Pure Characteristics Demand Models and Distributionally Robust Mathematical Programs with Stochastic Complementarity Constraints

TL;DR

This paper develops a distributionally robust optimization framework for pure characteristics demand models with stochastic complementarity constraints, providing convergence guarantees and preliminary numerical validation.

Contribution

It introduces a novel approximation method for solving distributionally robust demand models with stochastic complementarity constraints, with proven convergence properties.

Findings

The approximation converges to the original problem as regularization diminishes.

Numerical results demonstrate the method's effectiveness and efficiency.

The approach handles uncertainties in probability distributions robustly.

Abstract

We formulate pure characteristics demand models under uncertainties of probability distributions as distributionally robust mathematical programs with stochastic complementarity constraints (DRMP-SCC). For any fixed first-stage variable and a random realization, the second-stage problem of DRMP-SCC is a monotone linear complementarity problem (LCP). To deal with uncertainties of probability distributions of the involved random variables in the stochastic LCP, we use the distributionally robust approach. Moreover, we propose an approximation problem with regularization and discretization to solve DRMP-SCC, which is a two-stage nonconvex-nonconcave minimax optimization problem. We prove the convergence of the approximation problem to DRMP-SCC regarding the optimal solution sets, optimal values and stationary points as the regularization parameter goes to zero and the sample size goes to infinity. Finally, preliminary numerical results for investigating distributional robustness of pure characteristics demand models are reported to illustrate the effectiveness and efficiency of our approaches.