Risk-averse Decision Making with Contextual Information: Model, Sample Average Approximation, and Kernelization

TL;DR

This paper studies risk-averse decision making under uncertainty with a focus on model equivalence, computational methods, and kernelization, providing theoretical insights and practical algorithms validated through numerical tests.

Contribution

It establishes conditions for the equivalence of nested and joint risk minimization, proposes a computational approach using one-stage risk minimization, and analyzes kernelization for consistency.

Findings

Optimal policies can be independent of the risk measure under certain conditions.

The proposed method simplifies risk-averse optimization by reducing it to a one-stage problem.

Numerical tests validate the theoretical results in newsvendor and portfolio problems.

Abstract

We consider risk-averse contextual optimization problems where the decision maker (DM) faces two types of uncertainties: problem data uncertainty (PDU) and contextual uncertainty (CU) associated with PDU, the DM makes an optimal decision by minimizing the risk arising from PDU based on the present observation of CU and then assesses the risk of the optimal policy against the CU. A natural question arises as to whether the nested risk minimization/assessment process is equivalent to joint risk minimization/assessment against CU and PDU simultaneously. First, we demonstrate that the equivalence can be established by appropriate choices of the risk measures and give counter examples where such equivalence may fail. One of the interesting findings is that the optimal policies are independent of the choice of the risk measure against the CU under certain conditions. Second, by using the equivalence, we propose computational method for solving the risk-averse contextual optimization problem by solving a one-stage risk minimization problem. The latter is particularly helpful in data-driven environments. We consider a number of risk measures/metrics to characterize the DM's risk preference for PDU and discuss the computational tractability for the resulting risk-averse contextual optimization problem. Third, when the risk-averse contextual optimization problem is defined in the reproducing kernel Hilbert space, we show consistency of the optimal values obtained from solving sample average approximation problems. Some numerical tests, in newsvendor problem and portfolio selection problem, are performed to validate the theoretical results.