Collective risk management: insights and opportunities for DoD decision-makers

Individuals make decisions every day in group contexts which vary in size, structure, and purpose. The US Department of Defense (DoD) is a large organization composed of many groups, and like many organizations, it has a vested interest in improving the performance of its affiliated groups, especially as it concerns risk-informed decision-making. This article discusses current foibles and considerations for decision-making in DoD groups as identified through a workshop with experts in risk-informed decision-making, cognitive science, and military operations. Experts noted that terms associated with risk-informed decision-making were often misconstrued, that formal decision-making frameworks are underutilized, and that many considerations should be taken into account when attempting to improve decision-making performance.     

Trends and applications of multi-criteria decision analysis in environmental sciences: literature review

Approximately 3000 papers concerning multi-criteria decision analysis (MCDA) in the environmental field were identified through a series of queries in the Web of Science database and classified by MCDA method and environmental application using text mining in R. Stemming and stop word removal techniques were used to remove irrelevant text from the literature. Trends in MCDA methods (AHP/ANP, TOPSIS, outranking, MAUT/MAVT) associated with specific environmental applications (water, air, energy, natural resources, and waste management) or interventions/tools applications (stakeholders, strategies, sustainability, and GIS) were identified. The results show a linear growth in the share of MCDA papers in environmental science across all application areas. Furthermore, the results show that AHP/ANP and MAUT/MAVT are the most frequently mentioned MCDA methods in the literature. For environmental applications, the results showed that natural resource and waste management keywords were, respectively, the most and least commonly discussed applications within the MCDA papers. For intervention/tool applications, we found that keywords associated with ‘strategy’ and ‘GIS’ applications are, respectively, the most and least commonly discussed keywords within the MCDA papers. The authors found that MCDA method keywords were evenly distributed across the environmental and intervention/tool applications, indicating a lack of preference in the environmental field for use of specific MCDA methods. This paper demonstrates that text mining is an applicable tool to assess specific textual trends and patterns when analyzing larger bodies of MCDA literature.     

Risk-based standards: integrating top–down and bottom–up approaches

In response to rapidly changing threats posed to increasingly complex socio-technical systems, many in the government and private sector have called for protection through risk-based standards. However, given the nature of these dynamic and uncertain threats, traditional risk assessment techniques may not be sufficient. Instead, there is a critical need for an integrated approach in which decision analytic techniques are used to assess evidence-based data with the values and preferences of decision makers. We point to three examples in the fields of nuclear power regulation, nanotechnology, and cybersecurity, where risk-based approaches (bottom–up) have been combined with decision analysis (top–down) to guide decision makers toward risk management policies that manifest both the best available evidence and the plurality of values within a society.     

Risk and resilience lessons from Venice

In the history of disasters in Venice, there are implications for modern times in terms of complex systems management and emerging threats, in particular from examples of risk management and resilience achieved by the Venetian state during outbreaks of the plague. In fourteenth century Venice, risk assessment the way we practice it today would fail to provide meaningful recommendations to reduce the casualty rate of the plague epidemic because the cause and transmission of the disease was not understood. Instead, a set of systemic actions across the social, economic, and transportation networks of the city taken by officials and doctors eventually slowed and arguably stopped the spread of the disease. These latter actions are an early example of what is now considered resilience management. Resilience management improves a complex system’s ability to prepare, absorb, recover, and adapt to unexpected threats and does so by address the capabilities at a system, rather than component, level. Resilience management can be a guide to addressing current issues of population growth and rising sea level in modern day Venice and across the globe. This paper calls for integration of resilience assessment in comprehensive risk and resilience management framework.     

Modelling and experimental studies on the transfer of radionuclides to fruit

Although fruit is an important component of the diet, the extent to which it contributes to radiological exposure remains unclear, partially as a consequence of uncertainties in models and data used to assess transfer of radionuclides in the food chain. A Fruits Working Group operated as part of the IAEA BIOMASS (BIOsphere Modelling and ASSessment) programme from 1997 to 2000, with the aim of improving the robustness of the models that are used for radiological assessment. The Group completed a number of modelling and experimental activities including: (i) a review of experimental, field and modelling information on the transfer of radionuclides to fruit; (ii) discussion of recently completed or ongoing experimental studies; (iii) development of a database on the transfer of radionuclides to fruit; (iv) development of a conceptual model for fruit and (v) two model intercomparison studies and a model validation study. The Group achieved significant advances in understanding the processes involved in transfer of radionuclides to fruit. The work demonstrated that further experimental and modelling studies are required to ensure that the current generation of models can be applied to a wide range of scenarios.     

Sources of uncertainty in model predictions: lessons learned from the IAEA Forest and Fruit Working Group model intercomparisons

The International Atomic Energy Agency (IAEA), through the BIOMASS program, has provided a unique international forum for assessing the relative contribution of different sources of uncertainty associated with environmental modeling. The methodology and guidance for dealing with parameter uncertainty have been fairly well developed and quantitative tools such as Monte-Carlo modeling are often recommended. The issue of model uncertainty is still rarely addressed in practical applications and the use of several alternative models to derive a range of model outputs (similar to what was done in IAEA model intercomparisons) is one of a few available techniques. This paper addresses the often overlooked issue of what we call 'modeler uncertainty,' i.e., differences in problem formulation, model implementation and parameter selection originating from subjective interpretation of the problem at hand. This study uses results from the Fruit and Forest Working Groups created under the BIOMASS program (BIOsphere Modeling and ASSessment). The greatest uncertainty was found to result from modelers' interpretation of scenarios and approximations made by modelers. In scenarios that were unclear for modelers, the initial differences in model predictions were as high as seven orders of magnitude. Only after several meetings and discussions about specific assumptions did the differences in predictions by various models merge. Our study shows that the parameter uncertainty (as evaluated by a probabilistic Monte-Carlo assessment) may have contributed over one order of magnitude to the overall modeling uncertainty. The final model predictions ranged between one and three orders of magnitude, depending on the specific scenario. This study illustrates the importance of problem formulation and implementation of an analytic-deliberative process in fate and transport modeling and risk characterization.     

Cryptocurrency: governance for what was meant to be ungovernable

Cryptocurrencies have the potential to revolutionize the exchange of information and money through blockchain and distributed ledger technologies. Despite the promise of such underlying technologies, their reliance upon distributed consensus processes to approve software updates raises the potential for governance failures to destabilize a given cryptocurrency. These governance failures, known as ‘hard forks,’ can separate a cryptocurrency into two rival camps. Where such events can destabilize a given cryptocurrency’s value, and instill distrust in the capacity of a cryptocurrency to survive as a reliable vehicle of exchange, it is imperative for the cryptocurrency community to improve their governance processes and limit the potential for hard forks to occur. While the distributed nature of cryptocurrency governance makes any traditional governing process unlikely to succeed, anticipatory approaches that establish thresholds and metrics to determine when software reform is necessary may help alleviate the governance failures presented by many hard forks.