We investigate the reengineeering of interbank networks with a specific focus on capital increase. We consider a scenario where all other components of the network’s infrastructure remain stable (a practical assumption for short-term situations). Our objective is to assess the impact of raising capital on the network’s robustness and to address the following key aspects. First, given a predefined target for network robustness, our aim is to achieve this goal optimally, minimizing the required capital increase. Second, in cases where a total capital increase has been determined, the central challenge lies in distributing this increase among the banks in a manner that maximizes the stability of the network. To tackle these challenges, we begin by developing a comprehensive theoretical framework. Subsequently, we formulate an optimization model for the network’s redesign. Finally, we apply this framework to practical examples, highlighting its applicability in real-world scenarios.

A case study review of a formal decision analysis involving a 10 year old girl. She had just faced the death of a close adult friend, and had become filled with uncertainty and emotion while facing the decision as to whether or not to attend his funeral. The study demonstrates that formal analysis can provide the sensitivity and caring basis for such decision making, thus counteracting some of the criticisms of decision analysis.

When there are multiple competing objectives in a decision-making process, Multi-Attribute Choice scoring models are excellent tools, permitting the incorporation of both subjective and objective attributes. However, their accuracy depends upon the subjective techniques used to construct the attribute scales and their concomitant weights. Conventional techniques using local scales tend to overemphasize small differences in attribute measures, which may yield erroneous conclusions. The Range Sensitivity Principle (RSP) is often invoked to adjust attribute weights when local scales are used. In practice, however, decision makers often do not follow the prescriptions of the Range Sensitivity Principle and under-adjust the weights, resulting in potentially poor decisions. Examples are discussed as is a proposed solution: the use of global scales instead of local scales.

How do people make everyday decisions in order to achieve the most successful outcome? Decision making research typically evaluates choices according to their expected utility. However, this research largely focuses on abstract or hypothetical tasks and rarely investigates whether the outcome is successful and satisfying for the decision maker. Instead, we use an everyday decision making task in which participants describe a personally meaningful decision they are currently facing. We investigate the decision processes used to make this decision, and evaluate how successful and satisfying the outcome of the decision is for them. We examine how well analytic, attribute-based processes explain everyday decision making and predict decision outcomes, and we compare these processes to associative processes elicited through free association. We also examine the characteristics of decisions and individuals that are associated with good decision outcomes. Across three experiments we found that: 1) an analytic decision analysis of everyday decisions is not superior to simpler attribute-based processes in predicting decision outcomes; 2) contrary to research linking associative cognition to biases, free association generates valid cues that predict choice and decision outcomes as effectively as attribute-based approaches; 3) contrary to research favouring either attribute-based or associative processes, combining both attribute-based and associates best explains everyday decisions and most accurately predicts decision outcomes; and 4) individuals with a tendency to attempt analytic thinking do not make more successful everyday decisions. Instead, frequency, simplicity, and knowledge of the decision predict success. We propose that attribute-based and associative processes, in combination, both explain everyday decision making and predict successful decision outcomes.

An experienced decision aider reflects on how misaligned priorities produce decision research that is less useful than it could be. Scientific interest and professional standing may motivate researchers — and their funders and publishers — more powerfully than concern to help people make better

Almost stochastic dominance has been receiving a great amount of attention in the financial and economic literatures. In this paper, we characterize the properties of almost first-order stochastic dominance (AFSD) via distorted expectations and investigate the conditions under which AFSD is preserved under a distortion transform. The main results are also applied to establish stochastic comparisons of order statistics and receiver operating characteristic curves via AFSD.

This article describes a methodology for a risk-informed benefit–cost analysis that includes (i) risk analysis to quantify risk reduction benefits and (ii) uncertainty analyses to quantify probability distributions over costs and benefits. It also summarizes the lessons from 25 applications of this methodology to evaluate R&D projects of the Science and Technology Directorate of the Department of Homeland Security. The article then illustrates the methodology with a specific application to evaluate the benefits and costs of the Advanced Personal Protection System (APPS), a new garment system developed to protect wildland firefighters. The goals of the APPS project were to reduce risk and to improve comfort. The cost analysis revealed that the APPS garments are more expensive by about $279 per garment system. Total costs were roughly $7.3 million, including the upfront project cost and the increased 5 year cost of purchasing the APPS. Benefits from reduced injuries and fatalities resulted in 5 year benefits of about $19.3 million, with an NPV of $13.6 million in 2019 dollars. In the base case, the benefit–cost ratio was 2.87 and the return on investment was 187 % over 5 years. Taking the perspective of a decision-maker when the project was first funded in 2011, NPVs are $11,993,728, $10,025,519, and $7,967,479 in 2011 dollars for discount rates of 0, 3, and 7 % respectively. An uncertainty analysis of the NPV showed a large variability, ranging from the 5th percentile of $6.4 million to a median of $19.3 million to the 95th percentile of $43.7 million in 2019 dollars. This large range was primarily due to the uncertainty about the reduction of fatality and injury risks and the market penetration rates of the new garments.

A simulation model based on the theory of clinical decision analysis was used to compare outcomes and costs when treating patients with major depressive episodes using either a selective serotonin re-uptake inhibitor (SSRI) or a tricyclic antidepressant (TCA), in comparison with milnacipran (a serotonin), and a norepinephrine re-uptake inhibitor (SNRI). The clinical data used were taken from published meta-analyses. This analysis supports: (1) a comparable efficacy of milnacipran and TCA with a better tolerance; and, (2) an advantage of milnacipran over SSRI for efficacy with a comparable tolerance. Based on these findings, a decision tree was constructed with the assistance of a panel of psychiatrists in order to provide a model of usual clinical practice. Estimates not available from clinical studies were obtained either from literature analysis or from the panel.

Economic appraisal was performed according to the viewpoint of the French national sickness fund (sécurité sociale), and expenditure assessment was limited to direct costs (hospitalizations, antidepressant medications, visits, and laboratory tests). The results suggest that milnacipran is a cost-effective alternative: the expected cost of treatment per depressive episode is lower than either a French representative panel of TCAs (a saving of 288 FF), or SSRIs (a savings of 961 FF). The expected length of clinical remission is slightly higher than comparators. The robustness of these findings was supported by sensitivity analyses.

“Uncertain futures” refers to a set of policy problems that possess some combination of the following characteristics: (i) they potentially cause irreversible changes; (ii) they are widespread, so that policy responses may make sense only on a global scale; (iii) network effects are difficult to understand and may amplify (or moderate) consequences; (iv) time horizons are long; and (v) the likelihood of catastrophic outcomes is unknown or even unknowable. These characteristics tend to make uncertain futures intractable to market solutions because property rights are not clearly defined and essential information is unavailable. These same factors also pose challenges for benefit-cost analysis (BCA) and other traditional decision analysis tools. The diverse policy decisions confronting decision-makers today demand “dynamic BCA,” analytic frameworks that incorporate uncertainties and trade-offs across policy areas, recognizing that: perceptions of risks can be uninformed, misinformed, or inaccurate; risk characterization can suffer from ambiguity; and experts’ tendency to focus on one risk at a time may blind policymakers to important trade-offs. Dynamic BCA – which recognizes trade-offs, anticipates the need to learn from experience, and encourages learning – is essential for lowering the likelihoods and mitigating the consequences of uncertain futures while encouraging economic growth, reducing fragility, and increasing resilience.

Protected area managers often face uncertainty when managing invasive plants at the landscape scale. Crested wheatgrass, a popular forage crop in the Great Plains since the 1930s, is an aggressive invader of native grassland and a problem for land managers in protected areas where seeded roadsides and abandoned fields encroach into the native mixed-grass prairie. Given limited resources, land managers need to determine the best strategy for reducing the cover of crested wheatgrass. However, there is a high degree of uncertainty associated with the dynamics of crested wheatgrass spread and control. To compare alternative management strategies for crested wheatgrass in the face of uncertainty, we conducted a decision analysis based on information from Grasslands National Park. Our analysis involves the use of a spatially explicit model that incorporates alternative management strategies and hypotheses about crested wheatgrass spread and control dynamics. Using a decision tree and assigning probabilities to our alternative hypotheses, we calculated the expected outcome of each management alternative and ranked these alternatives. Because the probabilities assigned to alternative hypotheses are also uncertain, we conducted a sensitivity analysis of the full probability space. Our results show that under current funding levels it is always best to prioritize the early detection and control of new infestations. Monitoring the effectiveness of control is paramount to long-term success, emphasising the need for adaptive approaches to invasive plant management. This type of decision analysis approach could be applied to other invasive plants where there is a need to find management strategies that are robust to uncertainty in the current understanding of how these plants are best managed.