Monotone Comparative Statics under Uncertainty
分析了多类随机优化问题中单调比较静态预测的充要条件，基于原始函数（效用函数和概率分布）的性质，适用于投资组合和拍卖等应用场景。
This paper analyzes monotone comparative statics predictions in several classes of stochastic optimization problems. The main results characterize necessary and sufficient conditions for comparative statics predictions to hold based on properties of primitive functions, that is, utility functions and probability distributions. The results apply when the primitives satisfy one of the following two properties: (i) a single-crossing property, which arises in applications such as portfolio investment problems and auctions, or (ii) log-supermodularity, which arises in the analysis of demand functions, affiliated random variables, stochastic orders, and orders over risk aversion.