A SYSTEMATIC REVIEW OF ROBUST AND ADAPTIVE DECISION MODELS IN COMPLEX AND UNCERTAIN ENVIRONMENTS

Abstract

This paper reviews robust and adaptive decision models for complex environments characterized by risk, ambiguity and deep uncertainty. It contrasts three regimes of uncertainty and shows how traditional “expected value” optimization becomes fragile under distribution shifts, adversarial behavior, and model misspecification. The review covers stochastic programming with recourse and risk measures such as chance constraints and conditional value-at-risk, which explicitly manage tail events.

It then examines robust optimization and distributionally robust optimization, highlighting how uncertainty sets and ambiguity sets protect against parameter and distributional misspecification. Sequential settings are addressed through robust and risk-sensitive Markov decision processes, which embed worst-case and tail-risk considerations into dynamic control. Decision-focused learning is presented as an approach to align machine learning with downstream optimization by differentiating through solvers and, where necessary, hardening models via adversarial and distributionally robust optimization-inspired training.

The paper emphasizes that mathematical robustness alone is insufficient: effective deployment requires human–AI teaming, layered socio-technical architectures, explainability, and autonomy-preserving governance. A multi-dimensional evaluation framework integrating algorithmic, human, stakeholder, learning, and implementation metrics is outlined. The paper concludes with open challenges in explainable uncertainty, robustness–adaptivity trade-offs, scalability to large combinatorial problems, multi-agent robustness, and federated, privacy-preserving decision support.