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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]

This paper explores a decision analysis on product integration of family security services and residential fire insurance in the London and Taiwan markets by using the proposed mathematical models for counting sustainable value. This paper shows the five main different results between London and Taiwan markets with ten different parameters of the family security market, to find out the optimal number of family security integrated services for each security company in London. The improvement of the risk aversion effect based on risk and financial management will enhance the market share of the private security industries in the London and Taiwan markets. The results of this research can serve as a reference for the decision-making of private security industries on product integration under sustainable value consideration. The research findings highlight the potential benefits for both the private security industry and the insurance industry in their design and negotiation for product integration to improve both of business operation and achieve corporate social responsibility goals to match the sustainability in the future. [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]

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]

During the review process for the paper "Architecting Portfolios of Systems" in this issue one of the reviewers suggested that part of the reviewer‐author dialog be published because it clarified some issues beyond the paper itself and illustrated some different perspectives. This paper elaborates three major topics: The categorization of different types of collections of systems, the concept of requisite modeling in how we choose to model systems under development across a spectrum of rigor, and how to judge the success of research in systems engineering processes and methods. [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]

The implementation of chemical filtration in air-conditioning systems in archive and library repositories has become standard practice for heritage institutions in many countries. This practice is codified in official standards with general reference to a substantial body of scientific publications on the effects of air pollutants on paper materials. In this paper we report the application of a well-established decision analysis approach elaborating an integral cost-benefit perspective to provide decision support to four European national heritage institutions confronted with dilemmas in connection to chemical air filtration. Available scientific evidence for air pollution induced damage of paper material is reviewed and weighed against financial and environmental costs of air pollution control. Exposure of paper to both indoor and outdoor generated air pollutants at typical concentration levels observed within storage areas is expected to cause minimal to no additional yellowing and embrittlement, while the costs of chemical air filtration are significant. From a cost-benefit perspective the use of chemical air filtration in typical library and archive repositories is therefore discouraged. [ABSTRACT FROM PUBLISHER]

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]

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]

Purpose: The purpose of this paper is to close the gap between the theoretical nature of existing contributions in customer engagement value (CEV) and its need to practically empower business decisions. This is done by proposing a framework that consists of three techniques, each of which combines the components of CEV to make it more comprehensive and applicable. The paper also reviews and analyzes the work that has been done so far in the area of CEV whether in business to business (B2B), business to consumer (B2C) or consumer to consumer (C2C) markets. Design/methodology/approach: CEV is a comprehensive term that measures the total value of the customer through capturing his transactional and non-transactional behaviors. Hence, it is an essential term for measuring the value of the customer in direct marketing. This motivates researchers to compete in developing models to maximize CEV. Meanwhile, most of the existing models are conceptual and the majority of them lack applicability due to many reasons. First, these models relied on a linear version of the CEV model, hence double-counting the value of the customer; also they weighted the components of CEV equally, which is unrealistic. Finally, the effect of the environmental components in determining the engagement level of each customer was almost ignored. In this paper, two main contributions are presented. First, a summary and analysis of the contributions of the literature in the CEV field for different market types whether in B2C, B2B or C2C. Furthermore, three modifications are added to the existing models. The first model introduces a non-linear relationship of the components of CEV. The second model is a weighted linear model of these components. Finally, the third model adds the environmental factors to the CEV components. All the proposed models are theoretical in nature, however, these models are expected to show superiority when being applied to real data sets due to their ability to capture the complexity in the relationship between the firm and its customers in real-life situations. The proposed models are expected to attract the practitioners and other researchers and they both are encouraged to apply the proposed models on real-life data sets, test their performance, compare them against each other, to be able to apply each of them on the best suitable data set and business scenario. Findings: Based on the review and analysis that has been done on about 87 papers, it is found that the majority of the contributions that have been done in the area of CEV are theoretical in nature, in spite of the effectiveness of CEV in empowering business decision. It is also found that few researchers proposed a set of theoretical comprehensive frameworks that combined CEV's components together. Meanwhile, those frameworks are not practically applicable. Research limitations/implications: Although the contribution of the proposed models expected to attract both researchers and practitioners, these are not applied to real-life case studies to prove their effectiveness. Practical implications: The research in this paper has many industrial and managerial implications. First, it helps managers and decision takers to treat the customers as assets and cost-free resources who can work with the firm to achieve what's both aims to (i.e. increase customer satisfaction and firm's profitability). Second, it helps the firm to determine the total value of each customer and treat its customers accordingly. Third, it empowers the managers to do target marketing, based on grouping the customers upon their total engagement. This would save time and cost and for sure increase the profitability and customer satisfaction. Forth, the proposed models take into consideration not only the transactional behavior of the customers but also the non-transactional factors that play a significant role in formulating the relationship between the firm and its customers. Originality/value: This is hereby to certify that the paper is original, neither the paper nor a part of it is under consideration for publication anywhere else. Also, this study has no conflicts of interest to disclose. [ABSTRACT FROM AUTHOR]

Current data transactions are usually driven by data providers, and data buyers have little information about the data they will buy. In turn, this information asymmetry impacts the data providers' sales decisions. In this paper, we consider a data provider with private quality information to sell a data set to multiple potential data buyers, and the data provider signals quality information to the potential buyers by setting the price and sample set size. We study how the data provider decides the optimal trading strategy in terms of price and sample set size based on different characteristics of data buyers through a signalling game. It is found that the degree of information asymmetry and the cost of providing a sample set affect the data provider's choice of whether to signal data quality truthfully or to hide quality information. The joint signal of price and sample set is a better signalling option for data providers to achieve higher profits than the price signal alone. Tracing data trading back to the production process of datasets, the increased information asymmetry and inefficient data production make data providers more inclined to generate low-quality datasets, which adversely affects the data trading market. [ABSTRACT FROM AUTHOR]

In group decision making (GDM), there may exist some problems that need to assign alternatives to some predefined ordered categories, which are called ordinal classification-based GDM problems. To obtain classification results that can be accepted by most decision makers (DMs), it is necessary to implement a consensus reaching process for ordinal classification-based GDM problems. In this paper, we study consensus reaching models for a new type of ordinal classification-based GDM problem, in which DMs do not provide criteria weights and category cardinalities but provide indirect and imprecise heterogeneous preference information. To do so, a consistency verification method is first proposed to check whether each DM's preference information is consistent and then a minimum adjustment optimization model is developed to modify DMs' inconsistent preference information. Afterwards, we establish some optimization models to obtain each DM's possible categories for alternatives. Followed by this, we define the consensus levels of DMs and devise some optimization models to assist DMs in adjusting alternatives' classification results and DMs' preference information at the same time. Furthermore, a maximum support degree-based method is provided to determine the consensual classification result for alternatives. Finally, a numerical application and some sensitivity analysis are provided to justify the proposed models. [ABSTRACT FROM AUTHOR]

Portfolio selection for Stock evaluation and selection by an investor is multi-criteria decision-making (MCDM) problem of finding the best portfolio among an efficient set of portfolios, which should be tackled by using the appropriate techniques. The Analytic Hierarchy Process (AHP) is amongst the most widely used MCDM methods, which is often used in operation management. The main feature of AHP is, that, it firstly reduces the complex decision problems in hierarchical structures of objectives, criteria, sub-criteria, and alternatives and then, uses a fundamental scale to construct pairwise comparisons. The Spherical Fuzzy Sets (SFS) have many advantages in handling the uncertainty and vagueness of ordinary and 2D Fuzzy Sets (FS)s. Therefore, taking the advantage of AHP and SFSs in this paper, we modify the Fuzzy Analytic Hierarchy Process (FAHP) into Spherical fuzzy AHP (SFAHP). We introduce the concept of Spherical Fuzzy Preference Relation (SFPR) and develop an automatic algorithm to construct a consistent SFPR from an inconsistent one. The validity of the proposed approach is tested through an illustrative application of portfolio selection on the Pakistan Stock Exchange to make a prototype of our results. [ABSTRACT FROM AUTHOR]

Background: There is growing evidence to support the use of the psychedelic drug psilocybin for difficult-to-treat depression. This paper compares the cost-effectiveness of psilocybin-assisted psychotherapy (PAP) with conventional medication, cognitive behavioural therapy (CBT), and the combination of conventional medication and CBT. Methods: A decision model simulated patient events (response, remission, and relapse) following treatment. Data on probabilities, costs and quality-adjusted life years (QALYs) were derived from previous studies or from best estimates. Expected healthcare and societal costs and QALYs over a 6-month time period were calculated. Sensitivity analyses were used to address uncertainty in parameter estimates. Results: The expected healthcare cost of PAP varied from £6132 to £7652 depending on the price of psilocybin. This compares to £3528 for conventional medication alone, £4250 for CBT alone, and £4197 for their combination. QALYs were highest for psilocybin (0.310), followed by CBT alone (0.283), conventional medication alone (0.278), and their combination (0.287). Psilocybin was shown to be cost-effective compared to the other therapies when the cost of therapist support was reduced by 50% and the psilocybin price was reduced from its initial value to £400 to £800 per person. From a societal perspective, psilocybin had improved cost-effectiveness compared to a healthcare perspective. Conclusions: Psilocybin has the potential to be a cost-effective therapy for severe depression. This depends on the level of psychological support that is given to patients receiving psilocybin and the price of the drug itself. Further data on long-term outcomes are required to improve the evidence base. [ABSTRACT FROM AUTHOR]

The decision to transfer or share an insurable risk is critical for the decision maker's economy. This paper deals with this decision, starting with the definition of a function that represents the difference between the expected utility of insuring, with or without deductibles, and the expected utility of not insuring. Considering a constant relative risk aversion (CRRA) utility function, we provide a decision pattern for the potential policyholders as a function of their wealth level. The obtained rule applies to any premium principle, any per-claim deductible and any risk distribution. Furthermore, numerical results are presented based on the mean principle, a per-claim absolute deductible and a Poisson-exponential model, and a sensitivity analysis regarding the deductible parameter and the insurer security loading was performed. One of the main conclusions of the paper is that the initial level of wealth is the main variable that determines the decision to insure or not to insure; thus, for high levels of wealth, the decision is always not to insure regardless of the risk aversion of the decision maker. Moreover, the parameters defining the deductible and the premium only have an influence at low levels of wealth. [ABSTRACT FROM AUTHOR]

Conservation decisions are challenging, not only because they often involve difficult conflicts among outcomes that people value, but because our understanding of the natural world and our effects on it is fraught with uncertainty. Value of Information (VoI) methods provide an approach for understanding and managing uncertainty from the standpoint of the decision maker. These methods are commonly used in other fields (e.g. economics, public health) and are increasingly used in biodiversity conservation. This decision‐analytical approach can identify the best management alternative to select where the effectiveness of interventions is uncertain, and can help to decide when to act and when to delay action until after further research. We review the use of VoI in the environmental domain, reflect on the need for greater uptake of VoI, particularly for strategic conservation planning, and suggest promising areas for new research. We also suggest common reporting standards as a means of increasing the leverage of this powerful tool. The environmental science, ecology and biodiversity categories of the Web of Knowledge were searched using the terms 'Value of Information,' 'Expected Value of Perfect Information,' and the abbreviation 'EVPI.' Google Scholar was searched with the same terms, and additionally the terms decision and biology, biodiversity conservation, fish, or ecology. We identified 1225 papers from these searches. Included studies were limited to those that showed an application of VoI in biodiversity conservation rather than simply describing the method. All examples of use of VOI were summarised regarding the application of VoI, the management objectives, the uncertainties, the models used, how the objectives were measured, and the type of VoI. While the use of VoI appears to be on the increase in biodiversity conservation, the reporting of results is highly variable, which can make it difficult to understand the decision context and which uncertainties were considered. Moreover, it was unclear if, and how, the papers informed management and policy interventions, which is why we suggest a range of reporting standards that would aid the use of VoI. The use of VoI in conservation settings is at an early stage. There are opportunities for broader applications, not only for species‐focussed management problems, but also for setting local or global research priorities for biodiversity conservation, making funding decisions, or designing or improving protected area networks and management. The long‐term benefits of applying VoI methods to biodiversity conservation include a more structured and decision‐focused allocation of resources to research. [ABSTRACT FROM AUTHOR]

Trust widely works in supply chain practices and deeply affects supply chain decisions. Full trust in transactions and inaccurate demand forecasts are most likely to lead to biased decisions and low supply chain performances. Therefore, we propose a trust updating model to quantify decision-maker’s trust. The model presents the variation of trust over time and is helpful to evaluate decision-maker’s trust level in each transaction. In the model, a coordination mechanism with contract is designed to mitigate each supply chain partner’s ordering risk. Optimal order strategies of both manufactures and retailers in a supply chain under a bidirectional option contract are analysed in this paper. We find the manufacturer’s optimal production quantity is positively affected by penalty price in the contract and its bounds are given by simulation analysis. Our findings in this paper indicate that the proposed coordination mechanism with bidirectional option contract is profitable to all supply chain partners in long-term transactions. Meanwhile, the coordination mechanism helps the supply chain partners to catch up with marketing fluctuations and enhances the supply chain trust and partnerships. Finally, some simulation experiments are employed to obtain more observations. [ABSTRACT FROM AUTHOR]