Econ Papers Daily / 期刊论文
利用数据改进稳健决策
Improving robust decisions with data
摘要
A decision‐maker faces uncertainty governed by a data‐generating process (DGP), which is only known to belong to a set of sequences of independent but possibly non‐identical distributions. A robust decision maximizes the expected payoff against the worst possible DGP in this set. This paper characterizes when and how such robust decisions can be objectively improved with data; that is, can yield higher expected payoffs under the true DGP regardless of which DGP is the truth. It further develops simple and novel inference procedures that achieve such improvement, while common methods (e.g., maximum likelihood) may fail to do so.
中文摘要
决策者面临由数据生成过程（DGP）主导的不确定性，该过程仅已知属于一组独立但可能非同分布序列的集合。稳健决策旨在最大化该集合中最差可能DGP下的期望收益。本文刻画了此类稳健决策何时以及如何能够通过数据实现客观改进——即无论真实DGP为何，均能在真实DGP下获得更高期望收益。进一步地，本文提出了实现此类改进的简洁新颖推断方法，而常见方法（如极大似然估计）可能无法达成这一目标。