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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]

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]

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]

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]

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]

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]

Copyright of Conservation Biology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

Purpose: This study aims to help understand individual donors' preferences over different designs of humanitarian–business partnerships in managing humanitarian operations and to help understand if donors' preferences align with their actual donation behavior. Design/methodology/approach: Choice-based conjoint analysis was used to understand donation preferences for partnership designs, and a donation experiment was performed using real money to understand the alignment of donors' preferences with actual donation behavior. Findings: The results show that partnering with the business sector can be a valuable asset for humanitarian organizations in attracting individual donors if these partnerships are managed well in terms of partnership strategy, partnership history and partnership report and disclosure. In particular, the study finds that the donation of services and products from businesses corporations to humanitarian organizations are preferable to individual donors, rather than cash. Furthermore, donors' preferences are not necessarily aligned with actual donation behavior. Practical implications: The results highlight the importance of presenting objective data on projects to individual donors. The results also show that donors value the provision of services and products by business corporations to humanitarian operations. Originality/value: Partnerships between humanitarian organizations and business corporations are important for the success of humanitarian operations. However, little is known about which partnership designs are most preferable to individual donors and have the biggest chance of being supported financially. [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]

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]

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]

In this paper, we consider multi-criteria decision-making problems of selecting the best decision alternative in which the preferences of the decision maker are given in the form of quasi-orders. If the decision maker's preferences are modeled by a family of complete quasi-orders (in particular, by a parametric family of additive value functions) then a common approach to solving such problems is based on the notion of potential optimality. In this paper, we consider the case in which the decision maker's preferences are modeled by a family of incomplete quasi-orders. We suggest a new approach to selecting decision alternatives by introducing the notion of potential nondominated alternatives. Such alternatives can be regarded as candidates for the best alternative. We investigate properties of the sets of potential nondominated alternatives and, for the case of a parametric family of partial quasi-orders, we develop a method of their identification. Our development is illustrated by numerical examples. [ABSTRACT FROM AUTHOR]

Copyright of Journal of General Health Sciences (JGEHES) is the property of Journal of General Health Sciences (JGEHES) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A significant proportion of the world's population has no access to grid-based electricity and so relies on off-grid lighting solutions. Rechargeable lamp technology is gaining popularity as an alternative off-grid lighting model in developing countries. In this paper, we explore consumer behavior and the operational inefficiencies that result under this model. Specifically, we are interested in (i) measuring the impact of inconvenience (of traveling to recharge the lamp) along with the impact of liquidity constraints (because of poverty) on lamp usage and (ii) evaluating the efficacy of strategies that address these factors. We build a structural model of consumers' recharge decisions that incorporates several operational features of the low-income regions. We conducted large-scale field experiments in Rwanda in partnership with a local rechargeable lamp operator and use the resultant data to estimate and test our model. We find that the complete removal of inconvenience and liquidity constraints from the current business model results in 73% and 126% increases in both recharges and revenue, thereby suggesting that these constraints are major sources of inefficiency. By implementing simple operations-based strategies—such as starting more recharge centers, visiting consumers periodically to collect their lamps for recharge, and allowing consumers to partially recharge their lamps and pay flexibly for the recharge—more than half the benefit of completely eliminating the inefficiencies can be attained. By contrast, the price- and capacity-based strategies that vary the economic variables (i.e., the amount paid per recharge and the amount of light obtained in return) but not the operational model perform far worse than the aforementioned strategies. Overall, our analysis emphasizes the importance of managing operations effectively even in markets with cash-constrained consumers, in which firms may have a natural tendency to focus more on reducing prices. This paper was accepted by Vishal Gaur, operations management. Funding: This work was supported by the International Growth Centre; Wharton School, University of Pennsylvania; and Institut Européen d'Administration des Affaires; The Environment and Energy Partnership; Grand Challenges Canada (Stars in Global Health); INSEAD's Emerging Markets Institute; INSEAD's Randomized Controlled Trials Laboratory; the INSEAD–Wharton Alliance, and International Growth Centre. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4844. [ABSTRACT FROM AUTHOR]

Background: Laboratory networks provide services through onsite testing or through specimen transport to higher-tier laboratories. This decision is based on the interplay of testing characteristics, treatment characteristics, and epidemiological characteristics. Objectives: Our objective was to develop a generalizable model using the threshold approach to medical decision making to inform test placement decisions. Methods: We developed a decision model to compare the incremental utility of onsite versus send-out testing for clinical purposes. We then performed Monte Carlo simulations to identify the settings under which each strategy would be preferred. Tuberculosis was modeled as an exemplar. Results: The most important determinants of the decision to test onsite versus send-out were the clinical utility lost due to send-out testing delays and the accuracy decrement with onsite testing. When the sensitivity decrements of onsite testing were minimal, onsite testing tended to be preferred when send-out delays reduced clinical utility by >20%. By contrast, when onsite testing incurred large reductions in sensitivity, onsite testing tended to be preferred when utility lost due to delays was >50%. The relative cost of onsite versus send-out testing affected these thresholds, particularly when testing costs were >10% of treatment costs. Conclusions: Decision makers can select onsite versus send-out testing in an evidence-based fashion using estimates of the percentage of clinical utility lost due to send-out delays and the relative accuracy of onsite versus send-out testing. This model is designed to be generalizable to a wide variety of use cases. Highlights: The design of laboratory networks, including the decision to place diagnostic instruments at the point-of-care or at higher tiers as accessed through specimen transport, can be informed using the threshold approach to medical decision making. The most important determinants of the decision to test onsite versus send-out were the clinical utility lost due to send-out testing delays and the accuracy decrement with onsite testing. The threshold approach to medical decision making can be used to compare point-of-care testing accuracy decrements with the lost utility of treatment due to send-out testing delays. The relative cost of onsite versus send-out testing affected these thresholds, particularly when testing costs were >10% of treatment costs. [ABSTRACT FROM AUTHOR]

Open in new tab Download slide OBJECTIVES Randomized controlled trials are the gold standard for evidence generation in medicine but are limited by their real-world generalizability, resource needs, shorter follow-up durations and inability to be conducted for all clinical questions. Decision analysis (DA) models may simulate trials and observational studies by using existing data and evidence- and expert-informed assumptions and extend analyses over longer time horizons, different study populations and specific scenarios, helping to translate population outcomes to patient-specific clinical and economic outcomes. Here, we present a scoping review and methodological primer on DA for cardiac surgery research. METHODS A scoping review was performed using the PubMed/MEDLINE, EMBASE and Web of Science databases for cardiac surgery DA studies published until December 2021. Articles were summarized descriptively to quantify trends and ascertain methodological consistency. RESULTS A total of 184 articles were identified, among which Markov models (N  = 92, 50.0%) were the most commonly used models. The most common outcomes were costs (N  = 107, 58.2%), quality-adjusted life-years (N  = 96, 52.2%) and incremental cost-effectiveness ratios (N  = 89, 48.4%). Most (N  = 165, 89.7%) articles applied sensitivity analyses, most frequently in the form of deterministic sensitivity analyses (N  = 128, 69.6%). Reporting of guidelines to inform the model development and/or reporting was present in 22.3% of articles. CONCLUSION DA methods are increasing but remain limited and highly variable in cardiac surgery. A methodological primer is presented and may provide researchers with the foundation to start with or improve DA, as well as provide readers and reviewers with the fundamental concepts to review DA studies. [ABSTRACT FROM AUTHOR]

Purpose: The main purpose of this study is to present a new approach to managing process changes in uncertain conditions. The proposed approach is applied in one of the largest production companies in Turkey to manage the changes in their warehouse processes which formed after the merger. Design/methodology/approach: In the model, interval-valued hesitant fuzzy the decision-making trial and evaluation laboratory (IVHF-DEMATEL) methodology is integrated into one of the most used BPR tools, change matrix. The main focus of the proposed model is to increase both flexibility and applicability in uncertain conditions. Thus, while the change matrix enables companies to be agile and responsive to changes, IVHF-DEMATEL provides a better way to continuously evaluate and determine critical processes, and strategies to align with evolving conditions. Findings: Initial analysis revealed two major problems, the slowness of shipments caused by the increase in costs and the confusion in the organizational structure. However, the conventional methods fall short of effectively determination of critical objectives in terms of dealing with uncertainty. Therefore, a comprehensive roadmap for managing the change is developed with the integration of IVHF-DEMATEL and change matrix so that a successful transition is achieved. Originality/value: It is believed that the study will contribute to the existing literature by providing a novel approach in which the IVHF-DEMATEL methodology is integrated into the change matrix. Also, the study provides a guideline for practical applications by presenting a step-by-step implementation of the model. [ABSTRACT FROM AUTHOR]

As algorithmic decision making is increasingly deployed in every walk of life, many researchers have raised concerns about fairness-related bias from such algorithms. But there is little research on harnessing psychometric methods to uncover potential discriminatory bias inside decision-making algorithms. The main goal of this article is to propose a new framework for algorithmic fairness based on differential item functioning (DIF), which has been commonly used to measure item fairness in psychometrics. Our fairness notion, which we call differential algorithmic functioning (DAF), is defined based on three pieces of information: a decision variable, a "fair" variable, and a protected variable such as race or gender. Under the DAF framework, an algorithm can exhibit uniform DAF, nonuniform DAF, or neither (i.e., non-DAF). For detecting DAF, we provide modifications of well-established DIF methods: Mantel–Haenszel test, logistic regression, and residual-based DIF. We demonstrate our framework through a real dataset concerning decision-making algorithms for grade retention in K–12 education in the United States. [ABSTRACT FROM AUTHOR]

Background: Non-alcoholic steatohepatitis (NASH) is one of the leading indications for liver transplantation (LT) in the United States. As with the current obesity epidemic, the incidence of NASH continues to rise. However, the impact of broad utilization of bariatric surgery (BS) for patients with NASH is unknown, particularly in regard to mitigating the need for LT. Methods: Markov decision analysis was performed to simulate the lives of 20,000 patients with obesity and concomitant NASH who were deemed ineligible to be waitlisted for LT unless they achieved a body mass index (BMI) < 35 kg/m2. Life expectancy following medical weight management (MWM) and sleeve gastrectomy (SG) were estimated. Base case patients were defined as having NASH without fibrosis and a pre-intervention BMI of 45 kg/m2. Sensitivity analysis of initial BMI was performed. Results: Simulated base case analysis patients who underwent SG gained 14.3 years of life compared to patients who underwent MWM. One year after weight loss intervention, 9% of simulated MWM patients required LT compared to only 5% of SG patients. Survival benefit for SG was observed above a BMI of 32.2 kg/m2. Conclusion: In this predictive model of 20,000 patients with obesity and concomitant NASH, surgical weight loss is associated with a reduction in the progression of NASH, thereby reducing the need for LT. A reduced BMI threshold of 32 kg/m2 for BS may offer survival benefit for patients with obesity and NASH. [ABSTRACT FROM AUTHOR]

Background: Current methods for developing clinical practice guidelines have several limitations: they are characterised by the "black box" operation—a process with defined inputs and outputs but an incomplete understanding of its internal workings; they have "the integration problem"—a lack of framework for explicitly integrating factors such as patient preferences and trade‐offs between benefits and harms; they generate one recommendation at a time that typically are not connected in a coherent analytical framework; and they apply to "average" patients, while clinicians and their patients seek advice tailored to individual circumstances. Methods: We propose augmenting the current guideline development method by converting evidence‐based pathways into fast‐and‐frugal decision trees (FFTs) and integrating them with generalised decision curve analysis to formulate clear, individualised management recommendations. Results: We illustrate the process by developing recommendations for the management of heparin‐induced thrombocytopenia (HIT). We converted evidence‐based pathways for HIT, developed by the American Society of Hematology, into an FFT. Here, we consider only thrombotic complications and major bleeding. We leveraged the predictive potential of FFTs to compare the effects of argatroban, bivalirudin, fondaparinux, and direct oral anticoagulants (DOACs) using generalised decision curve analysis. We found that DOACs were superior to other treatments if the FFT‐predicted probability of HIT exceeded 3%. Conclusions: The proposed analytical framework connects guidelines, pathways, FFTs, and decision analysis, offering risk‐tailored personalised recommendations and addressing current guideline development critiques. [ABSTRACT FROM AUTHOR]

This paper presents a decision-support methodology to support the development and assessment of military asset and resource strategies. The methodology is built around a system dynamics model that allows users to examine the performance of a strategy over time. The novelty of the model lies in its flexibility and ability to address questions about asset planning from both holistic and lifecycle viewpoints. From the user perspective, the decision-support methodology is structured around three phases: (1) Design an asset management strategy, (2) Evaluate strategy using system dynamics simulation model, and (3) Generate performance indicators and analyse results. The methodology is developed and used in a real case study to support the modernisation of the Australian Defence Force. This paper demonstrates that system dynamics offers useful methods to study the dynamics of supply-demand, and support the development of systemic asset and resource management strategies. [ABSTRACT FROM AUTHOR]

Some e‐platforms such as Amazon and JD.com are increasingly active in financing business, but theoretical research on this business is not comprehensive enough. This paper uses game theory model to compare the equilibrium of e‐platform financing (EPF) and bank financing (BF) in terms of pricing, quality, suppliers' competition, participants' profits, and consumer welfare. We find that a monopolistic supplier can always enjoy a lower loan interest rate and borrow more loans to improve production quantity or quality under EPF, which benefits to the participants and consumers. Interestingly, the e‐platform provides free loans and only profits from commission fee under some conditions. When there are two suppliers competing for the market share, the absolute advantage of EPF will change. If the supply chain or market environment is bad (production cost or commission rate is high; the potential market or initial budget is low), the e‐platform will offer lower interest rates than bank to reduce the strength gap between the suppliers; thus, the capital‐constrained supplier prefers EPF, which shows that the EPF business is an effective means to regulate upstream competition. However, when there are more competitive suppliers, the preferences of the suppliers with less initial capital are the same as the scenario of the two competitive suppliers, while the preference of the supplier with more initial capital is just the opposite. Finally, if the commission rate is endogenous, we demonstrate that the e‐platform will offer free loans but charge a relatively high commission fee. [ABSTRACT FROM AUTHOR]

This paper investigates the Indoor Environmental Quality (IEQ) factors influencing occupant satisfaction in commercial buildings in Iran, contrasting the views of building experts (architects and engineers) with those of building occupants. Employing the fuzzy analytical hierarchy process (FAHP), this study focuses on the four primary IEQ factors: thermal comfort, indoor air quality, visual comfort, and acoustic comfort. The study aims to bridge the gap between expert evaluations and occupant perceptions of IEQ factors in commercial buildings in Iran. By examining the disparities in prioritising IEQ factors between these two groups, the study sheds light on the complexities of IEQ assessment and highlights the importance of considering diverse perspectives in optimising indoor environments. Our methodology includes a survey conducted among 30 building experts (15 architects and 15 building engineers) and 102 occupants, employing FAHP to derive the relative importance weights of each IEQ factor. The results highlight significant disparities between architects, engineers, and occupants in prioritising these factors. Architects emphasise visual comfort (42%), while engineers and occupants view thermal comfort (53% and 41%) as the most crucial factor for occupant satisfaction. The study underscores the complexity of IEQ in commercial buildings and the diverse perspectives influencing its assessment. It contributes to the broader discourse on optimising IEQ, emphasising the need for a comprehensive approach that encompasses both technical expertise and occupant experience. [ABSTRACT FROM AUTHOR]

The free rider problem is widely observed in investments in the common good. For example, if Best Buy offers corporate social responsibility (CSR) programs, such as recycling, tech training, and supplier audit, at its own expense, other firms in the industry can benefit from these programs at no cost (i.e., they become the free riders). Such a free rider effect often manifests itself as a mixed strategy equilibrium, in which potential investors unnecessarily delay their investments. The article "Investment in the common good: Free rider effect and the stability of mixed strategy equilibria" finds that recurring investment opportunities can be a major driver of delays in investment in the common good. It also shows that sufficient heterogeneity in investment cost among potential investors can alleviate the free rider effect. Therefore, the policy makers may try to facilitate investments in public goods (e.g., CSR activities) through some form of selective benefits to potential investors. In the game of investment in the common good, the free rider problem can delay the stakeholders' actions in the form of a mixed strategy equilibrium. However, it has been recently shown that the commonly known form of mixed strategy equilibria of the stochastic war of attrition is destabilized by even the slightest degree of asymmetry between the players. Such extreme instability is contrary to the widely accepted notion that a mixed strategy equilibrium is the hallmark of the war of attrition. Motivated by this quandary, we search for a mixed strategy equilibrium in a stochastic game of investment in the common good. Our results show that, despite asymmetry, a mixed strategy equilibrium exists if the model takes into account the repeated investment opportunities. This class of mixed strategy equilibria disappear only if the asymmetry is sufficiently high. Because the mixed strategy equilibrium is less efficient than pure strategy equilibria, it behooves policy makers to prevent it by promoting a sufficiently high degree of asymmetry between the stakeholders through, for example, asymmetric subsidy. Funding: Y. Kim gratefully acknowledges the financial support from the ISM department in the Culverhouse College of Business. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.2371. [ABSTRACT FROM AUTHOR]

Manufacturing industry is the material foundation of national economy, the traditional supply chain in a single value creation mode makes the manufacturing industry in the global value chain competition in the "low-end lock" predicament, to create a highly efficient, intelligent supply chain, is the manufacturing industry in the global competition to gain an advantage in the key. In the process of "Digital to Intelligent Reform", the matching mechanism and decision-making method of supply chain heterogeneity partner selection is the focus of manufacturing supply chain transformation to be solved urgently. This paper takes the manufacturers and suppliers of manufacturing secondary supply chain as the research object, takes service quality as the important influence factor of partner selection, and explores the supply chain partner matching evaluation model under the three modes of unilateral decision-making, bilateral decision-making, and optimization of bilateral decision-making. The study shows that the bilateral matching decision-making mechanism based on QoS can obtain a higher matching completion rate, while digital transformation helps to improve the efficiency and stability of supply chain partner selection. The study can provide a decision basis for heterogeneous partner selection in manufacturing supply chain, and then improve the overall performance of manufacturing supply chain. [ABSTRACT FROM AUTHOR]

Air pollution affects public health and the environment, creating great concern in developed and developing countries. In India, there are numerous reasons for air pollution, and festivals like Diwali also contribute to air contamination. Determining the polluted region using several air contaminants is significant and should be analyzed carefully. This study aims to analyze the air quality in Tamil Nadu, India, during the Diwali festival from 2019 to 2021, based on multiple air pollutants. The study models the impact of air pollution as a Multi-Attribute Decision-Making (MADM) problem. It introduces a hybrid approach, namely the Analytical Hierarchy Process-Entropy-VlseKriterijumska Optimizacija I Kompromisno Resenje (AHPEntropy-VIKOR) model, to analyze and rank the areas based on the quality of air. A combined approach of AHP and entropy is employed to determine the weights of multiple air pollutants. The VIKOR approach ranks the areas and identifies the areas with the worst air quality during the festival. The proposed model is validated by performing the Spearman's rank correlation with two existing MADM methods: Combinative Distance Based Assessment (CODAS) and Weighted Aggregates Sum Product Assessment (WASPAS). Sensitivity analysis is carried out to assess the effects of the priority weights and the dependency of the pollutants in ranking the regions. The highest air pollution level during the festival was seen in Cellisini Colony (2019), Rayapuram (2020), T. Nagar and Triplicane (2021) in their respective year. The results demonstrate the consistency and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Ecological restoration is considered an essential activity as we attempt to repair anthropogenic degradation. Yet, resources are limited and it is important that efforts focus on activities that are effective and yield successful restoration. Structured decision making (SDM) is an organized framework that is designed to incorporate differing values across stakeholders and evaluate alternatives. The SDM framework typically consists of six steps: define the decision problem, define objectives and evaluation criteria, develop alternatives, estimate consequences, evaluate trade-offs, and decide, implement, and monitor. Here, we posit that SDM is well suited for ecological restoration, yet remains underused. Specifically, tools such as stakeholder surveys, conceptual modeling, and multi-criteria decision analysis are notably useful in ecological restoration and can be applied under the SDM framework to ensure robust and transparent decision making. We illustrate the application of SDM to ecological restoration with case studies that used SDM alongside ecosystem service assessments, for species-as-risk management, and to assess action desirability across large and diverse stakeholder groups. Finally, we demonstrate how SDM is equipped to handle many of the challenges associated with ecological restoration by identifying commonalities. We contend that increased use of SDM for ecological restoration by environmental managers has the potential to yield wise use of limited resources and more effective restoration outcomes. [ABSTRACT FROM AUTHOR]

Copyright of Water Conservancy Science & Techonlogy & Economy is the property of Water Conservancy Science & Technology & Economy Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we consider an optimal reinsurance contract under a mean-variance criterion in a Stackelberg game theoretical framework. The reinsurer is the leader of the game and decides on an optimal reinsurance premium to charge, while the insurer is the follower of the game and chooses an optimal per-loss reinsurance to purchase. The objective of the insurer is to maximize a given mean-variance criterion, while the reinsurer adopts the role of social planner balancing its own interests with those of the insurer. That is, we assume that the reinsurer determines the reinsurance premium by maximizing a weighted sum of the insurer's and reinsurer's mean-variance criteria. Under the general mean-variance premium principle, we derive the optimal reinsurance contract by solving the extended Hamilton–Jacobi–Bellman (HJB) systems. Moreover, we provide an intuitive way to set the weight of each party in the reinsurer's objective. Finally, we consider some special cases to illustrate our main results. [ABSTRACT FROM AUTHOR]

Wildlife disease management decisions often require rapid responses to situations that are fraught with uncertainty. By recognizing that management is implemented to achieve specific objectives, resource managers and science partners can identify an analysis technique and develop a monitoring plan to evaluate management effectiveness. For emerging infectious diseases, objectives may take several distinct forms, dependent on the perceived stage of disease emergence (i.e., pre‐epidemic, early outbreak, mid‐epidemic, and endemic), the expected rate of spread, and the anticipated effect of the disease on host populations. Identifying modeling techniques and metrics that are linked to management objectives will require early and consistent communication between managers and science partners. We link modeling approaches that can be used to forecast and evaluate the performance of intervention strategies with a range of disease management objectives. Our aim is to help scientists recognize alternative modeling approaches which may better align with different forms of disease management objectives, and to help managers evaluate the relevance of proposed modeling approaches to their specified objectives for disease management. Recognizing that disease management objectives can take different forms, and thus require different modeling approaches, can help wildlife disease response teams (i.e., natural resource managers, scientists, and stakeholders working collaboratively) better prepare and respond to disease threats. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the relative live birth rate and net cost difference between mosaic embryo transfer and an additional cycle of IVF with PGT-A for patients whose only remaining embryos are non-euploid. Methods: A decision analytic model was designed with model parameters varying based on discrete age cutoffs (<35, 35–37, 38–39, 40–42, 43–44, >44). Model inputs included probabilities of successful IVF, clinical pregnancy, and live birth as well as costs of IVF with PGT-A, embryo transfer, live birth, amniocentesis, and dilation and curettage. All costs were modeled from the healthcare system perspective and adjusted for inflation to 2023 $USD. Model outcomes were sub-stratified by degree and type of mosaicism. Results: For patients younger than 43, an additional cycle of IVF with PGT-A resulted in a higher relative live birth rate (<35, +20%; 35–37, +15%; 38–39, +17%; 40–42, +6%; average, +14.5%) compared to mosaic embryo transfer with an average additional cost of $16,633. For patients older than 42, mosaic embryo transfer resulted in a higher live birth rate (43–44, +5%; >44, +3%; average, +4%) while on average costing $9572 less than an additional cycle of IVF with PGT-A. Conclusion: Mosaic embryo transfers are a superior alternative to an additional cycle of IVF with PGT-A for patients older than 42 whose only remaining embryos are non-euploid. Mosaic embryo transfers also should be considered for patients younger than 42 who are unable to pursue additional autologous IVF cycles. Counseling and care should be personalized to individual patients and embryos. [ABSTRACT FROM AUTHOR]

With the application of advanced technology, smart logistics ecological cooperation is in development, characterised by data sharing and platform empowerment. In order to explore the trend of ecological cooperation between the logistics platform and the supplier from the perspective of business ecosystem, this study uses evolutionary game theory to describe the multi-period game between the two parties, and obtains the equilibrium where the two parties both choose ecological cooperation and its preconditions. Through sensitivity analysis, we obtain the specific paths to convert other evolutionary stable strategies (ESSs) into the ideal ESS by adjusting certain parameters, and provide suggestions for the platform to induce its supplier to choose ecological cooperation. The platform should charge a reasonable agency fee to reach the ecological cooperation. The relationship between the agency fee, empowerment cost, and service price elasticity coefficient determines the platform's ultimate evolutionary stable strategy. This study finds the butterfly effect of ecological cooperation. There are different combinations of the initial probabilities between the two parties making them both choose ecological cooperation or both not choose under the same condition. [ABSTRACT FROM AUTHOR]

Identifying conservation actions to recover threatened species can be challenging due to many ecological uncertainties. For example, major threats to a species’ conservation are commonly known or suspected, but the specific impacts on population or metapopulation dynamics can be uncertain. This is frequently the case with emerging infectious diseases, including chytridiomycosis, a global driver of amphibian population declines caused by the fungal pathogens Batrachochytrium dendrobatidis (Bd) and Batrachochytrium salamandrivorans. While these diseases are known to cause amphibian declines and extirpations, the mechanisms of their landscape-scale spread are still largely unknown. Such uncertainty can lead to inaction which may jeopardize timely recovery of a species. Decision analysis is a pragmatic approach to making transparent and defensible decisions while dealing with uncertainties. We investigated whether optimal actions aimed at recovering boreal toad (Anaxyrus boreas boreas) metapopulations in the southern Rocky Mountains are robust to the unknown dynamics of Bd spread using value of information and regret analyses. Value of information is a decision-analytic tool for calculating the value of new information in terms of performance on management objectives, while regret measures the cost of acting under incorrect information. We further conducted a stochastic sensitivity analysis to identify the relative effects of metapopulation parameters on system dynamics. We found optimal actions were robust to the unknown dynamics of Bd spread. While boreal toad breeding occurrence is highly sensitive to Bd distribution, the optimal decision is not. Resolving the unknown dynamics of Bd spread would lead to a minimal gain of less than one breeding toad subpopulation at the end of 50 years, given the currently available management actions. Applying a decision-analytic framework coupled with value of information and regret analyses can help frame how uncertainties affect decisions in a way that empowers decision makers. [ABSTRACT FROM AUTHOR]

Individuals with a family history of colorectal cancer (CRC) may benefit from early screening with colonoscopy or immunologic fecal occult blood testing (iFOBT). We systematically evaluated the benefit‐harm trade‐offs of various screening strategies differing by screening test (colonoscopy or iFOBT), interval (iFOBT: annual/biennial; colonoscopy: 10‐yearly) and age at start (30, 35, 40, 45, 50 and 55 years) and end of screening (65, 70 and 75 years) offered to individuals identified with familial CRC risk in Germany. A Markov‐state‐transition model was developed and used to estimate health benefits (CRC‐related deaths avoided, life‐years gained [LYG]), potential harms (eg, associated with additional colonoscopies) and incremental harm‐benefit ratios (IHBR) for each strategy. Both benefits and harms increased with earlier start and shorter intervals of screening. When screening started before age 50, 32‐36 CRC‐related deaths per 1000 persons were avoided with colonoscopy and 29‐34 with iFOBT screening, compared to 29‐31 (colonoscopy) and 28‐30 (iFOBT) CRC‐related deaths per 1000 persons when starting age 50 or older, respectively. For iFOBT screening, the IHBRs expressed as additional colonoscopies per LYG were one (biennial, age 45‐65 vs no screening), four (biennial, age 35‐65), six (biennial, age 30‐70) and 34 (annual, age 30‐54; biennial, age 55‐75). Corresponding IHBRs for 10‐yearly colonoscopy were four (age 55‐65), 10 (age 45‐65), 15 (age 35‐65) and 29 (age 30‐70). Offering screening with colonoscopy or iFOBT to individuals with familial CRC risk before age 50 is expected to be beneficial. Depending on the accepted IHBR threshold, 10‐yearly colonoscopy or alternatively biennial iFOBT from age 30 to 70 should be recommended for this target group. [ABSTRACT FROM AUTHOR]

This work aims to answer the natural question of how probable it is that a given method produces rank reversal in a priority vector (PV) if a decision maker (DM) introduces perturbations to the pairwise comparison matrix (PCM) under concern. We focus primarily on the concept of robustness against rank reversal, independent of specific methods, and provide an in-depth statistical insight into the application of the Monte Carlo (MC) approach in this context. This concept is applied to three selected methods, with a special emphasis on scenarios where a method may not provide outputs for all possible PCMs. All results presented in this work are replicable using our open-source implementation. [ABSTRACT FROM AUTHOR]

Incomplete pairwise comparison matrices contain some missing judgements. A natural approach to estimate these values is provided by minimising a reasonable measure of inconsistency after unknown entries are replaced by variables. Two widely used inconsistency indices for this purpose are Saaty's inconsistency index and the geometric inconsistency index, which are closely related to the eigenvector and the logarithmic least squares priority deriving methods, respectively. The two measures are proven to imply the same optimal filling for incomplete pairwise comparison matrices up to order four but not necessarily for order at least five. [ABSTRACT FROM AUTHOR]