Determinant Maximization with Linear Matrix Inequality Constraints
SIAM Journal on Matrix Analysis and Applications
Mathematics of Operations Research
Correlations and Copulas for Decision and Risk Analysis
Management Science
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
Lectures on modern convex optimization: analysis, algorithms, and engineering applications
Resampling methods for input modeling
Proceedings of the 33nd conference on Winter simulation
On the Rate of Convergence of Optimal Solutions of Monte Carlo Approximations of Stochastic Programs
SIAM Journal on Optimization
Feature Article: Optimization for simulation: Theory vs. Practice
INFORMS Journal on Computing
Robust portfolio selection problems
Mathematics of Operations Research
Centralized and Competitive Inventory Models with Demand Substitution
Operations Research
Operations Research
Convex Optimization
Input modeling: input model uncertainty: why do we care and what should we do about it?
Proceedings of the 35th conference on Winter simulation: driving innovation
A brief introduction to optimization via simulation
Winter Simulation Conference
Solving Log-Determinant Optimization Problems by a Newton-CG Primal Proximal Point Algorithm
SIAM Journal on Optimization
Theory and Applications of Robust Optimization
SIAM Review
Robust simulatoin of environmental policies using the dice model
Proceedings of the Winter Simulation Conference
Hi-index | 0.01 |
Integrated assessment models that combine geophysics and economics features are often used to evaluate and compare global warming policies. Because there are typically profound uncertainties in these models, a simulation approach is often used. This approach requires the distribution of the uncertain parameters clearly specified. However, this is typically impossible because there is often a significant amount of ambiguity (e.g., estimation error) in specifying the distribution. In this paper, we adopt the widely used multivariate normal distribution to model the uncertain parameters. However, we assume that the mean vector and covariance matrix of the distribution are within some ambiguity sets. We then show how to find the worst-case performance of a given policy for all distributions constrained by the ambiguity sets. This worst-case performance provides a robust evaluation of the policy. We test our algorithm on a famous integrated model of climate change, known as the Dynamic Integrated Model of Climate and the Economy (DICE model). We find that the DICE model is sensitive to the means and covariance of the parameters. Furthermore, we find that, based on the DICE model, moderately tight environmental policies robustly outperform the no controls policy and the famous aggressive policies proposed by Stern and Gore. This paper was accepted by Dimitris Bertsimas, optimization.