• Corpus ID: 233394179

Robust decision-making under risk and ambiguity

  title={Robust decision-making under risk and ambiguity},
  author={Maximilian Blesch and Philipp Eisenhauer},
Economists often estimate a subset of their model parameters outside the model and let the decision-makers inside the model treat these point estimates as-if they are correct. This practice ignores model ambiguity, opens the door for misspecification of the decision problem, and leads to post-decision disappointment. We develop a framework to explore, evaluate, and optimize decision rules that explicitly account for the uncertainty in the first step estimation using statistical decision theory… 

Structural Models for Policy-Making: Coping with Parametric Uncertainty

The ex-ante evaluation of policies using structural econometric models is based on estimated parameters as a stand-in for the true parameters. This practice ignores uncertainty in the counterfactual



Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher

This paper formulates a simple, regenerative, optimal-stopping model of bus-eng ine replacement to describe the behavior of Harold Zurcher, superinte ndent of maintenance at the Madison (Wisconsin)

Foundations Of Statistics

Robust Dynamic Programming

  • G. Iyengar
  • Mathematics, Economics
    Math. Oper. Res.
  • 2005
It is proved that when this set of measures has a certain "rectangularity" property, all of the main results for finite and infinite horizon DP extend to natural robust counterparts.

Robust Solutions of Optimization Problems Affected by Uncertain Probabilities

The robust counterpart of a linear optimization problem with φ-divergence uncertainty is tractable for most of the choices of φ typically considered in the literature and extended to problems that are nonlinear in the optimization variables.

Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald

This paper proposes statistical decision theory as a framework for evaluation of the performance of models in decision making, and considers the common practice of as-if optimization: specification of a model, point estimation of its parameters, and use of the point estimate to make a decision that would be optimal if the estimate were accurate.


Maximum likelihood estimation of discrete control processes

Consider the following “inverse stochastic control” problem. A statistician observes a realization of a controlled stochastic process $\{ d_t ,x_t \} $ consisting of the sequence of states $x_t$, and

Statistical Decision Theory and Bayesian Analysis

An overview of statistical decision theory, which emphasizes the use and application of the philosophical ideas and mathematical structure of decision theory. The text assumes a knowledge of basic

Theory Of Decision Under Uncertainty

This book describes the classical axiomatic theories of decision under uncertainty, as well as critiques thereof and alternative theories. It focuses on the meaning of probability, discussing some

Data-driven robust optimization

This work proposes a novel schema for utilizing data to design uncertainty sets for robust optimization using statistical hypothesis tests, and shows that data-driven sets significantly outperform traditional robust optimization techniques whenever data is available.