Use and Misuse of MCDA to Support Decision Making Informed by Risk.

Recent guidelines for risk-informed decision making (RIDM) provide a gold-standard for how to incorporate probabilistic risk models in conjunction with other considerations in a decision process. Nevertheless, risk quantification using probabilistic and statistical methods is difficult in situations where threat, vulnerability, and consequences are highly uncertain and risk quantification. In such situations a wider variety of methods could be employed, which we call decision making informed by risk (DMIR) combining risk and decision analytics. Risk informed decision making (RIDM) can be considered as a special case of DMIR. Multi criteria decision analysis (MCDA) often serves as a basis for DMIR in order to flexibly accommodate different levels of analytical detail. DMIR often involves artful use of proxy variables that correlate with, and are more measurable than, underlying factors of interest. This article introduces the notion of DMIR and discusses the use of MCDA in its application in the context of risk-based problems. MCDA-based risk analyses identify metrics associated with threats of concern and system vulnerabilities, characterize the way in which alternative actions can affect these threats and vulnerabilities, and ultimately synthesize this information to compare, prioritize, or select alternative mitigation strategies. Simple linear additive MCDA models often integrate these inputs, but the same simplicity can limit such approaches and create pitfalls and more advanced models including multiplicative relationships can be warranted. This essay qualitatively explores the critical practitioner questions of how and when the use of linear multicriteria models creates significant problems, and how to avoid them.

The Need to Reconcile Concepts that Characterize Systems Facing Threats.

Desirable system performance in the face of threats has been characterized by various management concepts. Through semistructured interviews with editors of journals in the fields of emergency response and systems management, a literature review, and professional judgment, we identified nine related and often interchangeably used system performance concepts: adaptability, agility, reliability, resilience, resistance, robustness, safety, security, and sustainability. A better understanding of these concepts will allow system planners to pursue management strategies best suited to their unique system dynamics and specific objectives of good performance. We analyze expert responses and review the linguistic definitions and mathematical framing of these concepts to understand their applications. We find a lack of consensus on their usage between interview subjects, but by using the mathematical framing to enrich the linguistic definitions, we formulate comparative visualizations and propose distinct definitions for the nine concepts. We present a conceptual framing to relate the concepts for management purposes.

Multicriteria Decision Framework for Cybersecurity Risk Assessment and Management.

Risk assessors and managers face many difficult challenges related to novel cyber systems. Among these challenges are the constantly changing nature of cyber systems caused by technical advances, their distribution across the physical, information, and sociocognitive domains, and the complex network structures often including thousands of nodes. Here, we review probabilistic and risk-based decision-making techniques applied to cyber systems and conclude that existing approaches typically do not address all components of the risk assessment triplet (threat, vulnerability, consequence) and lack the ability to integrate across multiple domains of cyber systems to provide guidance for enhancing cybersecurity. We present a decision-analysis-based approach that quantifies threat, vulnerability, and consequences through a set of criteria designed to assess the overall utility of cybersecurity management alternatives. The proposed framework bridges the gap between risk assessment and risk management, allowing an analyst to ensure a structured and transparent process of selecting risk management alternatives. The use of this technique is illustrated for a hypothetical, but realistic, case study exemplifying the process of evaluating and ranking five cybersecurity enhancement strategies. The approach presented does not necessarily eliminate biases and subjectivity necessary for selecting countermeasures, but provides justifiable methods for selecting risk management actions consistent with stakeholder and decisionmaker values and technical data.

Resilience of Cyber Systems with Over- and Underregulation.

Recent cyber attacks provide evidence of increased threats to our critical systems and infrastructure. A common reaction to a new threat is to harden the system by adding new rules and regulations. As federal and state governments request new procedures to follow, each of their organizations implements their own cyber defense strategies. This unintentionally increases time and effort that employees spend on training and policy implementation and decreases the time and latitude to perform critical job functions, thus raising overall levels of stress. People's performance under stress, coupled with an overabundance of information, results in even more vulnerabilities for adversaries to exploit. In this article, we embed a simple regulatory model that accounts for cybersecurity human factors and an organization's regulatory environment in a model of a corporate cyber network under attack. The resulting model demonstrates the effect of under- and overregulation on an organization's resilience with respect to insider threats. Currently, there is a tendency to use ad-hoc approaches to account for human factors rather than to incorporate them into cyber resilience modeling. It is clear that using a systematic approach utilizing behavioral science, which already exists in cyber resilience assessment, would provide a more holistic view for decisionmakers.

Emerging Technologies for Environmental Remediation: Integrating Data and Judgment.

Emerging technologies present significant challenges to researchers, decision-makers, industry professionals, and other stakeholder groups due to the lack of quantitative risk, benefit, and cost data associated with their use. Multi-criteria decision analysis (MCDA) can support early decisions for emerging technologies when data is too sparse or uncertain for traditional risk assessment. It does this by integrating expert judgment with available quantitative and qualitative inputs across multiple criteria to provide relative technology scores. Here, an MCDA framework provides preliminary insights on the suitability of emerging technologies for environmental remediation by comparing nanotechnology and synthetic biology to conventional remediation methods. Subject matter experts provided judgments regarding the importance of criteria used in the evaluations and scored the technologies with respect to those criteria. The results indicate that synthetic biology may be preferred over nanotechnology and conventional methods for high expected benefits and low deployment costs but that conventional technology may be preferred over emerging technologies for reduced risks and development costs. In the absence of field data regarding the risks, benefits, and costs of emerging technologies, structuring evidence-based expert judgment through a weighted hierarchy of topical questions may be helpful to inform preliminary risk governance and guide emerging technology development and policy.

The worsening divergence of biotechnology: the importance of risk culture.

In the last 20 years, the field of biotechnology has made significant progress and attracted substantial investments, leading to different paths of technological modernization among nations. As a result, there is now an international divide in the commercial and intellectual capabilities of biotechnology, and the implications of this divergence are not well understood. This raises important questions about why global actors are motivated to participate in biotechnology modernization, the challenges they face in achieving their goals, and the possible future direction of global biotechnology development. Using the framework of prospect theory, this paper explores the role of risk culture as a fundamental factor contributing to this divergence. It aims to assess the risks and benefits associated with the early adoption of biotechnology and the regulatory frameworks that shape the development and acceptance of biotechnological innovations. By doing so, it provides valuable insights into the future of biotechnology development and its potential impact on the global landscape.

Flood protection diversification to reduce probabilities of extreme losses.

Recent catastrophic losses because of floods require developing resilient approaches to flood risk protection. This article assesses how diversification of a system of coastal protections might decrease the probabilities of extreme flood losses. The study compares the performance of portfolios each consisting of four types of flood protection assets in a large region of dike rings. A parametric analysis suggests conditions in which diversifications of the types of included flood protection assets decrease extreme flood losses. Increased return periods of extreme losses are associated with portfolios where the asset types have low correlations of economic risk. The effort highlights the importance of understanding correlations across asset types in planning for large-scale flood protection. It allows explicit integration of climate change scenarios in developing flood mitigation strategy.

Integrate life-cycle assessment and risk analysis results, not methods.

Two analytic perspectives on environmental assessment dominate environmental policy and decision-making: risk analysis (RA) and life-cycle assessment (LCA). RA focuses on management of a toxicological hazard in a specific exposure scenario, while LCA seeks a holistic estimation of impacts of thousands of substances across multiple media, including non-toxicological and non-chemically deleterious effects. While recommendations to integrate the two approaches have remained a consistent feature of environmental scholarship for at least 15 years, the current perception is that progress is slow largely because of practical obstacles, such as a lack of data, rather than insurmountable theoretical difficulties. Nonetheless, the emergence of nanotechnology presents a serious challenge to both perspectives. Because the pace of nanomaterial innovation far outstrips acquisition of environmentally relevant data, it is now clear that a further integration of RA and LCA based on dataset completion will remain futile. In fact, the two approaches are suited for different purposes and answer different questions. A more pragmatic approach to providing better guidance to decision-makers is to apply the two methods in parallel, integrating only after obtaining separate results.

Resilience and efficiency in transportation networks.

Urban transportation systems are vulnerable to congestion, accidents, weather, special events, and other costly delays. Whereas typical policy responses prioritize reduction of delays under normal conditions to improve the efficiency of urban road systems, analytic support for investments that improve resilience (defined as system recovery from additional disruptions) is still scarce. In this effort, we represent paved roads as a transportation network by mapping intersections to nodes and road segments between the intersections to links. We built road networks for 40 of the urban areas defined by the U.S. Census Bureau. We developed and calibrated a model to evaluate traffic delays using link loads. The loads may be regarded as traffic-based centrality measures, estimating the number of individuals using corresponding road segments. Efficiency was estimated as the average annual delay per peak-period auto commuter, and modeled results were found to be close to observed data, with the notable exception of New York City. Resilience was estimated as the change in efficiency resulting from roadway disruptions and was found to vary between cities, with increased delays due to a 5% random loss of road linkages ranging from 9.5% in Los Angeles to 56.0% in San Francisco. The results demonstrate that many urban road systems that operate inefficiently under normal conditions are nevertheless resilient to disruption, whereas some more efficient cities are more fragile. The implication is that resilience, not just efficiency, should be considered explicitly in roadway project selection and justify investment opportunities related to disaster and other disruptions.

Balancing research and funding using value of information and portfolio tools for nanomaterial risk classification.

Risk research for nanomaterials is currently prioritized by means of expert workshops and other deliberative processes. However, analytical techniques that quantify and compare alternative research investments are increasingly recommended. Here, we apply value of information and portfolio decision analysis-methods commonly applied in financial and operations management-to prioritize risk research for multiwalled carbon nanotubes and nanoparticulate silver and titanium dioxide. We modify the widely accepted CB Nanotool hazard evaluation framework, which combines nano- and bulk-material properties into a hazard score, to operate probabilistically with uncertain inputs. Literature is reviewed to develop uncertain estimates for each input parameter, and a Monte Carlo simulation is applied to assess how different research strategies can improve hazard classification. The relative cost of each research experiment is elicited from experts, which enables identification of efficient research portfolios-combinations of experiments that lead to the greatest improvement in hazard classification at the lowest cost. Nanoparticle shape, diameter, solubility and surface reactivity were most frequently identified within efficient portfolios in our results.

Operational resilience: concepts, design and analysis.

Building resilience into today's complex infrastructures is critical to the daily functioning of society and its ability to withstand and recover from natural disasters, epidemics, and cyber-threats. This study proposes quantitative measures that capture and implement the definition of engineering resilience advanced by the National Academy of Sciences. The approach is applicable across physical, information, and social domains. It evaluates the critical functionality, defined as a performance function of time set by the stakeholders. Critical functionality is a source of valuable information, such as the integrated system resilience over a time interval, and its robustness. The paper demonstrates the formulation on two classes of models: 1) multi-level directed acyclic graphs, and 2) interdependent coupled networks. For both models synthetic case studies are used to explore trends. For the first class, the approach is also applied to the Linux operating system. Results indicate that desired resilience and robustness levels are achievable by trading off different design parameters, such as redundancy, node recovery time, and backup supply available. The nonlinear relationship between network parameters and resilience levels confirms the utility of the proposed approach, which is of benefit to analysts and designers of complex systems and networks.

How decision analysis can further nanoinformatics.

The increase in nanomaterial research has resulted in increased nanomaterial data. The next challenge is to meaningfully integrate and interpret these data for better and more efficient decisions. Due to the complex nature of nanomaterials, rapid changes in technology, and disunified testing and data publishing strategies, information regarding material properties is often illusive, uncertain, and/or of varying quality, which limits the ability of researchers and regulatory agencies to process and use the data. The vision of nanoinformatics is to address this problem by identifying the information necessary to support specific decisions (a top-down approach) and collecting and visualizing these relevant data (a bottom-up approach). Current nanoinformatics efforts, however, have yet to efficiently focus data acquisition efforts on the research most relevant for bridging specific nanomaterial data gaps. Collecting unnecessary data and visualizing irrelevant information are expensive activities that overwhelm decision makers. We propose that the decision analytic techniques of multicriteria decision analysis (MCDA), value of information (VOI), weight of evidence (WOE), and portfolio decision analysis (PDA) can bridge the gap from current data collection and visualization efforts to present information relevant to specific decision needs. Decision analytic and Bayesian models could be a natural extension of mechanistic and statistical models for nanoinformatics practitioners to master in solving complex nanotechnology challenges.

Enhanced adaptive management: integrating decision analysis, scenario analysis and environmental modeling for the Everglades.

We propose to enhance existing adaptive management efforts with a decision-analytical approach that can guide the initial selection of robust restoration alternative plans and inform the need to adjust these alternatives in the course of action based on continuously acquired monitoring information and changing stakeholder values. We demonstrate an application of enhanced adaptive management for a wetland restoration case study inspired by the Florida Everglades restoration effort. We find that alternatives designed to reconstruct the pre-drainage flow may have a positive ecological impact, but may also have high operational costs and only marginally contribute to meeting other objectives such as reduction of flooding. Enhanced adaptive management allows managers to guide investment in ecosystem modeling and monitoring efforts through scenario and value of information analyses to support optimal restoration strategies in the face of uncertain and changing information.

A decision-directed approach for prioritizing research into the impact of nanomaterials on the environment and human health.

The emergence of nanotechnology has coincided with an increased recognition of the need for new approaches to understand and manage the impact of emerging technologies on the environment and human health. Important elements in these new approaches include life-cycle thinking, public participation and adaptive management of the risks associated with emerging technologies and new materials. However, there is a clear need to develop a framework for linking research on the risks associated with nanotechnology to the decision-making needs of manufacturers, regulators, consumers and other stakeholder groups. Given the very high uncertainties associated with nanomaterials and their impact on the environment and human health, research resources should be directed towards creating the knowledge that is most meaningful to these groups. Here, we present a model (based on multi-criteria decision analysis and a value of information approach) for prioritizing research strategies in a way that is responsive to the recommendations of recent reports on the management of the risk and impact of nanomaterials on the environment and human health.

Additivity of information value in two-act linear loss decisions with normal priors.

Information about two uncertainties is superadditive in value if the value of information for resolving both uncertainties together exceeds the sum of the value of information for resolving each uncertainty alone. For the two-act linear loss decision problem with normal priors, conditions are derived for which the expected value of perfect information about two independent risks is superadditive. An approximate condition is also presented. The level of additivity of information value has implications for how risk-management resources should be allocated to acquisition of information. Several applications show how a variety of decision problems can reduce to the basic problem, and how the general results obtained here can be translated simply to prescriptions for specific situations.