Improving robust decisions with data
Abstract
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.
Fields of Study & Subjects
Advanced Statistical Process Monitoring
Publication details
- Journal / source
- Theoretical Economics
- Volume
- 21
- Issue
- 3
- Pages
- 888–927
- Publication date
- 1 January 2026
- Language
- EN