Decision Maker's Preferences Research Articles

Group Decision Making in Multiobjective Optimization: A Systematic Literature Review

AbstractGroup decision making has been studied from several viewpoints and a variety of methods has been proposed. However, in the literature on solving multiobjective optimization problems, the main focus has been on supporting a single decision maker. We conducted a systematic literature review to examine and synthesize the state-of-the-art of multiobjective optimization methods developed for group decision making. We analyze group decision making methods of multiobjective optimization according to how preferences of several decision makers are incorporated into the solution process, how to select the most preferred solution for the group, different types of decision makers, types of groups and how the group is to operate during the solution process. In addition, we identify the key issues in the literature that are required to be considered in further method development to increase the methods’ applicability in solving real-world problems. Finally, we guide how to select a method for solving real-world multiobjective optimization problems with multiple decision makers and suggest future research directions.

Green Technology Investment and Production Decisions Considering Stochastic Demand and Firms' Decision Preferences Under Cap‐and‐Trade Regulation

ABSTRACTThis paper investigates how finite rational firms optimally respond to market potential under cap‐and‐trade regulation, considering stochastic demand. Using the mean–variance model framework, we develop an optimization model for green technology investment and production by finite rational firms with different decision‐making preferences. The main results indicate: (1) Under stable demand, firms investing in green technology gain more profits and consumer surplus, however, the effectiveness of carbon emission reduction depends on the market potential size. (2) Under stochastic demand, optimal decisions resulting from firms' varying decision preferences systematically deviate from those of risk‐neutral firms and often demonstrate firms' risk‐averse tendencies that lean towards conservatism. As external factors change, there can even be a reversal in technology investment with an increase in demand uncertainty that amplifies this systematic deviation. (3) Taking firms with different decision‐making preferences as the starting point, this study further analyses the distortion effect of the firm's risk attitude on the optimal technology investment and production decisions, enriching the related research on the joint decision‐making risk model of clean production. And it is more pertinent to the complex situation firms face in the real situation with decision‐making. (4) This study provides a theoretical basis and practical reference for the finite rational firms facing green technology investment choices. Considering the risk‐averse characteristics of firms, this study suggests that the government should establish a risk‐sharing mechanism to weaken the deviation of stochastic demand from the optimal decision‐making of firms or implement certain subsidy policies to protect the profit level of firms.

A method of articulating the decision maker's preferences based on examples of decisions made

The paper examines the problem of determining a decision-maker's preference model based on the decisions he makes. This is a fundamental issue in behavioral identification tasks or collaborative information filtering. Inferences about the decision-maker's preferences are made possible by a database containing the values of features accompanying the decisions made. The preference vector serves as the basic preference model, determining the weights of individual coordinates in the decision selection process. The proposed method of preference articulation allows for ordering decisions solely on the basis of the proposed model, without directly referring to the indicated pattern.

Multivariate process capability analysis with decision-maker preferences

A process capability analysis is an invaluable technique for assessing the efficacy of a system by determining its alignment with customer expectations. In the context of a manufacturing process with multiple correlated variables, the use of multivariate methods is essential for the analysis of quality characteristics. In the literature, there have been various proposals for MPCI (multivariate process capability indices) based on different approaches, including the proportion of non-conforming items or PCA (principal component analysis). In this context, a novel approach to capability analysis with multiple responses of varying degrees of importance has been proposed, termed WAMP (Weighted Arithmetic Mean with Prioritization). The findings indicated that the WAMP method demonstrated greater robustness than the SAM (simple arithmetic mean) and SGM (simple geometric mean) approaches. However, the WAM (weighted arithmetic mean) and WGM (weighted geometric mean) methods also exhibited satisfactory performance in accordance with the established acceptance criteria. The method was evaluated using both simulated and experimental data, demonstrating its efficacy in scenarios with varying degrees of correlation. When a greater weight is assigned to a single original variable, the MPCI is observed to be similar in value to that of the original variable with the highest weight when considered univariately. When equal weights are assigned to two original variables, the MPCI is situated within the capability indices of the original variables with the highest weight. Furthermore, it was observed that the performance of the MPCI improves with an increase in correlation. In conclusion, WAMP has been demonstrated to be a valuable and effective tool for capability analysis in systems with multiple correlated responses of varying degrees of importance.

Attitudes toward Death, Perceptions of Hospice Care and Needs for Hospice Care among the LGBT Population.

This study explored the attitudes of lesbian, gay, bisexual, and transgender (LGBT) individuals toward death and their perceptions of hospice and palliative care, as well as their care needs. It also investigated their preferences for alternative decision-making and advance care planning options at the end of life. A descriptive survey study was conducted with LGBT adults aged 18 and older. Participants were recruited through LGBT organizations and online communities, and data from 207 respondents were analyzed. Questionnaires were utilized to assess attitudes toward death, perceptions of hospice care, and care needs. Statistical analysis was performed using SPSS 20.0, employing the t-test, ANOVA, and Pearson correlation coefficients. LGBT individuals generally had low attitudes toward death and negative perceptions of hospice care. Attitudes toward death varied significantly by sex, age, and education, while perceptions of hospice care differed significantly by sexual orientation and education. Care needs in hospice settings also varied significantly by age, gender identity, and education. Most LGBT participants preferred their biological family members (52%) and partners (39%) as surrogate decision-makers. A significant positive correlation was found between perceptions of hospice care and overall care needs. The findings highlight the need for healthcare services tailored to LGBT individuals' specific needs. It is necessary to promote social understanding, awareness, and policy support to improve the quality of life for LGBT individuals through more inclusive and responsive healthcare services. It also points to the need for healthcare professionals to increase their awareness and understanding of these issues.

Physician and Patient Preferences for Oral Anticoagulation Therapy Decision Making in Atrial Fibrillation: Results From a National Best-Worst Scaling Survey in Türkiye.

Atrial fibrillation (AF) is the most common cardiac dysrhythmia and a common cause of ischemic stroke. Stroke prevention with oral anticoagulation (OAC) is the cornerstone of AF management. Patients and their treating physicians may have different views on different attributes of OACs. The objective of this study was to quantify the relative importance that patients and physicians in Turkey place on different OAC attributes when making treatment decisions in AF. A cross-sectional survey was administered to AF patients (≥ 50 years) receiving OAC and practising cardiologists, including residents with ≥ 2 years of experience in Turkey. For both patients (N = 230; 50% male) and physicians (N = 194; 74% male), the most important attributes for OAC treatment decision making in AF were "success in preventing stroke" (57% and 73.9% or overall importance, respectively) and "risk of major bleeding" (20% and 23.4%, respectively). For patients, other attributes were much less important, but not altogether unimportant: reversal agent availability (7%), monitoring (5%), food or drug interactions (3%), minor bleeding (3%), and ease of swallowing (2%). For physicians, among the other attributes, only the need for monitoring (1.3%) had a relative importance of > 1%. For all Turkish participants, efficacy and safety were found to be the most important attributes influencing OAC choice in AF with these two attributes accounting for 77% and 97.3% of overall importance for patients and physicians, respectively. Certain considerations, especially reversal agent availability and monitoring appear to be more important to patients than to physicians This is the first study to use BWS to quantify patient and physician preferences for OAC treatments in AF in Turkey.

A comprehensive experience-based framework of actionable priorities to enhance Canada’s health and social care system: discussions with older persons, clinicians, managers, and decision-makers using nominal group technique

BackgroundExisting frameworks of older persons’ experiences in health and social care systems are usually based on single stakeholder perspectives and focused on limited aspects that are independent from one another. This study aimed to describe such experiences from the perspective of diverse stakeholders and develop a comprehensive experience-based framework for actionable priorities to enhance Canada’s health and social care system.MethodsWe adapted a nominal group technique with a qualitative approach. We formed four groups of stakeholders (4 older persons, 7 clinicians, 5 managers, 4 decision-makers) in the province of Quebec. First, participants individually completed a questionnaire to rank the six domains of an international set of metrics of quality of care for older persons in terms of their importance. Then, we conducted a remote discussion with each stakeholder group, obtained consensus on the priority domains, and explored personal/professional experiences explaining prioritization processes and what changes are needed. An inductive-deductive thematic analysis was conducted to develop a framework for actionable health and social care priorities.ResultsThe framework included six key categories revolving around a core category of ‘health equity’. (1) ‘Access to services and care’ focused on improved accessibility of healthcare navigators, primary and home care services, and end-of life care. (2) ‘Respect for older persons’ involved individuality and uniqueness, daily needs, advocacy in defence of vulnerability, and decision-making valuing care preferences. (3) ‘Involvement of family and friends’ encompassed formal recognition, responsibilities, and trajectories associated with caregiving. (4) ‘Communication and education’ consisted of adaptive communication between older persons, caregivers, and professionals and societal-level education and awareness-raising. (5) ‘Coordination and integration of care’ comprised health and social services organization, cross-sectoral approaches, and interdisciplinary collaboration. (6) ‘Responsiveness to evolving needs’ of older persons involved timely response to health transitions and promoting holistic approaches.ConclusionsThis study illustrates the complex challenges of caring for older persons. The framework suggests several areas for improvement in care practices and policies for older persons and caregivers. Proposed intervention targets include public healthcare system navigators, standardized goals of care practices respecting persons’ wishes, formal recognition of caregiver contributions, society-level education, cross-sectoral initiatives, and holistic approaches.

A comprehensive multi-stage decision-making model for supplier selection and order allocation approach in the digital economy

The increasingly serious environmental issues and fierce competition caused by globalization have brought pressure on supply chain managers who seek to allocate multiple purchase demands comprehensively, highlighting the significance of supplier assessment considering sustainability and technique. Moreover, many multi-criteria decision-making (MCDM) methods fail to quantify the risk preference of decision-makers (DMs) when conducting the supplier assessment process. Indeed, a hybrid supplier selection and order allocation model that integrates such requirements is yet to be proposed. Thus, this work develops a comprehensive decision-making model that constructs a deep learning model to forecast the potential demand and addresses the sustainable supplier selection based on cumulative prospect theory (CPT) and multi-material order allocation problem simultaneously. The proposed order allocation model is solved by the second generation of adaptive geometry estimation based many-objective evolutionary algorithm, with the technique of order preference similarity to the ideal solution used to filter out the best Pareto solution for DMs as the reference. Through implementing an illustrative case study of a leading Chinese engineering machinery manufacturer followed by a sensitivity analysis, the relatively strong applicability and scalability of the proposed model and methods are demonstrated. The results show that introducing Weibull distribution to estimate the theoretical obsolescence rate of historically sold accessories can result in higher demand prediction accuracy for consumable mechanical accessories. Integrating CPT into the MCDM framework allows us to evaluate suppliers more comprehensively by capturing the effect of DMs’ risk preferences and gain or loss sensitivity.

Suitability matching evaluation of the low-carbon regional integrated energy system based on the “resource-demand” balance

The integrated energy system (IES) stands out due to its advantages of multi-energy complementarity, green and efficiency. This paper aims to provide a practical model for suitable region site selection of low-carbon regional IES based on the “resource-demand” balance. A “resource-demand” suitability matching analysis indicator system is established. By using prospect theory and variable precision rough set theory, the decision-making preferences and the cognitive fuzziness influence of expert scoring were corrected respectively, and reasonable subjective weights were obtained. Then, the objective weights are obtained using the entropy method, and the optimal combined weights are obtained through the game theory weighting method. Finally, this paper establishes a “resource-demand” suitability matching model consisting of three sub models: similarity matching degree, numerical matching degree, and suitability matching degree. An empirical study is conducted on 16 regions in Beijing, and the results show that the numerical matching degree of “resource-demand” gradually decrease from the central urban regions to the surrounding regions. The similarity matching degree in most regions reaches the highest range (0.776,1.00). There are 4 regions with a suitability matching rating of perfect, 7 regions with a rating of great, 1 region with a rating of good, and 4 regions with a rating of poor. The suitability matching model established in this paper can guide energy project decision-makers to select regions with greater potential for the development of IES. In addition, adjusting the similarity matching coefficient and numerical matching coefficient can meet the diversified needs of decision-makers for the similarity matching degree and numerical matching degree of the “resource-demand” balance, providing support for energy project planning decisions.

A VIKOR-Based Sequential Three-Way Classification Ranking Method

VIKOR uses the idea of overall utility maximization and individual regret minimization to afford a compromise result for multi-attribute decision-making problems with conflicting attributes. Many researchers have proposed corresponding improvements and expansions to make it more suitable for sorting optimization in their respective research fields. However, these improvements and extensions only rank the alternatives without classifying them. For this purpose, this text introduces the three-way sequential decisions method and combines it with the VIKOR method to design a three-way VIKOR method that can deal with both ranking and classification. By using the final negative ideal solution (NIS) and the final positive ideal solution (PIS) for all alternatives, the individual regret value and group utility value of each alternative were calculated. Different three-way VIKOR models were obtained by four different combinations of individual regret value and group utility value. In the ranking process, the characteristics of VIKOR method are introduced, and the subjective preference of decision makers is considered by using individual regret, group utility, and decision index values. In the classification process, the corresponding alternatives are divided into the corresponding decision domains by sequential three-way decisions, and the risk of direct acceptance or rejection is avoided by putting the uncertain alternatives into the boundary region to delay the decision. The alternative is divided into decision domains through sequential three-way decisions, sorted according to the collation rules in the same decision domain, and the final sorting results are obtained according to the collation rules in different decision domains. Finally, the effectiveness and correctness of the proposed method are verified by a project investment example, and the results are compared and evaluated. The experimental results show that the proposed method has a significant correlation with the results of other methods, ad is effective and feasible, and is simpler and more effective in dealing with some problems. Errors caused by misclassification is reduced by sequential three-way decisions.

Dynamic multiobjective optimization via an improved r-dominance relation and a novel prediction approach

When decision makers are only interested in a portion of the Pareto optimal front (PF) in dynamic multiobjective optimization problems (DMOPs), dynamic multiobjective evolutionary algorithms (DMOEAs) need to search only for the PF portion of interest to decision makers. However, this is challenging for most existing DMOEAs, as they are designed to search the entire PF, overlooking the preferences of decision makers. Therefore, we present a novel dynamic multiobjective optimization algorithm based on decision makers’ preference information, involving an improved r-dominance relation and a response strategy. It focuses on searching for the preference solutions (the region of interest) according to the decision makers’ preference information in dynamic multiobjective optimization. The improved r-dominance relation adapts the angle to measure the closeness between the solution and the preference information, which solves the convergence problem of the r-dominance relation since the original r-dominance relation has difficulty converging to the true PF when the preference information is located in the feasible objective region. The prediction mechanism is based on the movement of the population’s special points; it helps the population make adjustments in its moving direction and step size towards the new PF when a change is detected. Experimental results show that the proposed algorithm is efficient for dynamic multiobjective optimization and is competitive compared to state-of-the-art methods.

Treatment decision-making factors among patients with cervical myelopathy: a discrete-choice experiment

BackgroundDegenerative Cervical Myelopathy is a debilitating condition and current recommendations encourage shared decision-making between surgeons and patients. However, there is limited data on patients’ values and preferences for surgical decision making. This study aimed to quantify and compare the relative importance of neurologic function, risk of future surgery, and complications to patients with cervical stenosis.MethodsPatients with cervical stenosis presenting for surgical evaluation, or post-operative cervical decompression patients, were recruited to participate. Demographic information including modified Japanese Orthopedic Association (mJOA) score, type of surgery, and complications were recorded and anonymized to study ID. Patients then completed an online discrete-choice experiment survey. In a series of 10 questions, respondents chose between two hypothetical health states defined in terms of five attributes, or “decision factors”: (i) upper extremity neurologic function, (ii) lower extremity neurologic function, (iii) risk of cervical spine surgery, (iv) dysphagia, and (v) C5 palsy. Participants were asked to choose which ‘life’ they preferred, and a regression model was used to quantify the importance of each decision factor.ResultsWe report three key findings that can aid clinicians in shared decision-making conversations: (i) all patients regard lower extremity neurologic function as the most important decision factor, (ii) dysphagia, a complication, and upper extremity neurologic function are equally important, and (iii) patients who have undergone surgery weigh neurologic function as less important, and complications as more important than patients who have not undergone surgery.ConclusionsPatient preferences for management of degenerative cervical myelopathy are influenced by several considerations including the experience of surgery itself. Communication of benefits and harms associated with surgical and conservative care can optimize shared decision making. Further research should be conducted to evaluate for decisional regret and the impact of complications to inform treatment conversations.

A Multi-Objective Energy Optimization Model for Renewable Energy Systems with Hybrid Energy Storage

Building a Renewable Energy System (RES) is a viable way to address resource depletion and accomplish decarbonization. This study presents a hybrid power system that combines wind, solar, battery, and thermal energy storage. It also examines the co-optimization of scheduling and operation for multiple objectives. The hybrid system combines the affordability of thermal energy storage (TES) with the adaptability of batteries to effectively address the issue of intermittent RES. A new approach for integrated operation is offered, which relies on the electrical block's operation limit. The planning-operation co-optimization system considers minimizing the Net Present Cost (NPC) and reducing power supply likelihood todetermine the best operation limit and sizing decision factors. The co-optimization issue is addressed using novel multi-objective decision-making (MO-DM). This approach incorporates the Decision-Maker (DM) preferences data to direct the evolutionary process toward the desired area. In addition, a data-driven prediction model captures the risks and losses associated with wind generation. The case study's findings indicate the following: (1) The data-driven system has a higher level of precision in wind power projection when compared with commonly used physical simulations. (2) The suggested MO-DM exhibits superior integration, variety, and robustness achievement in the DMchosen area compared to others. (3) The combined battery-thermal energy storing structure achieves improved economy and dependability through the optimum organized operation approach, in contrast to using a single energy storage system under various testing constraints.

Beyond risk preferences in sequential decision-making: How probability representation, sequential structure and choice perseverance bias optimal search

Sequential decision-making, where choices are made one after the other, is an important aspect of our daily lives. For example, when searching for a job, an apartment, or deciding when to buy or sell a stock, people often have to make decisions without knowing what future opportunities might arise. These situations, which are known as optimal stopping problems, involve a risk associated with the decision to either stop or continue searching. However, previous research has not consistently found a clear connection between individuals’ search behavior in these tasks and their risk preferences as measured in controlled experimental settings. In this paper, we explore how particular characteristics of optimal stopping tasks affect people’s choices, extending beyond their stable risk preferences. We find that (1) the way the underlying sampling distribution is presented (whether it is based on experience or description), (2) the sequential presentation of options, and (3) the unequal frequencies of choices to reject versus to accept significantly bias people choices. These results shed light on the complex nature of decisions that unfold sequentially and emphasize the importance of incorporating context factors when studying human decision behavior.

Multi-criteria approach for weights definition of sustainability indicators in the swine supply chain

Goal: This study presents a four-steps methodology for the definition of the weights of sustainable indicators in the Swine Supply Chain (SSC) considering the Triple Bottom-Line (TBL) perspective. Design/Methodology/Approach: The methodology proposed was based on the Bellagio principles that guide the definition of importance levels for TBL criteria. It was applied to the swine supply chain using Saaty’s scale and information obtained from managers of two agribusiness companies, adjusted by the AHP method. Results: The results showed the relevance of an appropriate definition of the preferences of decision-makers to decide on the best actions towards environmental, economic, and social sustainability. Limitations: The limitations of the proposed model are mainly its subjective nature. Despite being necessary in multi-criteria methods, it could be a limitation if more quantitative indicators are relevant or demanded. Nevertheless, the proposed methodology also provides a quantitative perspective, as a result of using the Saaty scale. Practical implications: The definition and application of importance levels within a TBL-based model can help managers to prioritize environmental, social, and/or economic dimensions under a large diversity of alternatives toward more sustainable scenarios. The AHP method used in this work is a hierarchy method, particularly appropriate for defining weights that are essentially compensatory by nature. Originality/value: The correct definition and weight of the different dimensions, sub-dimensions, and respective indicators is currently one relevant gap in the literature, limiting the design and prioritization of corrective actions in each stage of the SSC and in the different dimensions of the TBL.

Contract Farming in Low and Lower-Middle-Income Countries: Using Meta-Analysis and Factor Analysis

Purpose: Smallholder farmers' agriculture in developing countries particularly in low and lower-middle-income countries is known for poor production and productivity levels which has been related to the inadequate use of improved agricultural inputs and marketing systems, and in this case, contract farming. Therefore, this study aimed to investigate the various factors which affect contract farming among smallholder farmers in developing countries. However, trying to investigate those contract farming determinants by a single analytical method leads it to have limited findings, narrowed generalizability and difficulty in investigating major determinants that determine contract farming success. Therefore, a joint approach using factor analysis and meta-analysis can give a more comprehensive understanding of contract farming factors. Methodology: The data was gathered through a Systematic Literature Review, finding a total of 3007 studies from SCOPUS, PubMed, PubAg, and EMBASE. The PRISMA method was applied to, and finally, 35 peer-reviewed articles in English between January 1990 to September 2023 were selected. Findings: The meta-analysis showed interesting insights into factors influencing contract farming participation. Education and household size exhibited a negative association, suggesting that farmers with higher education levels and larger families may be less likely to participate in contract farming arrangements. This could be due to a preference for independent decision-making or the need for family labor in alternative income-generating activities. On the other hand, farmers with larger landholdings are more inclined to participate. To encourage broader participation, policymakers, and program designers could consider targeted outreach and support services for these specific demographics. Originality and value: Our new approach, joining meta-analysis and factor analysis, sheds innovative light on contract farming determinants. While the expected determinants like farm and household size, along with education, remained significant, our analysis showed unexpected nuances. Age of the household head emerged as potentially favoring younger, less experienced farmers. Moreover, access to extension services played an important role, while large household size might have a more complex influence depending on age composition. This comprehensive approach offers valuable insights for targeted outreach programs and collaboration with extension services to optimize contract farming adoption and success in developing countries.

Exploring treatment decision-making at diagnosis for children with advanced cancer in low- and middle-income countries

PurposeGlobal childhood cancer survival outcomes correlate with regional contextual factors, yet upfront treatment decision-making for children with advanced or poor prognosis cancer in low- and middle-income countries (LMICs) is not well understood. This study aimed to (1) characterize the landscape of contextual factors that shape physician decision-making at diagnosis for these children in LMICs and (2) describe physician rationales for if/when to offer treatment with non-curative intent, including how they define “poor prognosis” during treatment decision-making.MethodsAn international panel of pediatric oncologists practicing in LMICs participated in two focus groups structured for the collaborative generation of factors influencing treatment decision-making, including consideration of non-curative treatment pathways at diagnosis. Thematic analysis of qualitative data was conducted, followed by member checking.ResultsEleven pediatric oncologists participated, representing all global regions defined by the World Health Organization. Participants identified a broad range of factors influencing decision-making across multiple levels, including the individual, hospital, health system, community, and country levels. All participants agreed that treatment with non-curative intent could be offered at diagnosis in certain contexts, and diverse definitions for poor prognosis were described.ConclusionsUpfront treatment decision-making for children with advanced or poor prognosis cancer in LMICs is variable and challenging. Difficulties with decision-making in LMICs may be amplified by inconsistent definitions of poor prognosis and underrepresentation of the factors that influence treatment decision-making within existing decision-making frameworks or childhood cancer treatment guidelines. Future research should explore decision-making approaches, preferences, and challenges in depth from the perspectives of pediatric cancer patients, families, and multidisciplinary clinicians.

Integrating machine learning models to learn potentially non-monotonic preferences for multi-criteria sorting from large-scale assignment examples

Learning preferences from assignment examples has attracted considerable attention in the field of multi-criteria sorting (MCS). However, traditional MCS methods, designed to infer decision makers’ preferences from small-scale assignment examples, encounter limitations when confronted with large-scale data sets. Additionally, the presence of decision makers’ non-monotonic preferences for certain criteria in MCS problems necessitates accounting for potential non-monotonicity when devising preference learning methods. To address this, this paper proposes some new models to learn potentially non-monotonic preferences for MCS problems from large-scale assignment examples by leveraging machine learning models. Specifically, we first introduce the Piecewise-Linear Neural Network (PLNN) model, which leverages the threshold-based value-driven sorting procedure as the underlying sorting model and integrates a perceptron-based model to establish piecewise-linear marginal value functions to approximate real ones. On this basis, we address MCS problems with criteria interactions and extend the PLNN model to develop the Piecewise-Linear Factorization Machine-based Neural Network (PLFMNN) model by incorporating the factorization machine to factorize interaction coefficients. Training these models allows us to learn potentially non-monotonic preferences of decision makers. To illustrate the proposed models, we apply them to a red wine quality classification problem. Furthermore, we assess the performance of the proposed models through computational experiments on both artificial and real-world data sets. Additionally, we conduct statistical tests to ascertain the significance of the performance differences. Experimental results reveal that the proposed models are comparable to the multilayer perceptron model and outperform other baseline models on most data sets, thus affirming their efficacy. Finally, we conduct some sensitivity analysis to assess the impact of certain parameters on the performance of the proposed models and compare them with existing studies from a theoretical perspective, further demonstrating their effectiveness.

Examining Permanent Contraception for Children, Adolescents, and Young Adults With Intellectual Developmental Disorder: Ethical, Legal, and Medical Considerations: Clinical Report.

There have been significant advances in the medical and surgical options available for contraception and management of menses for individuals, including those with intellectual developmental disorder. This new statement frames the ethical, legal, and medical issues of permanent contraception in children, adolescents, and young adults with intellectual developmental disorder, emphasizing the importance of utilizing long-acting reversible and minimally invasive treatments, whenever possible. The historical use and abuse of permanent contraception is briefly reviewed, providing the foundation for ongoing ethical and legal considerations, including issues of informed consent. The authors then discuss medical decision-making and patient preferences that should be considered and make recommendations to providers who are contemplating permanent contraception therapies in this population.

Do Affective Polarization and Populism Affect the Support for Holding Referendums?

What populism and polarization have in common is that their relationship with democracy is an ambiguous one. Studies have found that certain degrees of polarization can be helpful for citizens to make up their minds about their choices and because of that encourage them to democratic participation. Similarly, populism can help increase participation by, for example, presenting policies in a simpler language. Citizens with less political interest and political knowledge might be incited to participate in elections and democratic politics in general. However, high levels of polarization lead to the irreconcilability of factions and thereby to gridlock. Democracy can be regarded as incapable of solving citizens’ problems. Likewise, populism can be destructive to democracy when occurring in certain forms and degrees. While populism is not per se antidemocratic, populist parties and leaders, when in power, repeatedly challenge democratic elements. To disentangle how polarization and populism affect democracy, I focus on certain specifics of these three concepts (democracy, populism, and polarization). Namely, I analyze how affective polarization and individual-level populism affect the support for the direct democratic instrument of holding referendums. Drawing on survey data from Austria and Germany, I find that being affectively polarized has a positive effect on the support for holding referendums. However, this effect is moderated by citizens’ individual-level populism. Thus, this study provides insights into citizens’ preferences for democratic decision-making, dependent on their levels of affective polarization and populism.