The field of resource allocation and strategic decision making is witnessing significant developments, driven by the need to handle uncertainty and adversarial attacks. Researchers are exploring innovative approaches to capacity planning, resource allocation, and scheduling, with a focus on developing robust and efficient methods that can adapt to changing conditions. A key direction in this field is the integration of stochastic optimization techniques, such as sample average approximation and chance-constrained programming, to handle uncertainty in resource usage and preference reporting. Another important area of research is the development of defense mechanisms against adversarial attacks, including the use of game-theoretic frameworks and quickest change detection methods. These advances have the potential to improve the efficiency and resilience of resource allocation systems in various domains, including cloud computing, job scheduling, and strategic games. Noteworthy papers in this area include: Capacity Planning in Stable Matching with Truthful or Strategic Preference Uncertainty, which introduces a two-stage stochastic matching problem to handle uncertainty in preference reporting. Efficient Resource Allocation under Adversary Attacks: A Decomposition-Based Approach, which proposes a decomposition-based solution to allocate resources in a network under adversarial attacks. Quickest Detection of Adversarial Attacks Against Correlated Equilibria, which derives the maxmin strategies for detecting adversarial attacks in strategic games. Capacity Planning and Scheduling for Jobs with Uncertainty in Resource Usage and Duration, which proposes approximate approaches using deterministic estimators and pair sampling-based constraint programming to handle uncertainty in job scheduling.