（神经生理机制）和外部影响（可测试的影响）。
英文摘要：
We provide two characterizations, one axiomatic and the other neuro-computational, of the dependence of choice probabilities on deadlines, within the widely used softmax representation \[ p_{t}\left( a,A\right) =\dfrac{e^{\frac{u\left( a\right) }{λ\left( t\right) }+α\left( a\right) }}{\sum_{b\in A}e^{\frac{u\left( b\right) }{λ\left( t\right) }+α\left( b\right) }}% \] where $p_{t}\left( a,A\right) $ is the probability that alternative $a$ is selected from the set $A$ of feasible alternatives if $t$ is the time available to decide, $λ$ is a time dependent noise parameter measuring the unit cost of information, $u$ is a time independent utility function, and $α$ is an alternative-specific bias that determines the initial choice probabilities reflecting prior information and memory anchoring. Our axiomatic analysis provides a behavioral foundation of softmax (also known as Multinomial Logit Model when $α$ is constant). Our neuro-computational derivation provides a biologically inspired algorithm that may explain the emergence of softmax in choice behavior. Jointly, the two approaches provide a thorough understanding of soft-maximization in terms of internal causes (neurophysiological mechanisms) and external effects (testable implications).
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。
