论文标题
有不确定后果的决定 - 损失分配的总订单
Decisions with Uncertain Consequences -- A Total Ordering on Loss-Distributions
论文作者
论文摘要
决策通常基于不精确，不确定或模糊的信息。同样，行动的后果通常同样不可预测，从而使决策者陷入了双重危险。假设动作的效果可以通过随机变量进行建模，那么决策问题归结为通过比较其分布函数来比较不同的效果（随机变量）。尽管无法订购概率分布的整个空间，但是可以完全有意义地订购了适当限制的分布子集。我们称这些损失分配为决策理论中损失功能的概念提供了替代。本文介绍了必要限制和特此对随机损失变量的可构造总顺序的理论，这可以在后果的不确定性下进行决策。使用从模拟获得的数据，我们证明了方法的实际适用性。
Decisions are often based on imprecise, uncertain or vague information. Likewise, the consequences of an action are often equally unpredictable, thus putting the decision maker into a twofold jeopardy. Assuming that the effects of an action can be modeled by a random variable, then the decision problem boils down to comparing different effects (random variables) by comparing their distribution functions. Although the full space of probability distributions cannot be ordered, a properly restricted subset of distributions can be totally ordered in a practically meaningful way. We call these loss-distributions, since they provide a substitute for the concept of loss-functions in decision theory. This article introduces the theory behind the necessary restrictions and the hereby constructible total ordering on random loss variables, which enables decisions under uncertainty of consequences. Using data obtained from simulations, we demonstrate the practical applicability of our approach.
