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To overcome product homogenisation issues, an increasing number of supply chain members have been binding their competitive advantages to services which extend supply chain competition to a new dimension. This paper proposes a product service supply chain (PSSC) network competition model with multiple competing manufacturers and retailers offering demand-enhancing and costly services. In order to obtain the decision-making results in a non-cooperative game context, equilibrium conditions are established using variational inequality, and the qualitative properties are discussed. We compare the two competition scenarios, price competition only and simultaneous price and service competition, to discover the impact of service-offering on supply chain members. After introducing the service competition, both manufacturers and retailers realise higher profits and marginal profits with the same service costs. In contrast, the manufacturers' marginal profits achieve higher growth rates. We also find that (a) the increase of the demand sensitivities to the service levels of retailers would contribute more to the revenue growth of the whole PSSC network and (b) when service costs are different for manufacturers and retailers, the optimal decision-making on service levels are not only affected by the service costs of the members in the same tier but also correlated with those in other tiers. [ABSTRACT FROM AUTHOR]

Most theories of decision making under risk assume that payoffs and probabilities are separable. In the context of a lottery, the subjective value of a prospective outcome (the payoff) is assumed to be independent of the likelihood that the outcome will occur (the probability). In violation of this assumption, we present eight experiments showing that people anticipate less utility from uncertain outcomes than from certain outcomes, even conditional on their realization. The devaluation of uncertain outcomes is observed across different measures of utility (willingness to spend money or time; choice between different options), different populations (student and online samples), and different manipulations of uncertainty. We show that this result does not simply reflect a misunderstanding of the instructions or people's aversion toward a "weird" transaction with unexplained features. We highlight the implications of this phenomenon for empirical investigations of risk preferences and conclude with a discussion of the psychological mechanisms that might drive the devaluation of probabilistic outcomes. This paper was accepted by Yuval Rottenstreich, behavioral economics and decision analysis. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.02284. [ABSTRACT FROM AUTHOR]

Background: Denosumab and alendronate had a positive impact on bone mineral density (BMD) preservation after kidney transplantation. However, the cost-effectiveness of these agents in the context of kidney transplantation remains largely unexplored. We have conducted a cost-effectiveness analysis to compare the cost and the clinical outcomes of adding subcutaneous (SC) 60 mg denosumab every 6 months vs. oral weekly 70 mg Alendronate, to the standard care therapy (vitamin D and calcium) in renal transplant recipients (RTRs). The impact of both drugs on BMD t-score improvement and fracture prevention was investigated. A decision-analysis model from a health care payer perspective was applied. Results: The cost-effectiveness analysis has shown that alendronate add-on to the standard therapy was the most cost-effective regimen, in terms of BMD improvement and fracture prevention with an incremental cost-effectiveness ratio (ICER) of $154/patient/year. The one-way sensitivity analyses have delineated the change in cost-effectiveness when alendronate retail unit price was increased by 25% and 50%, or when denosumab retail unit price was decreased by 25% and 50%. The model was sensitive to the uncertainties (95% confidence interval) in the probabilities of fracture prevention and the probabilities of attaining the desired outcome. Conclusion: The model suggests that oral once weekly alendronate add-on regimen to standard therapy seems to be substantially more cost effective than twice yearly SC denosumab in terms of BMD improvement and fracture prevention in RTRs. Longer time horizon models with longer follow-up periods for fracture risks and adverse events are warranted to validate these data. [ABSTRACT FROM AUTHOR]

Background: Despite suicide's public health significance and global mental health awareness, current suicide prevention efforts show limited impact, posing a challenge for low- and middle-income countries. This study aimed to develop a dynamic simulation model that could be used to examine the potential effectiveness of alternative interventions for reducing youth mental health problems and suicidal behavior in Bogotá, Colombia. Methods: A system dynamics model was designed using a participatory approach involving three workshops conducted in 2021 and 2022. These workshops engaged 78 stakeholders from various health and social sectors to map key mental health outcomes and influential factors affecting them. A model was subsequently developed, tested, and presented to the participants for interactive feedback, guided by a moderator. Simulation analyses were conducted to compare projected mental health outcomes for a range of intervention scenarios with projections for a reference scenario corresponding to business-as-usual. Results: A total of 6670 suicide attempts and 347 suicides are projected among 7 − 17-year-olds from January 1, 2023, to early 2031 under the business-as-usual scenario. Mental health issues among 12 − 17-year-olds are projected to increase from 18.9% (2023) to 27.8% (2031), and substance use issues from 2.29 to 2.49% over the same period. School-based suicide prevention and gatekeeper training are the most effective strategies, reducing total numbers of suicide attempts and suicides by more than 20% (i.e., compared to business-as-usual). However, discontinuous funding significantly hinders these effective suicide prevention efforts. Conclusions: Systems modelling is an important tool for understanding where the best strategic financial and political investments lie for improving youth mental health in resource-constrained settings. [ABSTRACT FROM AUTHOR]

Background: Treatment strategies for a symptomatic accessory navicular include both operative and nonoperative approaches. The primary aim of this study is to define health utility values for 7 health states experienced by those with a symptomatic accessory navicular who undergo operative and/or nonoperative treatment. Secondarily, the study incorporates the health utility values with treatment costs, probabilities of various outcomes, and duration of health states into a cost-effectiveness model comparing the nonoperative treatment protocol at our institution vs surgical excision. Methods: Institutional review board approval was obtained to call parents of patients 10-20 years old at the time of interview who were evaluated for a symptomatic accessory navicular from February 1, 2016, to March 2, 2023, at a single institution by one of 4 pediatric orthopaedic surgeons. Participants were asked to rate 7 health states from 0 to 100, with 0 representing death (if 18 years or older) or the worst health imaginable (if under 18 years) and 100 representing perfect health. Using published values for the probabilities of various treatment outcomes, time spent in various health states, and Medicare costs from the perspective of the payor and society, a decision analysis was constructed. Results: Health utility values for 7 health states were obtained. Operative treatment was preferred to nonoperative treatment in the base case model. Surgery was more expensive ($16 825) than nonoperative treatment ($7486). Using a willingness-to-pay threshold of <$50 000 per quality-adjusted life year (QALY), surgery was cost-effective compared to nonoperative treatment with an incremental cost-effectiveness ratio of $20 303/QALY. Sensitivity analysis revealed that the only variable that indicated a preference for nonoperative treatment is a 71% likelihood of nonoperative treatment resolving the condition. Conclusion: Unless a physician suspects at least a 71% chance of a symptomatic accessory navicular resolving without operative treatment, surgical excision is recommended from a cost-effectiveness perspective. [ABSTRACT FROM AUTHOR]

Hydrocracking represents a complex and time-consuming chemical process that converts heavy oil fractions into various valuable products with low boiling points. It plays a pivotal role in enhancing the quality of products within the oil refining process. Consequently, the development of efficient surrogate models for simulating the hydrocracking process and identifying appropriate solutions for multi-objective oil refining is now an important area of research. In this study, a novel transferable preference learning-driven evolutionary algorithm is proposed to facilitate multi-objective decision analysis in the oil refining process. Specifically, our approach involves considering user preferences to divide the objective space into a region of interest (ROI) and other subspaces. We then utilize Kriging models to approximate the sub-problems within the ROI. In order to enhance the robustness and generalization capability of the Kriging models during the evolutionary process, we transfer the mutual information between the sub-problems in the ROI. To validate the effectiveness as well as efficiency of our proposed method, we undertake a series of experiments on both benchmarks and the oil refining process. The experimental results conclusively demonstrate the superiority of our approach. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has the potential to improve life and reduce risks by providing large amounts of information embedded in big databases and by suggesting or implementing automated decisions under uncertainties. Yet, in the design of a prescriptive AI algorithm, some problems may occur, first and clearly, if the AI information is wrong or incomplete. But the main point of this article is that under uncertainties, the decision algorithm, rational or not, includes, in one way or another, a risk attitude in addition to deterministic preferences. That risk attitude implemented in the software is chosen by the analysts, the organization that they serve, the experts who inform them, and more generally by the process of identifying possible options. The problem is that it may or may not represent, as it should, the preferences of the actual decision maker (the risk manager) and of the people subjected to his/her decisions. This article briefly describes the sometimes‐serious problem of that discrepancy between the preferences of the risk managers who use an AI output, and the risk attitude embedded in the AI system. The recommendation is to make these AI factors as accessible and transparent as possible and to allow for preference adjustments in the model if needed. The formulation of two simplified examples is described, that of a medical doctor and his/her patient when using an AI system to decide of a treatment option, and that of a skipper in a sailing race such as the America's Cup, receiving AI‐processed sensor signals about the sailing conditions on different possible courses. [ABSTRACT FROM AUTHOR]

Background: Emergency departments (EDs) are often the front door for urgent mental health care, especially when demand exceeds capacity. Long waits in EDs exert strain on hospital resources and worsen distress for individuals experiencing a mental health crisis. We used as a test case the Australian Capital Territory (ACT), with a population surge of over 27% across 2011–2021 and a lagging increase in mental health care capacity, to evaluate population-based approaches to reduce mental health-related ED presentations. Methods: We developed a system dynamics model for the ACT region using a participatory approach involving local stakeholders, including health planners, health providers and young people with lived experience of mental health disorders. Outcomes were projected over 2023–2032 for youth (aged 15–24) and for the general population. Results: Improving the overall mental health care system through strategies such as doubling the annual capacity growth rate of mental health services or leveraging digital technologies for triage and care coordination is projected to decrease youth mental health-related ED visits by 4.3% and 4.8% respectively. Implementation of mobile crisis response teams (consisting of a mental health nurse accompanying police or ambulance officers) is projected to reduce youth mental health-related ED visits by 10.2% by de-escalating some emergency situations and directly transferring selected individuals to community mental health centres. Other effective interventions include limiting re-presentations to ED by screening for suicide risk and following up with calls post-discharge (6.4% reduction), and limiting presentations of frequent users of ED by providing psychosocial education to families of people with schizophrenia (5.1% reduction). Finally, combining these five approaches is projected to reduce youth mental health-related ED presentations by 26.6% by the end of 2032. Conclusions: Policies to decrease youth mental health-related ED presentations should not be limited to increasing mental health care capacity, but also include structural reforms. [ABSTRACT FROM AUTHOR]

Invasive species are a major cause of biodiversity loss and are notoriously expensive and challenging to manage. We developed a decision‐analytic framework for evaluating invasive species removal strategies, given objectives of maximizing eradication probability and minimizing costs. The framework uses an existing estimation model for spatially referenced removal data—one of the most accessible types of invasive species data—to obtain estimates of population growth rate, movement probability, and detection probability. We use these estimates in simulations to identify Pareto‐efficient strategies—strategies where increases in eradication probability cannot be obtained without increases in cost—from a set of proposed strategies. We applied the framework post hoc to a successful eradication of veiled chameleons (Chamaeleo calyptratus) and identified the potential for substantial improvements in efficiency. Our approach provides managers and policymakers with tools to identify cost‐effective strategies for a range of invasive species using only prior knowledge or data from initial physical removals. [ABSTRACT FROM AUTHOR]

The valuation and planning of complex projects are becoming increasingly challenging with rising market uncertainty and the deregulation of many industries, which have also raised the need for efficient risk management. We take the perspective of a private firm interested in sequential capacity expansion of a project and develop a framework for measuring the downside risk of the serial project and optimising the sequence of the stages. Under general distributional assumptions for the duration of each stage, we present an accurate representation of the project's net present value (NPV) distribution based on a Pearson curve fit, leading to closed-form expressions for the associated risk measures. We then assess the impact of duration variability on the value at risk and demonstrate its role in stochastic project scheduling. We also account for the trade-off between maximising the expected NPV and minimising the risk exposure, and obtain the optimal schedule for risk-averse decision-makers. It becomes obvious that both the duration variability of each stage and the decision-makers' risk preferences can significantly affect the optimal sequence of the stages and that high duration variability is not always undesirable, even for risk-averse decision-makers. [ABSTRACT FROM AUTHOR]

Background: A clinical tool to estimate the risk of treatment-resistant schizophrenia (TRS) in people with first-episode psychosis (FEP) would inform early detection of TRS and overcome the delay of up to 5 years in starting TRS medication. Aims: To develop and evaluate a model that could predict the risk of TRS in routine clinical practice. Method: We used data from two UK-based FEP cohorts (GAP and AESOP-10) to develop and internally validate a prognostic model that supports identification of patients at high-risk of TRS soon after FEP diagnosis. Using sociodemographic and clinical predictors, a model for predicting risk of TRS was developed based on penalised logistic regression, with missing data handled using multiple imputation. Internal validation was undertaken via bootstrapping, obtaining optimism-adjusted estimates of the model's performance. Interviews and focus groups with clinicians were conducted to establish clinically relevant risk thresholds and understand the acceptability and perceived utility of the model. Results: We included seven factors in the prediction model that are predominantly assessed in clinical practice in patients with FEP. The model predicted treatment resistance among the 1081 patients with reasonable accuracy; the model's C-statistic was 0.727 (95% CI 0.723–0.732) prior to shrinkage and 0.687 after adjustment for optimism. Calibration was good (expected/observed ratio: 0.999; calibration-in-the-large: 0.000584) after adjustment for optimism. Conclusions: We developed and internally validated a prediction model with reasonably good predictive metrics. Clinicians, patients and carers were involved in the development process. External validation of the tool is needed followed by co-design methodology to support implementation in early intervention services. [ABSTRACT FROM AUTHOR]

We consider a dynamic duopoly game where firms choose both the timing and size of their investments. The existing real options literature predominantly consists of contributions where firms have a single option to invest. This paper relaxes this assumption by giving Firm A multiple options to undertake further investments with the purpose to expand whereas Firm B only holds the option to enter the market. In this asymmetric setting we get the surprising result that, in equilibrium, Firm B invests first. If Firm A invests first, Firm A and Firm B keep on being involved in preemption games for subsequent investments until Firm B enters the market, which leads to inefficiently early investments of Firm A. When Firm B invests first, then only one preemption game is played, which leads to Firm A being free to choose its unrestricted optimal investment moments. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach to study the production of consumption goods within a price floor carbon emission trading scheme. A stochastic Stackelberg game is set up between the environment authority (Player 1), and the manufacturer/producer (Player 2). In such a hybrid emission trading scheme, the authority sets an emissions target and producers will choose to participate in carbon trading on this basis, subject to a stochastic demand with information delayed. When the stochastic demand is determined by the market sale price and the scale of carbon emission reduction, we apply the maximum principle to obtain both the optimal production and market price. We show when the demand is determined by both the market sales price and carbon emission reduction, manufacturers are persuaded to set the market sales price first (ex ante incentive) and then proceed to a carbon trading scheme, thus providing a backward-looking advantage to manufacturers. Consequently, the manufacturer and the environmental authority can find a subgame-perfect Nash equilibrium via backward induction. [ABSTRACT FROM AUTHOR]

Abstract Background Denosumab and alendronate had a positive impact on bone mineral density (BMD) preservation after kidney transplantation. However, the cost-effectiveness of these agents in the context of kidney transplantation remains largely unexplored. We have conducted a cost-effectiveness analysis to compare the cost and the clinical outcomes of adding subcutaneous (SC) 60 mg denosumab every 6 months vs. oral weekly 70 mg Alendronate, to the standard care therapy (vitamin D and calcium) in renal transplant recipients (RTRs). The impact of both drugs on BMD t-score improvement and fracture prevention was investigated. A decision-analysis model from a health care payer perspective was applied. Results The cost-effectiveness analysis has shown that alendronate add-on to the standard therapy was the most cost-effective regimen, in terms of BMD improvement and fracture prevention with an incremental cost-effectiveness ratio (ICER) of $154/patient/year. The one-way sensitivity analyses have delineated the change in cost-effectiveness when alendronate retail unit price was increased by 25% and 50%, or when denosumab retail unit price was decreased by 25% and 50%. The model was sensitive to the uncertainties (95% confidence interval) in the probabilities of fracture prevention and the probabilities of attaining the desired outcome. Conclusion The model suggests that oral once weekly alendronate add-on regimen to standard therapy seems to be substantially more cost effective than twice yearly SC denosumab in terms of BMD improvement and fracture prevention in RTRs. Longer time horizon models with longer follow-up periods for fracture risks and adverse events are warranted to validate these data.

Abstract Background Despite suicide’s public health significance and global mental health awareness, current suicide prevention efforts show limited impact, posing a challenge for low- and middle-income countries. This study aimed to develop a dynamic simulation model that could be used to examine the potential effectiveness of alternative interventions for reducing youth mental health problems and suicidal behavior in Bogotá, Colombia. Methods A system dynamics model was designed using a participatory approach involving three workshops conducted in 2021 and 2022. These workshops engaged 78 stakeholders from various health and social sectors to map key mental health outcomes and influential factors affecting them. A model was subsequently developed, tested, and presented to the participants for interactive feedback, guided by a moderator. Simulation analyses were conducted to compare projected mental health outcomes for a range of intervention scenarios with projections for a reference scenario corresponding to business-as-usual. Results A total of 6670 suicide attempts and 347 suicides are projected among 7 − 17-year-olds from January 1, 2023, to early 2031 under the business-as-usual scenario. Mental health issues among 12 − 17-year-olds are projected to increase from 18.9% (2023) to 27.8% (2031), and substance use issues from 2.29 to 2.49% over the same period. School-based suicide prevention and gatekeeper training are the most effective strategies, reducing total numbers of suicide attempts and suicides by more than 20% (i.e., compared to business-as-usual). However, discontinuous funding significantly hinders these effective suicide prevention efforts. Conclusions Systems modelling is an important tool for understanding where the best strategic financial and political investments lie for improving youth mental health in resource-constrained settings.

Abstract Background Emergency departments (EDs) are often the front door for urgent mental health care, especially when demand exceeds capacity. Long waits in EDs exert strain on hospital resources and worsen distress for individuals experiencing a mental health crisis. We used as a test case the Australian Capital Territory (ACT), with a population surge of over 27% across 2011–2021 and a lagging increase in mental health care capacity, to evaluate population-based approaches to reduce mental health-related ED presentations. Methods We developed a system dynamics model for the ACT region using a participatory approach involving local stakeholders, including health planners, health providers and young people with lived experience of mental health disorders. Outcomes were projected over 2023–2032 for youth (aged 15–24) and for the general population. Results Improving the overall mental health care system through strategies such as doubling the annual capacity growth rate of mental health services or leveraging digital technologies for triage and care coordination is projected to decrease youth mental health-related ED visits by 4.3% and 4.8% respectively. Implementation of mobile crisis response teams (consisting of a mental health nurse accompanying police or ambulance officers) is projected to reduce youth mental health-related ED visits by 10.2% by de-escalating some emergency situations and directly transferring selected individuals to community mental health centres. Other effective interventions include limiting re-presentations to ED by screening for suicide risk and following up with calls post-discharge (6.4% reduction), and limiting presentations of frequent users of ED by providing psychosocial education to families of people with schizophrenia (5.1% reduction). Finally, combining these five approaches is projected to reduce youth mental health-related ED presentations by 26.6% by the end of 2032. Conclusions Policies to decrease youth mental health-related ED presentations should not be limited to increasing mental health care capacity, but also include structural reforms.

In the evolving field of decision-making, the continuous advancement of technologies and methodologies drives the pursuit of more reliable tools. Decision support systems (DSS) provide information to make informed choices and multi-criteria decision analysis (MCDA) methods are an important component of defining decision models. Despite their usefulness, there are still challenges in making robust decisions in dynamic environments due to the varying performance of different MCDA methods. It creates space for the development of techniques to aggregate conflicting results. This paper introduces a fuzzy ranking approach for aggregating results from multi-criteria assessments, specifically addressing the limitations of current result aggregation techniques. Unlike conventional methods, the proposed approach represents rankings as fuzzy sets, providing detailed insights into the robustness of decision problems. The study uses green supplier selection as a case study, examining the performance of the introduced approach and the robustness of its recommendations within the sustainability field. This study offers a new methodology for aggregating results from multiple evaluation scenarios, thereby enhancing decision-maker awareness and robustness. Through comparative analysis with traditional compromise solution methods, this paper highlights the limitations of current approaches and indicates the advantages of adopting fuzzy ranking aggregation. This study significantly advances the field of decision-making by enhancing the understanding of the stability of decision outcomes. [ABSTRACT FROM AUTHOR]

Abstract Hydrocracking represents a complex and time-consuming chemical process that converts heavy oil fractions into various valuable products with low boiling points. It plays a pivotal role in enhancing the quality of products within the oil refining process. Consequently, the development of efficient surrogate models for simulating the hydrocracking process and identifying appropriate solutions for multi-objective oil refining is now an important area of research. In this study, a novel transferable preference learning-driven evolutionary algorithm is proposed to facilitate multi-objective decision analysis in the oil refining process. Specifically, our approach involves considering user preferences to divide the objective space into a region of interest (ROI) and other subspaces. We then utilize Kriging models to approximate the sub-problems within the ROI. In order to enhance the robustness and generalization capability of the Kriging models during the evolutionary process, we transfer the mutual information between the sub-problems in the ROI. To validate the effectiveness as well as efficiency of our proposed method, we undertake a series of experiments on both benchmarks and the oil refining process. The experimental results conclusively demonstrate the superiority of our approach.

I thank Thomas Richardson and James Robins for their discussion of my article, and discuss the similarities and differences between their approach to causal modelling, based on single world intervention graphs, and my own decision-theoretic approach.

Contemporary wildlife disease management is complex because managers need to respond to a wide range of stakeholders, multiple uncertainties, and difficult trade‐offs that characterize the interconnected challenges of today. Despite general acknowledgment of these complexities, managing wildlife disease tends to be framed as a scientific problem, in which the major challenge is lack of knowledge. The complex and multifactorial process of decision‐making is collapsed into a scientific endeavor to reduce uncertainty. As a result, contemporary decision‐making may be oversimplified, rely on simple heuristics, and fail to account for the broader legal, social, and economic context in which the decisions are made. Concurrently, scientific research on wildlife disease may be distant from this decision context, resulting in information that may not be directly relevant to the pertinent management questions. We propose reframing wildlife disease management challenges as decision problems and addressing them with decision analytical tools to divide the complex problems into more cognitively manageable elements. In particular, structured decision‐making has the potential to improve the quality, rigor, and transparency of decisions about wildlife disease in a variety of systems. Examples of management of severe acute respiratory syndrome coronavirus 2, white‐nose syndrome, avian influenza, and chytridiomycosis illustrate the most common impediments to decision‐making, including competing objectives, risks, prediction uncertainty, and limited resources. [ABSTRACT FROM AUTHOR]

Sustainable tourism can combine recreation for people, economic opportunities for entrepreneurs, and environmental protection, but for mountain tourism, climate change and the predicted reductions in snowfall require urgent action. Therefore, this study aims to identify sustainable mountain tourism strategies and consumption patterns using a multi‐criteria decision methodology and data collected through an online survey and expert opinions. The results show the importance of stakeholder engagement and the relevance of three strategies: (i) zero‐emission lodges; (ii) energy communities, and (iii) zero‐emission ski lifts. As well as pointing out the need for sustainability awareness and education. While respondents pay a great deal of attention to sustainability, the analysis highlights three policy interventions to safeguard mountain tourism: (i) financing the conversion of facilities; (ii) expanding infrastructure to reach mountain resorts, and (iii) rewarding consumers for choosing a certified zero‐emission resort. [ABSTRACT FROM AUTHOR]

Analysing and mitigating errors in production processes is a primary objective of companies in the automotive sector. Unfortunately, due to inaccurate or partially missing information, comparing errors is often very difficult, resulting from the experts' provision of incomplete pairwise comparison matrices. In the literature, several techniques have been developed to complete such matrices. These techniques merely estimate what the decision makers or experts would have entered according to known entries. In this article, we propose a new methodology based on the stochastic multi-objective acceptability analysis; we apply it to vary the missing entries of the pairwise comparison matrix, thus providing the probability that an alternative/criterion will attain a given rank. This approach gives a complete view of the possible outcomes because it represents all possible decision maker mindsets. We present a case study carried out in a multinational automotive industry where we apply our methodology for evaluating errors in the production process. [ABSTRACT FROM AUTHOR]

Generic data envelopment analysis (DEA) models are based on deterministic input and output. However, input and output vectors are often interrupted by random factors, such as measurement errors and data noise, in real economic situations. This study proposes a new chance-constrained network DEA model based on the modified directional distance function (DDF) and enhanced Russell measure (ERM) model for assessing government management and culture-led urban regeneration. In addition to exploring the randomness of data, this study integrates the advantages of both ERM and DDF and considers the inefficiency level from a non-oriented viewpoint, the direction vector, and each input and output simultaneously. Each input and output of the two production stages can use non-radials to measure efficiency. Results show that the urban-rural gap has gradually widened since 2015. To validate the legitimacy of the model, this study utilizes the bootstrapping method to verify the results of the stochastic network DEA model and the conventional two-stage network DEA approach. This study also considers different alpha values as basis for comparison to confirm whether the results obtained differ by uncertainty level. [ABSTRACT FROM AUTHOR]

Communities throughout the United States have come to rely upon agriculture as a pillar of their political integrity, economic security, and health and wellbeing. Climatic conditions in the western portion of the United States necessitate most lands be irrigated to be arable. As a result, a major portion of the economy of the United States, and by extension the world economy, is driven by the continued viability of western United States water law and policy. Furthermore, due to the strong interrelationship between anthropogenic consumptive uses, streamflows, and wetland/riparian area ecology, irrigation demand has a strong effect on stream morphology, quality, and biology for aquatic species. Western water management is a complex mosaic that is controlled by western state, federal, and tribal governments. Each of these systems of law have vulnerabilities to climate change, which is well understood to cause increasing water supply scarcity. This articledemonstrates the risks climate change poses to our management of irrigation water demand, as well as the interrelationship between water supply and demand. Due to the shared nature of the resource, this article addresses both tribal reserved rights and state-based rights using data from Indian reservations that either contain and/or are closely adjacent to non-tribal agricultural communities. Those data are used in a systems–dynamics model to integrate crop–water requirement estimation techniques with climate change estimates and a Monte Carlo analysis to assess how irrigation demand could change because of changing temperature, precipitation, incoming radiation, and wind speed caused by climate change. Results indicate that climate change will cause increases in irrigation requirements at most locations. Further, climate change is expected to significantly increase seasonal variability in many locations. The model provides a useful tool based upon publicly available data that will allow individual water users to make conservation decisions necessary to preserve their water rights as the climate changes. [ABSTRACT FROM AUTHOR]

Internal quality control in clinical chemistry laboratories are based on analyzing samples of stable control materials among the patient samples. The control results are interpreted by using quality control rules that usually are designed to detect systematic errors. The best rules have a high probability of error detection (Ped), i.e. to detect the maximal allowable (critical) systematic error and a low probability of false rejection (Pfr, false alarm). In this work we show that quality control rules can be represented by points on a ROC curve which appears when Ped is plotted against Pfrand only the control limit is varied. Further, we introduce a new method for choosing the optimal control limit, analogous to choosing the optimal operating point on the ROC curve of a diagnostic test. This decision needs knowledge of the pretest probability of a critical systematic error, the benefit of detecting it when it occurs and the cost of false alarm. The ROC curve analysis showed that if rules based on N = 2 are used, mean rules outperform Westgard rules because the ROC curve of the mean rules was lying above the ROC curves of the Westgard rules. A mean rule also had a lower maximum expected increase in the number of unacceptable patient results reported during the presence of an out-of-control error condition (Max E(NUF)) than comparable Westgard rules. [ABSTRACT FROM AUTHOR]

Colorectal cancer (CRC) screening is highly effective but underused. Blood-based biomarkers (liquid biopsy) could improve screening participation. Using our established Markov model, screening every 3 years with a blood-based test that meets minimum Centers for Medicare & Medicaid Services' thresholds (CMS min) (CRC sensitivity 74%, specificity 90%) was compared with established alternatives. Test attributes were varied in sensitivity analyses. CMS min reduced CRC incidence by 40% and CRC mortality by 52% vs no screening. These reductions were less profound than the 68%–79% and 73%–81%, respectively, achieved with multi-target stool DNA (Cologuard; Exact Sciences) every 3 years, annual fecal immunochemical testing (FIT), or colonoscopy every 10 years. Assuming the same cost as multi-target stool DNA, CMS min cost $28,500/quality-adjusted life-year gained vs no screening, but FIT, colonoscopy, and multi-target stool DNA were less costly and more effective. CMS min would match FIT's clinical outcomes if it achieved 1.4- to 1.8-fold FIT's participation rate. Advanced precancerous lesion (APL) sensitivity was a key determinant of a test's effectiveness. A paradigm-changing blood-based test (sensitivity >90% for CRC and 80% for APL; 90% specificity; cost ≤$120–$140) would be cost-effective vs FIT at comparable participation. CMS min could contribute to CRC control by achieving screening in those who will not use established methods. Substituting blood-based testing for established effective CRC screening methods will require higher CRC and APL sensitivities that deliver programmatic benefits matching those of FIT. High APL sensitivity, which can result in CRC prevention, should be a top priority for screening test developers. APL detection should not be penalized by a definition of test specificity that focuses on CRC only. [Display omitted] Blood tests to screen for colorectal cancer could be effective in people who decline stool tests or colonoscopy, but they should not substitute for established methods unless they achieve high detection rates for colorectal cancer and its precursors. [ABSTRACT FROM AUTHOR]

Consider the problem of allocating indivisible goods among agents with additive valuations, where monetary payments are not allowed. When randomization is allowed, it is possible to achieve compelling notions of fairness such as EV, which states that no agent should prefer any other agent's allocation to their own. When allocations must be deterministic, achieving exact fairness is impossible but approximate notions such as EV up to one good can be guaranteed. In "Best of Both Worlds: Ex Ante and Ex Post Fairness in Resource Allocation," H. Aziz, R. Freeman, N. Shah, and R. Vaish ask whether it is possible to achieve both types of guarantees simultaneously. More specifically, they ask whether there exists a probability distribution over deterministic allocations such that every deterministic allocation is envy-free up to one good and the distribution is exactly envy-free in expectation. The main result of the paper answers this question in the affirmative, showing that ex ante and ex post fairness need not be in conflict. We study the problem of allocating indivisible goods among agents with additive valuations. When randomization is allowed, it is possible to achieve compelling notions of fairness such as envy-freeness, which states that no agent should prefer any other agent's allocation to their own. When allocations must be deterministic, achieving exact fairness is impossible but approximate notions such as envy-freeness up to one good can be guaranteed. Our goal in this work is to achieve both simultaneously, by constructing a randomized allocation that is exactly fair ex ante (before the randomness is realized) and approximately fair ex post (after the randomness is realized). The key question we address is whether ex ante envy-freeness can be achieved in combination with ex post envy-freeness up to one good. We settle this positively by designing an efficient algorithm that achieves both properties simultaneously. The algorithm can be viewed as a desirable way to instantiate a lottery for the probabilistic serial rule. If we additionally require economic efficiency, we obtain three impossibility results that show that ex post or ex ante Pareto optimality is impossible to achieve in conjunction with combinations of fairness properties. Hence, we slightly relax our ex post fairness guarantees and present a different algorithm that can be viewed as a fair way to instantiate a lottery for the maximum Nash welfare allocation rule. Funding: This work was supported by the Office of Naval Research [Grant N00014-17-1-2621] and DST INSPIRE [Grant DST/INSPIRE/04/2020/000107]. [ABSTRACT FROM AUTHOR]

This paper considers the "main manufacturer-supplier" model in collaborative cooperation among firms which requires the leader to invest significant resources and bear huge risks. However, few scholars simultaneously consider innovation risks and incentive issues under the model. We construct a Stackelberg game incentive model with different cost-sharing ratios under the risk of technological innovation. We characterize the equilibrium of the model and highlight the key role played by the main manufacturer and supplier. The results are as follows: (a) Main manufacturer can implement the cost-sharing incentive strategy under certain conditions, which is, its profit coefficient is greater than 0.75 times that of supplier and the cost-sharing ratios expected coefficient is greater than the minimum threshold. (b) The optimal cost-sharing ratio is directly proportional to the profit coefficient of the main manufacturer, inversely proportional to that of the supplier, and shows an inverted U-shape function with the probability of successful technological innovation. (c)This strategy can motivate suppliers to invest more resources, reduce the investment of the main manufacturer, simultaneously increase the profits of the main manufacturer and supplier in the certain ranges of innovation success probability and profit coefficients, with significant incentive effects. [ABSTRACT FROM AUTHOR]

A previous cost-effectiveness analysis published in 2022 found that the Total Posterior Spine (TOPSTM) system was dominant over transforaminal lumbar interbody fusion (TLIF). This analysis required updating to reflect a more complete dataset and pricing considerations. To evaluate the cost-effectiveness of TOPSTM system as compared with TLIF based on an updated and complete FDA investigational device exemption (IDE) data set. Cost-utility analysis of the TOPSTM system compared to TLIF. A multicenter, FDA IDE, randomized control trial (RCT) investigated the efficacy of TOPSTM compared to TLIF with a current population of n=305 enrolled and n=168 with complete 2-year follow-up. Cost and quality adjusted life years (QALYs) were calculated to determine our primary outcome measure, the incremental cost-effectiveness ratio. Secondary outcome measures included: net monetary benefit as well at willingness-to-pay (WTP) thresholds. The primary outcome of cost-effectiveness is determined by incremental cost-effectiveness ratio. A Markov model was used to simulate the health outcomes and costs of patients undergoing TOPSTM or TLIF over a 2-year period. alternative scenario sensitivity analysis, one-way sensitivity analysis, and probabilistic sensitivity analysis were conducted to assess the robustness of the model results. The updated base case result demonstrated that TOPSTM was immediately and longitudinally dominant compared with the control with an incremental cost-effectiveness ratio of −9,637.37 $/QALY. The net monetary benefit was correspondingly $2,237, both from the health system's perspective and at a WTP threshold of 50,000 $/QALY at the 2-year time point. This remained true in all scenarios tested. The Alternative Scenario Sensitivity Analysis suggested cost-effectiveness irrespective of payer type and surgical setting. To remain cost-effective, the cost difference between TOPSTM and TLIF should be no greater than $1,875 and $3,750 at WTP thresholds of $50,000 and 100,000 $/QALY, respectively. This updated analysis confirms that the TOPSTM device is a cost-effective and economically dominant surgical treatment option for patients with lumbar stenosis and degenerative spondylolisthesis compared to TLIF in all scenarios examined. [ABSTRACT FROM AUTHOR]

Although the subjective expected utility (SEU) theory is more than 60 years old, it was recently discovered by Hartmann (Econometrica 88(1):203–205, 2020, https://doi.org/10.3982/ECTA17428) that one of the original seven postulates is redundant, i.e., it is implied by the other six postulates. In this brief communication, we show that this redundant axiom is the only one that is implied by the other axioms, thereby establishing that the remaining six postulates form an independent axiomatic system. This result further streamlines the preference assumptions underlying the SEU theory. [ABSTRACT FROM AUTHOR]

Background: Travel-related strategies to reduce the spread of COVID-19 evolved rapidly in response to changes in the understanding of SARS-CoV-2 and newly available tools for prevention, diagnosis, and treatment. Modeling is an important methodology to investigate the range of outcomes that could occur from different disease containment strategies. Methods: We examined 43 articles published from December 2019 through September 2022 that used modeling to evaluate travel-related COVID-19 containment strategies. We extracted and synthesized data regarding study objectives, methods, outcomes, populations, settings, strategies, and costs. We used a standardized approach to evaluate each analysis according to 26 criteria for modeling quality and rigor. Results: The most frequent approaches included compartmental modeling to examine quarantine, isolation, or testing. Early in the pandemic, the goal was to prevent travel-related COVID-19 cases with a focus on individual-level outcomes and assessing strategies such as travel restrictions, quarantine without testing, social distancing, and on-arrival PCR testing. After the development of diagnostic tests and vaccines, modeling studies projected population-level outcomes and investigated these tools to limit COVID-19 spread. Very few published studies included rapid antigen screening strategies, costs, explicit model calibration, or critical evaluation of the modeling approaches. Conclusion: Future modeling analyses should leverage open-source data, improve the transparency of modeling methods, incorporate newly available prevention, diagnostics, and treatments, and include costs and cost-effectiveness so that modeling analyses can be informative to address future SARS-CoV-2 variants of concern and other emerging infectious diseases (e.g., mpox and Ebola) for travel-related health policies.

Abstract Invasive species are a major cause of biodiversity loss and are notoriously expensive and challenging to manage. We developed a decision‐analytic framework for evaluating invasive species removal strategies, given objectives of maximizing eradication probability and minimizing costs. The framework uses an existing estimation model for spatially referenced removal data—one of the most accessible types of invasive species data—to obtain estimates of population growth rate, movement probability, and detection probability. We use these estimates in simulations to identify Pareto‐efficient strategies—strategies where increases in eradication probability cannot be obtained without increases in cost—from a set of proposed strategies. We applied the framework post hoc to a successful eradication of veiled chameleons (Chamaeleo calyptratus) and identified the potential for substantial improvements in efficiency. Our approach provides managers and policymakers with tools to identify cost‐effective strategies for a range of invasive species using only prior knowledge or data from initial physical removals.

An evaluation was conducted to predict the economic and clinical burden of vaccinating all immunocompromised (IC) individuals aged ≥30 years with mRNA-1273 variant-adapted COVID-19 vaccines versus BNT162b2 variant-adapted vaccines in Fall 2023 and Spring 2024 in France. The number of symptomatic SARS-CoV-2 infections, hospitalizations or deaths due to COVID-19, and long COVID cases, costs and quality-adjusted life years (QALYs) were estimated using a static decision-analytic model. Predicted vaccine effectiveness (VE) were based on real-world data from the original and BA.4/5 variant-adapted vaccines, suggesting higher protection against infection and hospitalization with mRNA-1273 vaccines. VE estimates were combined with COVID-19 incidence and probability of COVID-19 severe outcomes. Uncertainty surrounding VE, vaccine coverage, infection incidence, hospitalization and mortality rates, costs and QALYs were evaluated in sensitivity analyses. In an ideal situation where 100% coverage is achieved, the mRNA-1273 variant-adapted vaccine is predicted to prevent an additional 3,882 infections, 357 hospitalizations, 81 deaths, and 326 long COVID cases when compared to BNT162b2 variant-adapted vaccines in 230,000 IC individuals. This translates to €10.1 million cost-savings from a societal perspective and 645 QALYs gained. Results were consistent across all analyses and most sensitive to variations surrounding VE and coverage. These findings highlight the importance of increasing vaccine coverage, and ability to induce higher levels of protection with mRNA-1273 formulations in this vulnerable population.

This paper undertakes a revisit of the sunk cost fallacy, which refers to the tendency of people to persist investing resources into something, even if it is destined to have no good outcome. We emphasize that the utilities associated with different alternatives are not static for decision makers, which is exactly opposite to the traditional perspective. This paper argues that the utility of an option may change due to the choice of another option, suggesting that decisions considered irrational by the traditional analytical method, i.e., sunk cost fallacy, may be rational. We propose a novel analytical method for decision making with sunk cost when considering the utility change and validate the effectiveness of this method through mathematical modeling and computational experiments. This paper mathematically describes such decision-making problems, analyzing the impact of changes in the utilities across different alternatives on decision making with a real-world example. Furthermore, we develop a two-stage stochastic optimization model for such decision-making problems and employ the sample average approximation (SAA) method to solve them. The results from computational experiments indicate that some decisions traditionally considered irrational are, in fact, rational when the utility of an option changes as a result of choosing another option. This paper, therefore, highlights the significance of incorporating utility changes into the decision-making process and stands as a valuable addition to the literature, offering a refreshed and effective decision-making method for improved decision making. [ABSTRACT FROM AUTHOR]

Although rapid transition away from fossil fuels will be required to limit warming to any agreed upon level, long-lived fossil energy infrastructure buildout continues largely unabated. Many nations around the world have laws requiring regulators to meaningfully assess potential climate impacts when reviewing proposals for new fossil fuel infrastructure projects such as oil and gas extraction leases, major transmission pipelines, and power plants. However, reviewing agencies often report lack of appropriate tools to objectively assess the climate impacts of individual fossil fuel infrastructure projects, and to determine the significance of these impacts. Here we present a novel methodology to fill this critical gap by providing a science-based decision support tool, a 'climate test', for use in determining an infrastructure project's carbon emissions significance. The climate test defines emissions significance quantitatively according to a project's consistency with region-specific constraints and characteristics of climate mitigation pathways, considering both emissions and energy systems characteristics over the project's lifespan. We showcase the tool here, using an example of a natural gas pipeline to explore how often and under what conditions the project would 'pass' the climate test with an emissions significance result of ≤1, which indicates alignment with a 1.5°C goal. For this project, less than 1.2% of 10,000 scenarios yielded significance values ≤1, suggesting a high likelihood that the project has a significant climate impact, a determination that would be robust to uncertainty for such a project. This emission-based metric represents a first step toward a broader framework to align project-level infrastructure decisions – including and beyond new fossil energy projects – with climate, as well as economic and societal, policy goals. This novel 'climate test' tool is suitable to support U.S. regulators in meeting their statutory requirements to assess the significance of climate impacts for fossil energy infrastructure projects on an individualized basis. While the tool was designed based on specific gaps identified in the U.S. system, it can readily be adapted for use in other non-U.S. regulatory contexts. Widespread implementation of such a climate test tool would help the US, and other nations that use it, to act consistently with their national and international climate commitments by aligning individual project-level decisions with pathways to limiting warming. By defining project significance in the context of both the emissions constraints and energy needs of a warming-limited world, this climate test tool provides decisionmakers with integrated and nuanced analytical information necessary to responsibly manage both energy and climate policy objectives. [ABSTRACT FROM AUTHOR]