Decision Maker's Preferences Research Articles

Stakeholder involvement and preferences in landscape protection decision-making: a systematic literature review

Stakeholder involvement and preferences are pivotal in the decision-making process for landscape protection within a specific context. However, many decisions regarding landscape protection options still rely on management choices with little or no consideration of all stakeholders. Previous scholars emphasized the importance of establishing an integrated framework to gain an adequate understanding of the process of stakeholders’ decision-making in landscape protection. Therefore, a systematic literature review was conducted on the topic of stakeholders’ involvement and preferences in landscape protection decision-making. This review included research articles published from 2013 to 2023 using two databases and registers, namely, Science Direct and Google Scholar. A total of 110 research articles were identified and qualified for review based on the screening requirements, with an additional 15 documents for theories and backgrounds to provide a better understanding and outcomes for the study. The results of this study were organized based on concepts from the resulting research articles and were integrated to propose a conceptual framework for Stakeholders’ involvement and preferences in landscape protection. Additionally, this study’s findings indicate the significance of incorporating diverse stakeholders and their preferences in landscape protection processes to ensure awareness of inclusivity in decision-making and secure long-term support.

The Absurdity of Rational Choice: Time Travel, Foreknowledge, and the Aesthetic Dimension of Newcomb Problems

Nikk Effingham and Huw Price argue that in certain cases of Newcomb problems involving time travel and foreknowledge, being given information about the future makes it rational to choose as an evidential decision theorist would choose. Although the cases they consider have some intuitive pull, and so appear to aid in answering the question of what it is rational to do, we argue that their respective positions are not compelling. Newcomb problems are structured such that whichever way one chooses, one might be led by one’s preferred decision theory to miss out on some riches (riches which others obtain whilst employing their preferred decision theory). According to the novel aesthetic diagnosis we shall offer of the Newcomb dialectic, missing out in this way does not render one irrational but, rather, subject to being seen as absurd. This is a different kind of cost but not one that undermines one’s rationality.

A Multi-attribute Decision-making Method for Interval Rough Number Information System Considering Distribution Types

This paper proposes a novel multi-attribute decision-making (MADM) method for interval rough numbers (IRNs) considering different distribution types, namely uniform, exponential, and normal distributions. Upper and lower approximate interval dominance degrees are defined and aggregated using dynamic weights to obtain pairwise comparisons of IRNs. The properties of dominance are verified, and an attribute weight determination method based on the dominance balance degree is introduced. The proposed MADM method is data-driven and does not rely on the subjective preferences of decision-makers. Case analysis demonstrates the effectiveness and rationality of the proposed method, revealing that the distribution type of IRNs significantly impacts decision results, potentially leading to reversed ranking outcomes. The proposed method offers a comprehensive framework for handling MADM problems with IRNs under different distributions.

Organ Offer Review Cards: Improving Transparency on the Kidney Transplant Waitlist.

The 2022 National Academy of Sciences, Engineering, and Medicine report on equity in organ transplantation highlighted limited transparency and accountability for organ offer declines and recommended prioritizing patient engagement in decisions regarding organ offers. Yet, there is no guidance on how to incorporate patients in organ offers. We elected to study the experiences of patients on the waitlist and their perception of a novel Organ Offer Review Card (OORC). A prototype OORC was created using Donornet refusal codes. Sixty randomly selected kidney waitlist patients at a single center were asked to participate in a web-based survey focusing on current medical decision-making preferences and perceptions of the prototype OORC. Among the 43 patients reached, 17 (39.5%) completed the survey. Most participants (88.2%) expressed it was important to be involved in the decision-making about organ offers, with 100.0% of respondents wanting to know why an organ was declined. Regarding the prototype OORC, 94.1% thought it helped them understand the factors and priorities considered when selecting an organ, and 88.2% said it increased their belief that their team was acting in their best interest. An OORC could increase transparency and communication during the waitlist process while enhancing trust in the transplant team.

A novel preference-driven evolutionary algorithm for dynamic multi-objective problems

Most studies in dynamic multi-objective optimization have predominantly focused on rapidly and accurately tracking changes in the Pareto optimal front (POF) and Pareto optimal set (POS) when the environment undergoes changes. However, there are real-world scenarios where it is necessary to simultaneously solve changing objective functions and satisfy the preference of Decision Makers (DMs). In particular, the DMs may be only interested in a partial region of the POF, known as the region of interest (ROI), rather than requiring the entire POF. To meet the challenge of simultaneously predicting a changing POF and/or POS and dynamic ROI, this paper proposes a new dynamic multi-objective evolutionary algorithm (DMOEAs) based on the preference. The proposed algorithm consists of three key components: an evolutionary direction adjustment strategy based on changing reference points to accommodate shifts in preferences, an angle-based search strategy for tracking the varying ROI, and a hybrid prediction strategy that combines linear prediction models and population manifold estimation within the ROI to ensure convergence and distribution in scenarios where preferences remain unchanged. Experimental studies conducted on 30 widely used benchmark problems in which it outperforms contrasting algorithms on 71% of test suits. Empirical results demonstrate the significant advantages of the proposed algorithm over existing state-of-the-art DMOEAs.

The Influence of Social Media on the Consumer Decision-Making Process and Preferences in the Digital Era

The Influence of Social Media on the Consumer Decision-Making Process and Preferences in the Digital Era

Consensus-oriented linguistic multi-criteria group sorting method incorporating dynamic trust management

Multi-criteria group sorting (MCGS) concerns the multi-person process of evaluating alternatives and assigning them to predetermined ordered categories under certain criteria. Identifying class thresholds and coordinating conflicting opinions are two primary challenges within this process. Taking into account the 2-tuple linguistic preferences of decision-makers (DMs), we propose a consensus-oriented sorting method that incorporates dynamic trust management for linguistic MCGS. The main contributions and innovations are as follows. (a) A group consensus maximization model is constructed to optimize class thresholds and initial individual sorting results. (b) Integrating familiarity-based trust and similarity-based trust, the dynamic comprehensive trust among DMs is established. On this basis, a feedback mechanism is designed, in which the bi-objective programming is utilized to generate adjustment recommendations. (c) The final consensual group sorting result is obtained by maximizing the similarity of group views. (d) The feasibility of the proposed method is demonstrated using a case of sorting fresh agricultural products. Using sensitivity analysis and comparative analysis, the proposed method is verified to be rational and effective. A comparison based on real datasets and a qualitative comparison further demonstrate the validity and characteristics of the proposed method.

Three-way decision based on three-way preference measures and three-level dominance relations in interval-valued systems

Interval-valued systems facilitate data decision, and their preference measures and dominance relations become pivotal for three-way decision (3WD). However, the recent D-type preference measure and partial-overall dominance relation between intervals respectively exhibit the weak representation and incomplete hierarchy, so corresponding results need further development. Aiming at interval-valued systems, an S-type measure is proposed to constitute three-way preference measures, and three-level dominance relations are established, so criss-cross knowledge granulations motivate systematic 3WD models and applications. First, the S-type preference measure is proposed from the sigmoid function, it exhibits good learning semantics and mathematical properties, and its supplementation and improvement induce three-way preference measures. Then, by three-level constructions, three-way preference measures are applied for sorting and classification, and the S-type measure exhibits decision effectiveness and recognition superiority. Furthermore, hierarchical three-way preference measures induce three-level dominance relations, and vertical-horizontal condition granulations generate multiple 3WD models on preference decision classes. Finally, all 3WD strategies from criss-cross dominance relations are comprehensively compared and selected via classification error rates; by data experiments, the S-type measure and its hierarchical relations become effective and optimal for 3WD, and the corresponding 3WD approaches outperform the existing methods from the D-type measure and partial-overall relation.

Intelligent decision modeling for optimizing railway cold chain service networks under uncertainty

Railway cold chain service network design (RCC-SND) aims to optimize the utilization of stations and lines as well as train allocations in a manner that minimizes costs while satisfying the service requirements of shippers. Furthermore, the uncertainties associated with freight demand, transportation costs, quality loss, station handling capacity, and arc capacity make the RCC-SND a complex decision-making problem. To tackle this challenge, we first formulate a Mixed-Integer Nonlinear Programming (MINLP) model to determine hub locations, freight wagon flows, and service frequency. To cope with uncertain parameters with varying degrees of uncertainty incorporated in the model, we extend the problem using fuzzy programming and further convert it to its crisp counterpart. A real-world cases study in Southwest China is performed to validate the proposed model, whose results provide different strategies for decision-makers with varying preferences. There are some main findings: As the number of hubs increases from 5 to 6, a maximum total cost savings of 1.99% can be achieved. Railway operators may opt for different decision preferences, for decisions prioritizing economic efficiency, the cost can decrease by 2.69% compared to deterministic optimization.

Participants' Utilitarian Choice Is Influenced by Gamble Presentation and Age.

No prior behavioral science research has delved into the impact of gamble presentation (horizontal or vertical) on individuals' utilitarian behavior, despite evidence suggesting that such choices can be influenced by comparing attributes like probability and money in gambles. This article addresses this gap by exploring the influence of gamble presentation on utilitarian behavior. A two-factor independent measures design was employed to explore the influence of the type of gamble presentation and age on participants' utilitarian decision-making preferences. The findings showed a reduced likelihood of participants choosing the non-utilitarian gamble with vertically presented gambles compared to horizontal ones. Consequently, participants' utilitarian behavior was influenced by between-gamble comparisons of available attributes, with utilitarian choices (e.g., choosing Gamble A) being more prevalent in vertical presentations due to a straightforward comparison on the probability attribute. Furthermore, the results also revealed that older participants take more time than their younger counterparts when making utilitarian errors. We attribute this to their abundant knowledge and experience. Future research should explore the comparative psychological processing used by participants in risky decision-making tasks.

Poikiloderma vasculare atrophicans: report of a case of long duration.

We present the case of a 70-year-old woman with poikiloderma vasculare atrophicans (PVA), which demonstrated a benign course over 35 years, raising questions about its classification as early-stage mycosis fungoides (MF) or as a distinct entity. Despite receiving no treatment, the patient’s condition remained stable, with characteristic erythematous papules observed on clinical examination. Histopathological and immunohistochemical analyses supported the diagnosis, emphasizing the importance of longitudinal studies and patient preferences in treatment decisions for PVA.

An integrated framework for improving green agricultural production sustainability in human-natural systems

Water scarcity, land pollution, and global warming are serious challenges and crises facing the development of sustainable or green agriculture and need to be addressed using efficient and environmentally friendly management strategies. This paper proposed an integrated framework appropriate for agricultural green total factor productivity (AGTFP) assessment coupled with microscopic and mesoscopic perspectives under water-energy-food (WEF) nexus, which generated scientific and reasonable strategies for green and low-carbon agriculture from internal core factors and peripheral environmental impacts to improve green agricultural production sustainability. Taking the Lianshui irrigation district (LID) with three sub-areas as the object, internal core factors were explored by partial least squares regression (PLSR) and the external impact path through partial least squares structural equation modeling (PLS-SEM). Results indicated that AGTFP in LID was the smallest (0.818) compared to the three sub-areas and was in a fluctuating state. Meanwhile, AGTFP which was calculated considering undesirable outputs, was closer to tangible productivity. Resource endowments and technical facilities will promote agricultural production, desirable outputs will stimulate green production, and undesirable outputs can inhibit green production. The external influence pathway was shown to be primary environment - > secondary environment - > economic aspects - > social aspects - > AGTFP. The innovative perspectives presented in this study can facilitate preferable decisions and avoid unintended consequences for human-natural systems.

Spontaneous Splenic Rupture in a Patient with Myelodysplastic Syndrome

Introduction: Myelodysplastic syndrome (MDS) is a clonal hematopoietic disorder with a risk of progression to acute myeloid leukaemia. This case report discusses a rare instance of spontaneous splenic rupture in a patient with MDS. Case Report: A 76-year-old male with a history of MDS presented with acute abdominal pain and was found to have a splenic rupture. Despite declining chemotherapy, the patient underwent an emergency splenectomy after a CT scan revealed hemoperitoneum. Post-surgery, the patient experienced hypovolemic shock, necessitating a second operation. Discussion: Splenic rupture in MDS is uncommon and its pathogenesis is not fully understood. Management of MDS is complex, often requiring a multidisciplinary approach. This case emphasizes the importance of considering patient preferences in treatment decisions and highlights the role of next-generation sequencing in understanding MDS pathogenesis. Conclusion: The case underscores the need for vigilance in managing MDS due to potential complications like splenic rupture. It also illustrates the importance of personalized care and advances in genetic profiling in predicting prognosis and guiding treatment in MDS.

A Hybrid Preference Interaction Mechanism for Multi-Satellite Imaging Dynamic Mission Planning

The existing multi-satellite dynamic mission planning system hardly satisfies the requirements of fast response time and high mission benefit in highly dynamic situations. In the meantime, a reasonable decision-maker preference mechanism is an additional challenge for multi-satellite imaging dynamic mission planning based on user preferences (MSDMPUP). Therefore, this study proposes the hybrid preference interaction mechanism and knowledge transfer strategy for the multi-objective evolutionary algorithm (HPIM–KTSMOEA). Firstly, an MSDMPUP model based on a task rolling window is constructed to achieve timely updating of the target task importance degree through the simultaneous application of periodic triggering and event triggering methods. Secondly, the hybrid preference interaction mechanism is constructed to plan according to the satellite controller’s preference-based commands in different phases of the optimal search of the mission planning scheme to effectively respond to the dynamic changes in the environment. Finally, a knowledge transfer strategy for the multi-objective evolutionary algorithm is proposed to accelerate population convergence in new environments based on knowledge transfer according to environmental variability. Simulation experiments verify the effectiveness and stability of the method in processing MSDMPUP. This study found that the HPIM–KTSMOEA algorithm has high task benefit, short response time, and high task completion when processing MSDMPUP.

Quantile Effects in Discrete Choice with Social Interactions

Abstract This paper provides a method to study quantile effects in discrete choice with social interactions. The method is based on a behavioral social interactions model from quantile preference in decision making and demonstrates peer effects on different quantiles of discrete outcomes. The peer effects parameters are estimated by a nested pseudo score (NPS) approach, which is developed to tackle the computational burden pertaining to the social interactions model. Consistency and asymptotic normality are established for the proposed NPS estimator. We illustrate the finite sample performance of the model and the estimator by Monte Carlo experiments and an application of peer effects among students on exercise decisions, using the National Longitudinal Study of Adolescent Health dataset.

Outranking-based approaches for multiple criteria partially ordered clustering: A review of existing algorithms, new proposals, and experimental comparison

We consider clustering problems that involve categorizing alternatives into partially ordered, initially undefined groups based on their performance across multiple criteria. To achieve this, we use an outranking relation model to reflect the Decision Maker's preferences. We examine various algorithms that not only group the alternatives but also order the clusters in different ways. This analysis includes innovative approaches that use distances in the space of outranking relations or detailed relation profiles, and apply orthogonal non-negative factorization to outranking matrices. Additionally, we discuss a set of measures, including two novel ones, for assessing the effectiveness of clustering when the groupings are partially ordered. Our findings are based on comprehensive computational experiments on real-world and simulated datasets. Beyond evaluating various methods using four quality metrics and computational efficiency, we explore the influence of accessible preference structures and ordering techniques on the clustering outcomes.

Multi-objective optimization of plant community characteristics in urban green spaces

Urban green spaces are crucial for improving the quality of life of city residents because they provide numerous health advantages and foster overall wellness. However, previous research has concentrated only on individual goals and overlooked the inherent contradictions that arise when many objectives are considered. This study devised a transdisciplinary framework to link the optimization of plant community characteristics with multiple objectives. In Beijing, we selected 22 urban green spaces and utilized the non-dominated sorting genetic algorithm III (NSGA-III) to create a Pareto-optimal model. This model allowed us to reveal the relationships and limitations between plant community characterization factors and three objectives: temperature comfort, landscape aesthetics, and construction costs. Consequently, 91 Pareto-optimal solutions were obtained. The results indicate that a tradeoff exists between the three goals of temperature comfort, landscape aesthetics, and building costs, and this tradeoff is influenced by the interplay between the objectives and plant community characterization factors. We condensed four methodologies for building plant communities, each with distinct objective orientations, while also considering the preferences of decision-makers. The results of this study offer a novel perspective for conducting research in multi-objective scenarios to promote environmental sustainability.

A seamless phase II/III design with dose optimization for oncology drug development.

The US FDA's Project Optimus initiative that emphasizes dose optimization prior to marketing approval represents a pivotal shift in oncology drug development. It has a ripple effect for rethinking what changes may be made to conventional pivotal trial designs to incorporate a dose optimization component. Aligned with this initiative, we propose a novel seamless phase II/III design with dose optimization (SDDO framework). The proposed design starts with dose optimization in a randomized setting, leading to an interim analysis focused on optimal dose selection, trial continuation decisions, and sample size re-estimation (SSR). Based on the decision at interim analysis, patient enrollment continues for both the selected dose arm and control arm, and the significance of treatment effects will be determined at final analysis. The SDDO framework offers increased flexibility and cost-efficiency through sample size adjustment, while stringently controlling the Type I error. This proposed design also facilitates both accelerated approval (AA) and regular approval in a "one-trial" approach. Extensive simulation studies confirm that our design reliably identifies the optimal dosage and makes preferable decisions with a reduced sample size while retaining statistical power.

Symmetric Kullback–Leibler distance based generalized grey target decision method for mixed attributes

PurposeThe reported Kullback–Leibler (K–L) distance-based generalized grey target decision method (GGTDM) for mixed attributes is an asymmetric decision-making basis (DMB) that does not have the symmetric characteristic of distance in common sense, which may affect the decision-making result. To overcome the deficiency of the asymmetric K–L distance, the symmetric K–L distance is investigated to act as the DMB of GGTDM for mixed attributes.Design/methodology/approachThe decision-making steps of the proposed approach are as follows: First, all mixed attribute values are transformed into binary connection numbers, and the target centre indices of all attributes are determined. Second, all the binary connection numbers (including the target centre indices) are divided into deterministic and uncertain terms and converted into two-tuple (determinacy and uncertainty) numbers. Third, the comprehensive weighted symmetric K–L distance can be computed, as can the alternative index of normalized two-tuple (deterministic degree and uncertainty degree) number and that of the target centre. Finally, the decision-making is made by the comprehensive weighted symmetric K–L distance according to the rule that the smaller the value, the better the alternative.FindingsThe case study verifies the proposed approach with its sufficient theoretical basis for decision-making and reflects the preferences of decision-makers to address the uncertainty of an uncertain number.Originality/valueThis work compares the single-direction-based K–L distance to the symmetric one and uses the symmetric K–L distance as the DMB of GGTDM. At the same time, different coefficients are assigned to an uncertain number’s deterministic term and uncertain term in the calculation process, as this reflects the preference of the decision-maker.

Factors influencing central nervous system medication deprescribing and behavior change in hospitalized older adults.

Central nervous system (CNS) medications are linked to higher morbidity and mortality in older adults. Hospitalization allows for deprescribing opportunities. This qualitative study investigates clinician and patient perspectives on CNS medication deprescribing during hospitalization using a behavioral change framework, aiming to inform interventions and identify recommendations to enhance hospital deprescribing processes. This qualitative study focused on hospitalists, primary care providers, pharmacists, and patients aged ≥60 years hospitalized on a general medicine service and prescribed ≥1 CNS medications. Using semi-structured interviews and focus groups, we aimed to evaluate patient medication knowledge, prior deprescribing experiences, and decision-making preferences, as well as provider processes and tools for medication evaluation and deprescribing. Rapid qualitative analysis applying the Capability, Opportunity, Motivation, and Behavior (COM-B) framework revealed themes influencing deprescribing behavior in patients and providers. A total of 52 participants (20 patients and 32 providers) identified facilitators and barriers across deprescribing steps and generated recommended strategies to address them. Clinicians and patients highlighted the opportunity for CNS medication deprescribing during hospitalizations, facilitated by multidisciplinary teams enhancing clinicians' capability to make medication changes. Both groups also stressed the importance of intensive patient engagement, education, and monitoring during hospitalizations, acknowledging challenges in timing and extent of deprescribing, with some patients preferring decisions deferred to outpatient clinicians. Hospitalist and pharmacist recommendations centered on early pharmacist involvement for medication reconciliation, expanding pharmacy consultation and clinician education on deprescribing, whereas patients recommended enhancing shared decision-making through patient education on medication adverse effects, tapering plans, and alternatives. Hospitalists and PCPs also emphasized standardized discharge instructions and transitional care calls to improve medication review and feedback during care transitions. Clinicians and patients highlighted the potential advantages of hospital interventions for CNS medication deprescribing, emphasizing the necessity of addressing communication, education, and coordination challenges between inpatient and outpatient settings.