risk prioritization

This study addresses the challenge of optimizing resource allocation for multi-hazard risk mitigation in U.S. homeland security and emergency management. We propose an integer linear programming (ILP) decision-support model that integrates probabilistic risk assessment (PRA) with multi-criteria consequence quantification. Methodologically, the model innovatively fuses heterogeneous historical data and publicly available information to enable joint modeling across 16 hazard types and six consequence dimensions, while selecting optimal mitigation projects under budget constraints. It further introduces a sensitivity-driven framework that jointly optimizes robustness and cost-effectiveness. Applied empirically in Iowa, the model generates a high-value portfolio of 52 mitigation projects, achieving an average 37% reduction in expected risk. Multi-scenario sensitivity analysis confirms solution robustness. The approach provides a scalable, methodologically rigorous foundation for evidence-based resilience investment.

This paper addresses the absence of non-probabilistic, non-heuristic risk decision frameworks under extreme uncertainty—such as “unknown unknowns” and severe resource constraints. Methodologically, it introduces the RDOT classification paradigm, a structured taxonomy that categorizes cross-disciplinary risk strategies into six types: structural, responsive, formal, adversarial, multi-stage, and proactive. It systematically identifies over 110 domain-agnostic strategies, transcending the traditional dichotomy between probabilistic modeling and cognitive heuristics, and bridges theoretical gaps in robust design and emergency planning. The framework integrates multi-objective optimization, multi-attribute utility theory, and structured workflow modeling—requiring neither probability estimation nor predictive modeling. Empirically validated in engineering and public policy contexts, RDOT demonstrates robustness and embeddability, delivering the first lightweight, actionable, cross-domain risk decision toolkit. (149 words)

Current AI risk mitigation frameworks suffer from fragmentation, terminological ambiguity, and coverage gaps, hindering coordinated multistakeholder governance. To address this, we introduce the first cross-framework taxonomy for AI risk mitigation, systematically synthesizing 831 mitigation measures from 13 prominent frameworks published between 2023 and 2025. Our methodology combines rapid evidence scanning, iterative clustering-based coding, and structured knowledge modeling to develop a four-dimensional classification—governance & oversight, technical safety, operational processes, and transparency & accountability—with 23 granular subcategories. We explicitly resolve semantic inconsistencies in key terms (e.g., “red-teaming,” “risk management”) and deliver a scalable, role-aligned taxonomy alongside a dynamic, open-source database. The resulting resource enables comparative framework analysis and gap identification, supporting national policymaking and AI safety organizations worldwide. All artifacts are publicly released to advance global AI governance infrastructure.

Traditional risk scores estimate the probability of adverse outcomes under no intervention, offering limited support for high-stakes decisions that require balancing multiple potential outcomes. This work proposes a “triage score” that explicitly incorporates counterfactual outcomes under different interventions into the decision framework through additive counterfactual utility modeling, with conventional risk scores emerging as a special case. By integrating counterfactual reasoning, additive utility models, and randomized controlled trial data, this approach is the first to embed counterfactual utilities directly into a scoring system. Applied to pretrial risk assessment, it effectively captures complex utility structures and yields policy evaluation and learning conclusions that markedly diverge from those of traditional methods, thereby overcoming the limitations inherent in single-outcome prediction.

This study addresses task sequencing, resource allocation, and multi-constraint optimization during project planning, aiming to jointly minimize makespan and cost. We propose a sequential concession-based iterative task hierarchical decomposition method incorporating an “ideal point” concept to enable decision-makers to dynamically trade off time–cost preferences. We introduce the first modeling framework for synchronous task execution, integrated with Boolean programming to compute minimum-cost feasible schedules. Furthermore, we develop a hybrid qualitative–quantitative multi-objective decision support model that rigorously derives computable lower bounds for both makespan and cost, and generates tunable Pareto-optimal management plans. The model has been validated across educational, research, and industrial production scenarios, demonstrating significant improvements in plan robustness and execution efficiency.

This study addresses the challenge of selecting optimal sequential intervention strategies under resource budget constraints, balancing cost-overrun risk control with intervention efficacy. The authors propose a prediction–optimization framework that first estimates the joint distribution of intervention outcomes and costs from observational data, then employs chance-constrained optimization to select, from a finite set of candidate policies, those satisfying a prespecified upper bound on the probability of budget overruns. This approach is the first to identify cost tail risk directly from data and integrate it into chance-constrained optimization, supports arbitrary plug-in predictors, and constructs a safety–utility frontier to explicitly trade off budget compliance against performance loss. Across five experiments spanning clinical treatment and equipment maintenance, the method effectively controls budget violations—unlike conventional mean-constrained approaches, which frequently exceed limits—and precisely quantifies efficacy loss via the frontier curve.

This study addresses the multidimensional risks—operational, security, and governance-related—that enterprises face when deploying large language models, noting that existing open-source tools are fragmented and fail to comprehensively cover authoritative risk taxonomies. To bridge this gap, the work proposes a structured mapping protocol that automatically aligns the capabilities of 21 prominent open-source tools with the 32 subcategories of the MIT AI Risk Framework, leveraging retrieval-augmented generation (RAG) and LLM-based parsing. The protocol’s validity is substantiated through source code and documentation analysis, majority voting, and inter-rater reliability assessment using Fleiss’ Kappa (κ = 0.509, F1 = 75.5%). Findings reveal a pronounced overconcentration of current tools on technical controls, with significant gaps in governance, legal, and market risk domains, thereby providing an empirical foundation for developing layered AI risk mitigation architectures.