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In recent years, the state of Colorado has experienced extreme wildfire events that have degraded forest and watershed health and devastated human communities. With expanding human development and a changing climate, wildfire activity is likely to increase, and wildfire management agencies will be challenged to sustain landscapes and the ecosystem services they provide. A critical element of the United States' federal-, state-, and local-level multiagency wildfire response is the interagency dispatching system, which facilitates the ordering, mobilization, and tracking of firefighting resources to and from wildfire incidents—a role that is likely to increase in both importance and workload in the future. Given increasing demands, it is worth considering ways to improve efficiencies, capacity, and capability within the current Colorado dispatching system. With this, the Rocky Mountain Coordinating Group (RMCG) and the Rocky Mountain Area Fire Executive Council (RMA-FEC) sought to reorganize the dispatching system, beginning with exploration of changes to dispatching zone boundaries and the number and location of dispatching centers throughout the state. Here we describe a multiyear research–management partnership with the RMCG and RMA-FEC to apply a structured decision-making process to guide this reorganization effort. We highlight the steps used in a participatory process that involved local decision makers and included iteratively revising and clarifying the problem statement, developing objectives and translating them into measurable attributes, building a multiobjective optimization model to generate and compare alternatives, and communicating a recommended alternative that was ultimately adopted. To conclude, we discuss insights from our experience and highlight opportunities for similar work to support efficient wildfire management elsewhere in the United States. History: This paper has been accepted for the Decision Analysis Special Issue on Decision Analysis to Advance Environmental Sustainability. Funding: This research was supported by the U.S. Department of Agriculture Forest Service. [ABSTRACT FROM AUTHOR]

This paper uses an analytical framework to examine a firm's investment and switching strategy under uncertainty. The context is the possibility to launch and operate two distinct projects, one at a time, with exposure to a stochastic exogenous price. We allow for multiple switches between the two projects, along with abandonment options from each. These possibilities fundamentally influence the operational strategy. We show that under some conditions, a dichotomous waiting region may arise at the investment stage. In this case we have an inaction region, for a range of prices in a certain bounded interval, where the firm does not invest and waits to have more information about the price evolution. This region vanishes for a high level of uncertainty. Additionally, the firm may operate with a negative instantaneous profit. We prove that investment in this region is never optimal. Numerical examples enable comparative statics, while extension to allow for time-to-build is included. [ABSTRACT FROM AUTHOR]

Planning for community resilience through public infrastructure projects often engenders problems associated with social dilemmas, but little work has been done to understand how individuals respond when presented with opportunities to invest in such developments. Using statistical learning techniques trained on the results of a web‐based common pool resource game, we analyze participants' decisions to invest in hypothetical public infrastructure projects that bolster their community's resilience to disasters. Given participants' dispositions and in‐game circumstances, Bayesian additive regression tree (BART) models are able to accurately predict deviations from players' decisions that would reasonably lead to Pareto‐efficient outcomes for their communities. Participants tend to overcontribute relative to these Pareto‐efficient strategies, indicating general risk aversion that is analogous to individuals purchasing disaster insurance even though it exceeds expected actuarial costs. However, higher trait Openness scores reflect an individual's tendency to follow a risk‐neutral strategy, and fewer available resources predict lower perceived utilities derived from the infrastructure developments. In addition, several input variables have nonlinear effects on decisions, suggesting that it may be warranted to use more sophisticated statistical learning methods to reexamine results from previous studies that assume linear relationships between individuals' dispositions and responses in applications of game theory or decision theory. [ABSTRACT FROM AUTHOR]

Abstract 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 Background Demonstrating safety and efficacy of new medical treatments requires clinical trials but clinical trials are costly and may not provide value proportionate to their costs. As most health systems have limited resources, it is therefore important to identify the trials with the highest value. Tools exist to assess elements of a clinical trial such as statistical validity but are not wholistic in their valuation of a clinical trial. This study aims to develop a measure of clinical trials value and provide an online tool for clinical trial prioritisation. Methods A search of the academic and grey literature and stakeholder consultation was undertaken to identify a set of criteria to aid clinical trial valuation using multi-criteria decision analysis. Swing weighting and ranking exercises were used to calculate appropriate weights of each of the included criteria and to estimate the partial-value function for each underlying metric. The set of criteria and their respective weights were applied to the results of six different clinical trials to calculate their value. Results Seven criteria were identified: ‘unmet need’, ‘size of target population’, ‘eligible participants can access the trial’, ‘patient outcomes’, ‘total trial cost’, ‘academic impact’ and ‘use of trial results’. The survey had 80 complete sets of responses (51% response rate). A trial designed to address an ‘Unmet Need’ was most commonly ranked as the most important with a weight of 24.4%, followed by trials demonstrating improved ‘Patient Outcomes’ with a weight of 21.2%. The value calculated for each trial allowed for their clear delineation and thus a final value ranking for each of the six trials. Conclusion We confirmed that the use of the decision tool for valuing clinical trials is feasible and that the results are face valid based on the evaluation of six trials. A proof-of-concept applying this tool to a larger set of trials with an external validation is currently underway.

Background: Given the increased attention to functional improvement in spine surgery as it relates to motion preservation, activities of daily living, and cost, it is critical to fully understand the healthcare economic impact of new devices being tested in large FDA randomized controlled trials (RCT). The purpose of this analysis was to comprehensively evaluate the cost-effectiveness of the novel Total Posterior Spine (TOPS™) System investigational device compared with the trial control group, standard transforaminal lumbar interbody fusion (TLIF). Objective: To evaluate the cost-effectiveness of TOPS™ compared with TLIF. Methods: The study patient population was extracted from a multicenter RCT with current enrollment at n=121 with complete 1-year follow-up. The primary outcome was cost-effectiveness, expressed as the incremental cost-effectiveness ratio. Secondary outcomes were health-related utility, presented as quality-adjusted life-years (QALYs), and cost, calculated in US dollars. Analysis was conducted following Second Panel on Cost-Effectiveness Health and Medicine recommendations. The base case analysis utilized SF-36 survey data from the RCT. Both cost and QALY outcomes were discounted at a yearly rate of 3% to reflect their present value. A cohort Markov model was constructed to analyze perioperative and postoperative costs and QALYs for both TOPS™ and control groups. Scenario, probabilistic, and threshold sensitivity analyses were conducted to determine model discrimination and calibration. Results: The primary time horizon used to estimate cost and health utility was 2 years after index surgery. From a health system perspective, assuming a 50/50 split between Medicare and private payers, the TOPS™ cohort is cost-effective 2 years postoperatively ($6158/QALY) compared with control. At 6 years and beyond, TOPS™ becomes dominant, irrespective of payer mix and surgical setting. At willingness-to-pay thresholds of $100 000/QALY, 63% of all 5000 input parameter simulations favor TOPS, even with a $4000 upcharge vs TLIF. Discussion: The novel TOPS™ device is cost-effective compared with TLIF and becomes the dominant economic strategy over time. Conclusions: In the emerging, rapidly expanding field of value-based medicine, there will be an increased demand for these analyses, ensuring surgeons are empowered to make the best, most sustainable solutions for their patients and society.

Biochemical recurrence (BCR) following radical prostatectomy (RP) has a limited ability to predict prostate cancer (PC) progression, leading to overtreatment, decreased quality of life, and additional expenses. Previously, we established that one-third of men with BCR in our group experienced low-risk recurrences that were safely observed without treatment. Our retrospective cohort analysis of 407 BCR patients post RP validates the use of PSA doubling time (DT) kinetics to direct active observation (AO) versus treatment following RP. The primary outcome was no need for treatment according to the predictive value of models of ROC analysis. The secondary outcome was PC-specific mortality (PCSM) according to Kaplan−Meier analysis. A total of 1864 men underwent RP (June 2002−September 2019); 407 experienced BCR (PSA > 0.2 ng/dL, ×2), with a median follow-up of 7.6 years. In adjusted regression analysis, initial PSADT > 12 months and increasing DT were significant predictors for AO (p < 0.001). This model (initial PSADT and DT change) was an excellent predictor of AO in ROC analysis (AUC = 0.83). No patients with initial PSADT > 12 months and increasing DT experienced PCSM. In conclusion, the combination of PSADT > 12 months and increasing DT was an excellent predictor of AO. This is the first demonstration that one-third of BCRs are at low risk of PCSM and can be managed without treatment via DT kinetics.

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.

Product Lifecycle Management (PLM) systems support industrial organisations in managing their product portfolios and related data across all phases of the product lifecycle. PLM seeks to enhance an organisation's ability to manage its product development activities and support collaboration across organisational functions and business units, and between organisations. Effective decision-making is vital for the successful management of products over their lifecycle. However, decision-making is an under-researched area in PLM. We argue that decision-making theory and group decision support concepts can be brought to bear to enhance PLM decision-making processes. We present and justify a set of six principles to support decision-making in a PLM context. The paper highlights the need to consider and capture decisions as distinct units of PLM knowledge to support product lifecycle management. We derive a generic information flow and a group decision support structure for PLM decision-making that encapsulates the six principles. Three industrial cases are analysed to illustrate the application and value of the principles in supporting decision-making. The principles enable PLM decisions to be codified, recorded, and reviewed. Decision-making processes can be reused where appropriate. The principles can support future innovations that may affect PLM, such as ontological and semantic reasoning and Artificial Intelligence. [ABSTRACT FROM AUTHOR]

Abstract Areas beyond national jurisdiction, or the high seas, are vital to life on Earth. However, the conservation of these areas, for example, through area‐based management tools (ABMTs), is challenging, particularly when accounting for global change. Using decision science, integrated population models, and a Critically Endangered seabird (Kuaka; Pelecanoides whenuahouensis) as a case study, we evaluated potential ABMTs in the high seas under global change and different governance structures, while accounting for uncertainty and imperfect compliance. Our study highlighted that global change in these areas will likely cause population declines of ∼60% by 2050. However, decisive conservation action could cost‐effectively address predicted declines, particularly when implemented as soon as possible and under the Biodiversity Beyond National Jurisdiction Treaty. We illustrate how decision science can transparently navigate a complex seascape of management decisions and we advocate for its wider integration in the management of the largest sections of our planet, the high seas.

The rapid economic growth has not only created more opportunities for the development of colleges and universities, but also presented many challenges. In this paper, by analyzing the construction of university budget implementation system, based on 5 stages, the recursive hierarchy of university performance budget is established by using hierarchical analysis method, and the hierarchy is solved by using recursive hierarchy matrix. A combination of balanced scorecard and key performance indicators is used. The performance budget evaluation index system for colleges and universities is constructed from the four levels of financial performance, business performance, budget performance and social performance. The equity realization method is used to study the financial fund performance of universities in the current period in the form of dividends, emptying behavior and selling shares. Based on the budget model, the impact of various factors on the shareholders’ dividend decision-making behavior is calculated according to the paradigm of “decision analysis framework - parameter change - decision change”. After the effective implementation of the performance project, the income of university A increased by 28.6% compared to the previous year, which laid a solid foundation for the development of the university.

The impacts of weather, climate variability and climate change on agricultural production underline the increasing importance of actionable agro-climatic services. Transitioning from supply-driven provision of climate and agricultural information to demand-driven agro-climate services (ACS) at scale cannot be accomplished in a top-down manner but requires the engagement of diverse stakeholders in all phases of ACS development and implementation. This requires methods and tools to handle the diversity and dynamics of interactions between relevant stakeholders, including during the pre-financing stage of the ACS. We propose a transparent method to identify and engage stakeholders in the ACS planning phase and demonstrate this method as part of the socio-economic development planning process in Dien Bien, Vietnam. We find that considering stakeholder attributes such as availability, experience, gender, expertise, benefits and costs for each stakeholder, interest, influence, relevance, and attitude, combined with insights about the socio-economic development planning processes, is crucial for the engagement of stakeholders. We also find that facilitating collaborative interaction between ACS stakeholders is pivotal in supporting the planning of demand-driven ACS. Our methodology for engaging stakeholders is transferrable to designing and planning other interventions in complex systems.

Abstract Effective biodiversity conservation requires robust and transparent prioritization of management actions. However, this is often hampered by a lack of spatially‐explicit data on habitat variables and empirical data on the effect of management actions. Although approaches exist that integrate structured expert elicitation (SEE) with species distribution models (SDMs) to encode species responses across habitat gradients, difficulties remain in predicting management outcomes under different settings, at a region‐wide scale when key habitat covariates are not spatially explicit. Therefore, we developed an approach to integrate SDMs with SEE to capture expert understanding of likely outcomes of management actions for individual frog species, and use this to spatially predict the effect of management actions. We demonstrate our approach across approximately 4000 wetlands in greater Melbourne, Victoria, Australia. As a measure of management effectiveness, we used the change in predicted probability of occurrence of seven frog species at wetlands 10 years after conservation actions are implemented (or not implemented). Management effect was elicited from experts under six scenarios. Individual expert estimates were aggregated using generalized linear models that were then used to spatially predict expected management effects, and a measure of uncertainty in the prediction, at all wetlands. Predicted management effect was strongly influenced by species initial probability of occurrence, with enhancing aquatic and surrounding vegetation an effective action for most species. We discuss practical challenges and recommend solutions in the integration of SDMs and SEE for the spatial prediction of management effect.

This study examines the DCMA concerning the future risk of the water security status. We considered three risk factors: population growth, economic growth, and natural water supply–demand differences. In the risk analysis part, we consulted with experts from several sectors including academia, Non-Governmental Organizations (NGOs), and industry, to predict that the probability of future water stresses in high-, medium-, and low-risk scenarios are 0.73, 0.24, and 0.03, respectively. In the decision analysis part, we adopted two multiple criteria decision analysis (MCDA) approaches that include multiple attribute value theory (MAVT) and analytic hierarchy process (AHP) methods to evaluate the best alternative decision to alleviate future water stresses in the DCMA. The sensitivity analysis demonstrates that, although expanding existing water reservation might be a solution to tackle the challenge, the best option really closely connects to the weighting scheme of the criteria considered in the framework. This study provides a valuable risk and decision analysis framework to analyze the water security status associated with the future water supply and demand gap decrease caused by three risk factors: population growth, climate change, and natural water supply. [ABSTRACT FROM AUTHOR]

Background: Demonstrating safety and efficacy of new medical treatments requires clinical trials but clinical trials are costly and may not provide value proportionate to their costs. As most health systems have limited resources, it is therefore important to identify the trials with the highest value. Tools exist to assess elements of a clinical trial such as statistical validity but are not wholistic in their valuation of a clinical trial. This study aims to develop a measure of clinical trials value and provide an online tool for clinical trial prioritisation. Methods: A search of the academic and grey literature and stakeholder consultation was undertaken to identify a set of criteria to aid clinical trial valuation using multi-criteria decision analysis. Swing weighting and ranking exercises were used to calculate appropriate weights of each of the included criteria and to estimate the partial-value function for each underlying metric. The set of criteria and their respective weights were applied to the results of six different clinical trials to calculate their value. Results: Seven criteria were identified: 'unmet need', 'size of target population', 'eligible participants can access the trial', 'patient outcomes', 'total trial cost', 'academic impact' and 'use of trial results'. The survey had 80 complete sets of responses (51% response rate). A trial designed to address an 'Unmet Need' was most commonly ranked as the most important with a weight of 24.4%, followed by trials demonstrating improved 'Patient Outcomes' with a weight of 21.2%. The value calculated for each trial allowed for their clear delineation and thus a final value ranking for each of the six trials. Conclusion: We confirmed that the use of the decision tool for valuing clinical trials is feasible and that the results are face valid based on the evaluation of six trials. A proof-of-concept applying this tool to a larger set of trials with an external validation is currently underway. [ABSTRACT FROM AUTHOR]

There are many ways to elicit honest probabilistic forecasts from experts. Once those forecasts are elicited, there are many ways to aggregate them into a single forecast. Should the choice of elicitation method inform the choice of aggregation method? In "From Proper Scoring Rules to Max-Min Optimal Forecast Aggregation," Neyman and Roughgarden establish a connection between these two problems. To every elicitation method they associate the aggregation method that improves as much as possible upon the forecast of a randomly chosen expert, in the worst case. This association maps the two most widely used elicitation methods (Brier and logarithmic scoring) to the two most well-known aggregation methods (linear and logarithmic pooling). The authors show a number of interesting properties of this connection, including a natural axiomatization of aggregation methods obtained through the connection, as well as an algorithm for efficient no-regret learning of expert weights. This paper forges a strong connection between two seemingly unrelated forecasting problems: incentive-compatible forecast elicitation and forecast aggregation. Proper scoring rules are the well-known solution to the former problem. To each such rule s, we associate a corresponding method of aggregation, mapping expert forecasts and expert weights to a "consensus forecast," which we call quasi-arithmetic (QA) pooling with respect to s. We justify this correspondence in several ways: QA pooling with respect to the two most well-studied scoring rules (quadratic and logarithmic) corresponds to the two most well-studied forecast aggregation methods (linear and logarithmic); given a scoring rule s used for payment, a forecaster agent who subcontracts several experts, paying them in proportion to their weights, is best off aggregating the experts' reports using QA pooling with respect to s, meaning this strategy maximizes its worst-case profit (over the possible outcomes); the score of an aggregator who uses QA pooling is concave in the experts' weights (as a consequence, online gradient descent can be used to learn appropriate expert weights from repeated experiments with low regret); and the class of all QA pooling methods is characterized by a natural set of axioms (generalizing classical work by Kolmogorov on quasi-arithmetic means). Funding: This work was supported by the Division of Computing and Communication Foundations [Grant CCF-1813188], the Army Research Office [Grant W911NF1910294], and the Division of Graduate Education [Grant DGE-2036197]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2022.2414. [ABSTRACT FROM AUTHOR]

Many sequential decision problems have a weakly coupled structure in that a set of linking constraints couples an otherwise independent collection of subproblems. This structure arises in a wide variety of applications, such as network revenue management, online advertising, assortment planning, interactive marketing, optimization of power systems, and multilocation inventory management to name only a few. Such problems can be modeled as dynamic programs but are quite difficult to solve. Two widely studied approximation methods are approximate linear programs, which involve finding a best approximation of total value that is additive across the subsystems, and Lagrangian relaxations, which involve relaxing the linking constraints. It is well known that both of these approaches provide upper bounds to the optimal value, and the approximate linear programming approach is a better bound but also, more difficult to compute. In this paper, we provide a detailed theoretical analysis of these two approximations and show that, under fairly broad conditions, these two approximations lead to upper bounds that are very close and often identical. Our theory suggests that, between these two approximations, Lagrangian relaxations should usually be the preferred choice for researchers studying applications involving weakly coupled dynamic programs. Many stochastic dynamic programs (DPs) have a weakly coupled structure in that a set of linking constraints in each period couples an otherwise independent collection of subproblems. Two widely studied approximations of such problems are approximate linear programs (ALPs), which involve optimizing value function approximations that additively separate across subproblems, and Lagrangian relaxations, which involve relaxing the linking constraints. It is well known that both of these approximations provide upper bounds on the optimal value function in all states and that the ALP provides a tighter upper bound in the initial state. The purpose of this short paper is to provide theoretical justification for the fact that these upper bounds are often close if not identical. We show that (i) for any weakly coupled DP, the difference between these two upper bounds—the relaxation gap—is bounded from above in terms of the integrality gap of the separation problems associated with the ALP. (ii) If subproblem rewards are uniformly bounded and some broadly applicable conditions on the linking constraints hold, the relaxation gap is bounded from above by a constant that is independent of the number of subproblems. (iii) When the linking constraints are independent of subproblem states and have a unimodular structure, the relaxation gap equals zero. The conditions for (iii) hold in several widely studied problems: generalizations of restless bandit problems, online stochastic matching problems, network revenue management problems, and price-directed control of relocating resources. These findings generalize and unify existing results. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2287. [ABSTRACT FROM AUTHOR]

Lead pipe remediation budgets are limited and ought to maximize public health impact. This goal implies a nontrivial optimization problem; lead service lines connect water mains to individual houses, but any realistic replacement strategy must batch replacements at a larger scale. Additionally, planners typically lack a principled method for comparing the relative public health value of potential interventions and often plan projects based on nonhealth factors. This paper describes a simple process for estimating child health impact at a parcel level by cleaning and synthesizing municipal datasets that are commonly available but seldom joined due to data quality issues. Using geocoding as the core record linkage mechanism, parcel‐level toxicity data can be combined with school enrollment records to indicate where young children and lead lines coexist. A harm metric of estimated exposure‐years is described at the parcel level, which can then be aggregated to the project level and minimized globally by posing project selection as a 0/1 knapsack problem. Simplifying for use by nonexperts, the implied linear programming relaxation is solved with the greedy algorithm; ordering projects by benefit cost ratio produces a priority list that planners can then consider holistically alongside harder to quantify factors. A case study demonstrates the successful application of this framework to a small U.S. city's existing data to prioritize federal infrastructure funding. [ABSTRACT FROM AUTHOR]

Let ρ be a general law-invariant convex risk measure, for instance, the average value at risk, and let X be a financial loss, that is, a real random variable. In practice, either the true distribution μ of X is unknown, or the numerical computation of ρ (μ) is not possible. In both cases, either relying on historical data or using a Monte Carlo approach, one can resort to an independent and identically distributed sample of μ to approximate ρ (μ) by the finite sample estimator ρ (μ N) (μN denotes the empirical measure of μ). In this article, we investigate convergence rates of ρ (μ N) to ρ (μ). We provide nonasymptotic convergence rates for both the deviation probability and the expectation of the estimation error. The sharpness of these convergence rates is analyzed. Our framework further allows for hedging, and the convergence rates we obtain depend on neither the dimension of the underlying assets nor the number of options available for trading. Funding: Daniel Bartl is grateful for financial support through the Vienna Science and Technology Fund [Grant MA16-021] and the Austrian Science Fund [Grants ESP-31 and P34743]. Ludovic Tangpi is supported by the National Science Foundation [Grant DMS-2005832] and CAREER award [Grant DMS-2143861]. [ABSTRACT FROM AUTHOR]

In nearly every betting market, gamblers overvalue the riskiest bets. This "longshot bias" is usually explained by a tendency to overweight low-probability events. We offer a novel explanation: contrast effects enhance the attractiveness of longshots because gambles presented in terms of their payoffs are easier to compare along the payoff dimension than along the probability dimension. Our analysis of historical betting odds suggests that contrasts between gamble payoffs can explain the longshot bias. Laboratory experiments confirm those contrast effects and show that the longshot bias disappears when gamblers consider bets in isolation or when winning probabilities are easier to compare. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. Funding: The authors thank the University of Chicago's Center for Decision Research and Richard Thaler for funding. Supplemental Material: The online supplement and data are available at https://doi.org/10.1287/mnsc.2023.4684. [ABSTRACT FROM AUTHOR]

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

Incidentally detected gallbladder polyps are commonly encountered when performing upper abdominal ultrasound. Our purpose was to estimate the life expectancy (LE) benefit of ultrasound-based gallbladder surveillance in patients with small (6-7 to <10 mm), incidentally detected gallbladder polyps, accounting for patient sex, age, and comorbidity level. We developed a decision-analytic Markov model to evaluate hypothetical cohorts of women and men with small gallbladder polyps, with varying age (66-80 years) and comorbidity level (none, mild, moderate, severe). Drawing from current evidence, in the base case, we assumed no increased risk of gallbladder cancer in patients with small gallbladder polyps. To estimate maximal possible LE gains from surveillance, we assumed perfect cancer control consequent to 5 years of surveillance. We varied key assumptions including cancer risk and test performance characteristics in sensitivity analysis. Projected LE gains from surveillance were <3 days across most cohorts and scenarios evaluated. For 66- and 80-year-olds with no comorbidities, LE gains were 1.46 and 1.45 days, respectively, for women, and 0.67 and 0.75 days for men. With 10 years of surveillance, LE gains increased to 2.94 days for 66-year-old women with no comorbidities (men: 1.35 days). If we assumed a 10% increase in gallbladder cancer risk among individuals with polyps, LE gains increased slightly to 1.60 days for 66-year-old women with no comorbidities (men: 0.74 days). Results were sensitive to test performance and surgical mortality. Even under unrealistic, optimistic assumptions of cancer control, ultrasound surveillance of incidentally detected small gallbladder polyps provided limited benefit. [ABSTRACT FROM AUTHOR]

As online shopping flourished, consumers in their shopping can refer to rich product descriptions and a large amount of review information. For the scenario of consumer online choice decision among candidate products characterized by limited attributes, we refer to it as an online multi-attribute decision-making problem. To address the challenge of online choice decision support for consumers, we propose a data-driven analytic hierarchy process (AHP). The data-driven AHP includes extracting attributes of candidate products, calculating attribute values, attribute-weight learning, interaction-based preference revision process, and product ranking. In particular, we develop an Exp-strategy for attribute-weight learning, which helps learn the attribute weights of consumers who provide reviews as a reference for an end consumer. This learning method can handle dynamic online reviews without the problem of information overload. In addition, we design the interaction-based preference revision process to help the end consumer identify his attribute weights and make a choice decision. [ABSTRACT FROM AUTHOR]

Transboundary regional infrastructure development projects are prone to multiple and multilevel challenges and risks due to the involvement of the multiple heterogeneous and diverse nature of stakeholders. It is necessary to look deeply to avoid any discrepancy in timely and sustainable construction and development of the projects of transboundary nature which involve multiple and diverse stakeholders. The study serves this purpose. The study uses the Graph Model of Conflict Resolution method used to conclude the most suitable and agreed-upon Transboundary Transportation Project based on the preferences of the most relevant stakeholders. The findings of the conflict analysis provide valuable insights for governments and policymakers involved in developing and implementing transboundary transport corridors as part of the Central Asia Regional Economic Cooperation project. To strategize effectively in transboundary regional development initiatives, relevant stakeholders must be involved, and their preferences must be considered. This decision analysis adds to theory and practice by giving critical recommendations for the practical and long-term completion of such initiatives. [ABSTRACT FROM AUTHOR]

The optimal problem exists in the individual's self‐protection activity. Generally, people consider a two‐period model within the decision equation through some constraints. In this work, we extend the two‐period model in risk decision‐making to a n$$ n $$‐period model with n≥3$$ n\ge 3 $$. Prevention and saving are two main factors discussed in this model. Then we apply the coordinate descent algorithm to find the "optimal" solution of the model equation. To the best knowledge we know, it is the first time to give the approximation of the optimality from a theoretical view. Some substitution results based on the first derivative are also presented. The numerical examples are tested to verify the result. [ABSTRACT FROM AUTHOR]

In order to study whether the logistics service provider (LSP) should carry out intelligent transformation strategy of logistics services, this paper constructs a logistics service supply chain consisting of one LSP and one logistics service integrator (LSI), and discusses whether the LSP is independent or participate in LSI. The paper shows that choosing the intelligent transformation of logistics services under any mode can improve the profits of the LSP and the LSI. The joint transformation of logistics services to improve the profit of the LSI is not affected by the choice of mode, while the profit of LSP under the resale mode remains unchanged when she chooses joint intelligent transformation. When the intelligent transformation level is high, the LSI tends to choose the resale model; otherwise, the LSI tends to choose the platform model. When the LSP chooses intelligent transformation by herself, if the share ratio is low, the LSI tends to choose the resale model. If the share ratio is high and the level of intelligent transformation of logistics services is not high, the LSI more inclines to choose the platform model. [ABSTRACT FROM AUTHOR]

A large proportion of asthma patients remain uncontrolled despite using inhaled corticosteroids. Some add-on therapies such as vitamin D supplements have been recommended for this subgroup of patients. The purpose of this study was to assess the cost-utility of vitamin D supplementation in children with mild to moderate persistent asthma in Colombia. A probabilistic Markov model was created to estimate the cost and quality-adjusted life-years (QALYs) of patients with severe asthma in Colombia. The model was analyzed probabilistically, and a value of information (VOI) analysis was conducted to inform the value of conducting further research to reduce current uncertainties in the evidence base. Cost-effectiveness was evaluated at a willingness-to-pay (WTP) value of US$5180. The mean incremental cost of vitamin D supplementation versus no supplementation is USD $44.60. The mean incremental benefit of vitamin D supplementation versus no supplementation is 0.05 QALY. This position of absolute dominance (vitamin D supplementation has lower costs and higher QALYs than no supplementation) is unnecessary to estimate the incremental cost-effectiveness ratio. Our base-case results were robust to variations in all assumptions and parameters. Add-on therapy with vitamin D supplementation is a cost-effective strategy for patients between 6 and 17 years of age with mild to moderate asthma in Colombia. [ABSTRACT FROM AUTHOR]

In a comprehensive study to assess various aspects of the performance of qualified forensic firearms examiners, volunteer examiners compared both bullets and cartridge cases fired from three different types of firearms. They rendered opinions on each comparison according to the Association of Firearm & Tool Mark Examiners (AFTE) Range of Conclusions, as Identification, Inconclusive (A, B, or C), Elimination, or Unsuitable. In this part of the study, comparison sets used previously to characterize the overall accuracy of examiners were blindly resubmitted to examiners to assess the repeatability (105 examiners; 5700 comparisons of bullets and cartridge cases) and reproducibility (191 examiners of bullets, 193 of cartridge cases; 5790 comparisons) of firearms examinations. Data gathered using the prevailing AFTE Range were also recategorized into two hypothetical scoring systems. Consistently positive differences between observed agreement and expected agreement indicate that the repeatability and reproducibility of examiners exceed chance agreement. When averaged over bullets and cartridge cases, the repeatability of comparison decisions (involving all five levels of the AFTE Range) was 78.3% for known matches and 64.5% for known nonmatches. Similarly averaged reproducibility was 67.3%% for known matches and 36.5% for known nonmatches. For both repeatability and reproducibility, many of the observed disagreements were between a definitive and inconclusive category. Examiner decisions are reliable and trustworthy in the sense that identifications are unlikely when examiners are comparing non‐matching items, and eliminations are unlikely when they are comparing matching items. [ABSTRACT FROM AUTHOR]

Multiple criteria decision-making methods have been extremely useful for identifying the optimal alternative in complicated decision problems. However, in dynamic decision situations, they often suffer from rank reversal problem (RRP). For minimising the RRP, various improvements in existing methods have emerged in recent times. The proximity-indexed value (PIV) method is one method that promises to minimize the rank reversal problem. However, a detailed investigation of the RRP issue for the PIV method does not exist. In this paper, the PIV method has been taken up for a detailed examination of RRP using a MATLAB simulation-based framework by generating a sample of 10,000 random decision matrices. Seven approaches have been used to detect the existence of rank reversal. It is found that a sizeable prevalence of rank reversal remains in the PIV method. A new, improved PIV (IPIV) method is also proposed in this paper, which has been demonstrated to be effective in reducing the prevalence of rank reversal to almost zero. The effect of varying the number of criteria and the number of alternatives is also evaluated while considering the variation in criteria weights, i.e. equal and unequal criteria weights. Analysis of variance using the concept of blocking has been carried out on the results obtained at a 5% level of significance. The study also identifies more effective approaches from the seven approaches used to detect rank reversal. In addition, the application of the IPIV method is demonstrated in a real-world problem. [ABSTRACT FROM AUTHOR]

This paper aims to prove that when developing an inventory model with fuzzy demand, the order quantity should be identical for each replenishment cycle in order to achieve an optimal replenishment policy. In 2012, Glock, Schwindl and Jaber published a paper in the International Journal Services and Operations Management. They assumed that the order quantities should be identical for each replenishment cycle. We provide a patch work to prove that to attain the minimum solution, then the order quantities must be identical for each replenishment cycle. Moreover, a formulated solution for the optimal replenishment number is derived in this paper. On the other hand, we discuss two related problems in Analytic Hierarchy Process to present some theoretical derivations to help researchers realize the mathematical structure in those problems. [ABSTRACT FROM AUTHOR]

On online platforms, goods, services, and content providers, also known as agents, introduce adverse events. The frequency of these events depends on each agent's effort level. In "Efficient Resource Allocation Contracts to Reduce Adverse Events," Liang, Sun, Tang, and Zhang study continuous-time dynamic contracts that utilize resource allocation and monetary transfers to induce agents to exert effort and reduce the arrival rate of adverse events. They devise an iterative algorithm that characterizes and calculates such contracts and specify the profit-maximizing contract for the platform, also known as the principal. In contrast to the single-agent case, in which efficiency is not achievable, they show that efficient and incentive-compatible contracts, which allocate all resources and induce agents to exert constant effort, generally exist with two or more agents. Additionally, they also provide efficient and incentive-compatible dynamic contracts that can be expressed in closed form and are therefore easy to understand and implement in practice. Motivated by the allocation of online visits to product, service, and content suppliers in the platform economy, we consider a dynamic contract design problem in which a principal constantly determines the allocation of a resource (online visits) to multiple agents. Although agents are capable of running the business, they introduce adverse events, the frequency of which depends on each agent's effort level. We study continuous-time dynamic contracts that utilize resource allocation and monetary transfers to induce agents to exert effort and reduce the arrival rate of adverse events. In contrast to the single-agent case, in which efficiency is not achievable, we show that efficient and incentive-compatible contracts, which allocate all resources and induce agents to exert constant effort, generally exist with two or more agents. We devise an iterative algorithm that characterizes and calculates such contracts, and we specify the profit-maximizing contract for the principal. Furthermore, we provide efficient and incentive-compatible dynamic contracts that can be expressed in closed form and are therefore easy to understand and implement in practice. Funding: Y. Liang acknowledges support from the National Key R&D Program of China [Grant 2020AAA0103801] and the National Natural Science Foundation of China [Grant 71872095]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2322. [ABSTRACT FROM AUTHOR]

Dogan and Yildiz introduce and analyze the pro-con model that is inspired by Franklin's prudential algebra. Consider an agent who is endowed with two sets of orderings: pro- and con-orderings. For each choice set, if an alternative is the top-ranked by a pro-ordering (con-ordering), then this is a pro (con) for choosing that alternative. The alternative with more pros than cons is chosen from each choice set. Each ordering may have a weight reflecting its salience. In this case, the probability that an alternative is chosen equals the difference between the total weights of its pros and cons. Although, this is an additive model similar to the random utility model with structurally invariant primitives, authors show that every (random) choice function is (random) pro-con rational. Their technique requires a generalization of Ford-Fulkerson theorem. The connection between the random model and its deterministic counterpart demonstrates a fruitful use of classical integer programming techniques in choice theory. We consider an agent who is endowed with two sets of orderings: pro- and con-orderings. For each choice set, if an alternative is the top-ranked by a pro-ordering (con-ordering), then this is a pro (con) for choosing that alternative. The alternative with more pros than cons is chosen from each choice set. Each ordering may have a weight reflecting its salience. In this case, the probability that an alternative is chosen equals the difference between the total weights of its pros and cons. We show that every nuance of the rich human choice behavior can be captured via this structured model. Our technique requires a generalization of the Ford-Fulkerson theorem, which may be of independent interest. As an application of our results, we show that every choice rule is plurality-rationalizable. Funding: K. Yildiz is grateful for the hospitality of New York University, Department of Economics during his visit in the 2017–2018 academic year, and the support from the Scientific and Research Council of Turkey (TUBITAK) [Grant 1059B191601712]. [ABSTRACT FROM AUTHOR]

Feral horse (Equus caballus) population management is a challenging problem around the world because populations often exhibit density‐independent growth, can exert negative ecological effects on ecosystems, and require great cost to be managed. However, strong value‐based connections between people and horses cause contention around management decisions. To help make informed decisions, natural resource managers might benefit from more detailed understanding of how horse management alternatives, including combinations of removals and fertility control methods, could achieve objectives of sustainable, multiple‐use ecosystems while minimizing overall horse handling and fiscal costs. Here, we describe a modeling tool that simulates horse management alternatives and estimates trade‐offs in predicted metrics related to population size, animal handling, and direct costs of management. The model considers six management actions for populations (removals for adoption or long‐term holding; fertility control treatment with three vaccines, intrauterine devices, and mare sterilization), used alone or in combination. We simulated 19 alternative management scenarios at 2‐, 3‐, and 4‐year management return intervals and identified efficiency frontiers among alternatives for trade‐offs between predicted population size and six management metrics. Our analysis identified multiple alternatives that could maintain populations within target population size ranges, but some alternatives (e.g., removal and mare sterilization, removal and GonaCon treatment) performed better at minimizing overall animal handling requirements and management costs. Cost savings increased under alternatives with more effective, longer lasting fertility control techniques over longer management intervals compared with alternatives with less‐effective, shorter lasting fertility control techniques. We built a user‐friendly website application, PopEquus, that decision makers and interested individuals can use to simulate management alternatives and evaluate trade‐offs among management and cost metrics. Our results and website application provide quantitative trade‐off tools for horse population management decisions and can help support value‐based management decisions for wild or feral horse populations and ecosystems at local and regional scales around the world. [ABSTRACT FROM AUTHOR]

In order to solve the problems that decision process is not closely integrated with on-site data, and it lacks quantitative indicators and fails to grasp the key points of management and control of rail transit engineering, the method for safety decision analysis and on-site information standardization is proposed based on IFC and AHP. In combination with the information characteristics of safety management and control, the IFC standard of on-site data is derived from IFC standard, and the standardization of project safety information is realized. The calculation method of analysis and decision-making on safety management and control information is proposed based on AHP, and standard process is formulated. Combined with the comprehensive monitoring system for construction projects, automatic data analysis, early warning and visual display are fulfilled. The data collection, analysis, early warning and follow-up response are analyzed in detail based on a safety risk event of shield machine passing through a residential building of Guangzhou Metro Line 11. The practice shows that the extended data structure based on IFC can fully express various on-site data, and solve its data standardization problem; the decision-making system based on AHP can quantitatively calculate the amount of security risk at the target layer and evaluate the risk level, and provide an effective theoretical basis for scientific and accurate decision-making of security management and control. [ABSTRACT FROM AUTHOR]