Decision Maker's Preferences Research Articles

Multi-Objective Optimization for Seismic Structural Investigation and Maintenance Strategies of Bridges in Service

With the continuous updating of seismic design codes for Bridges, the new regulations have higher requirements for the seismic structure and mechanical properties of old Bridges. In order to make the decision of seismic structure strengthening more scientifically, with the goal of optimal overall performance, minimum economic cost and minimum environmental impact, the optimal model of seismic strengthening sustainable strategy is established. Firstly, according to the results of the seismic measures of the existing Bridges, the corresponding reinforcement measures are proposed for the structural problems that do not meet the seismic measures. Then the utility evaluation of the three objectives of the existing bridge was carried out. At last, NSGA-Ⅱ algorithm was used to build a multi-objective maintenance decision optimization model of the bridge network. Combined with the weight of the decision-maker's preference, TOPSIS algorithm was used to obtain the optimal solution of the target, and decision-making suggestions were provided for the relevant departments. The results show that there are many structural problems in the new evaluation of the old bridge, and the maintenance of the structural problems are more feasible and effective than the improvement of the traditional mechanical properties. Combined with the comprehensive evaluation utility and reinforcement objective function, the maintenance decision can be better under different objectives. The reinforcement maintenance decision model has the ability to improve the comprehensive performance and sustainability of the bridge to the maximum extent within the limited budget.

Multi-item order quantity optimization through stochastic goal programing

AbstractManaging multi-item economic order quantity (MIEOQ) problems within an uncertain business environment is a critical challenge. Decision-makers, with a comprehensive understanding of organizational goals and risk tolerances, play a pivotal role in this context. However, existing solutions often inadequately consider decision-maker preferences in MIEOQ problem-solving. The literature suggests that integrating the concept of satisfaction function with stochastic goal programming (SGP) can address this issue. However, the existing SGP approaches struggle with the challenge of effective goal setting. Additionally, employing distinct satisfaction functions for each uncertain goal can complicate threshold setting, diminishing their effectiveness. To tackle these challenges, we introduce a straightforward, yet effective approach called aspiration-free goal programming (AFGP) and integrate it with a unified satisfaction function. AFGP operates by minimizing expected values of deviation variables, eliminating the challenging task of goal setting under uncertainty. A unified satisfaction function is a singular metric applied uniformly across multiple goals, offering a consistent framework for evaluating performance across diverse objectives. This integration forms a preference-sensitive framework that not only captures nuanced trade-offs between conflicting objectives but also enhances decision quality and stakeholder satisfaction. By emphasizing the importance of decision-maker’s preferences and addressing identified issues, our research introduces a practical and effective approach for achieving balanced solutions in uncertain MIEOQ environments.

Value Evaluation Model of Multi-Temporal Energy Storage for Flexibility Provision in Microgrids

With the advancement of distributed power generation technology and the deepening of the low-carbon transformation of energy structure, a high proportion of renewable energy has become an inevitable trend in future energy systems, especially for microgrids. However, the volatility and uncertainty associated with renewable energy pose significant challenges to the secure and stable operation of power systems, necessitating the exploration of the flexible regulation of resources. Energy storage, as a crucial flexible resource characterized by technological diversity and a variety of regulation capabilities, has been extensively studied and applied. Nonetheless, the high investment costs and limited returns of energy storage technology, coupled with the ambiguous utility in different scenarios under the current electricity market’s framework, complicate its broader application. To thoroughly analyze the utility of energy storage in facilitating flexible adjustments in microgrids, this study developed a composite weight-TODIM (an acronym in Portuguese for interactive and multi-criteria decision making) model for assessing the utility of energy storage that incorporates heterogeneity in the risk preferences. This model enabled a comparative analysis of the utility of energy storage technology across multiple scenarios, taking the risk preferences of decision-makers into account, thereby providing strategic insights for the application of multi-temporal energy storage in microgrids. The feasibility and effectiveness of the model were validated through a case study analysis.

A novel multi-source TWD model based on multi-granularity ball for multiple decision makers

Three-way incomplete multi-attribute decision-making model (TWIMADM) for multiple decision makers is an important topic in data science, which is an effective tool for decision making. However, calculation results of neighborhood blocks are still affected by neighborhood radius. In addition, most of the existing models without considering the information from the evaluations of multiple decision makers separately is difficult to obtain a comprehensive assessment. To address these problems, this paper builds a novel multi-source TWD model based on multi-granularity ball. First, the well-established granular ball is extended to multi-granularity, by which a weighted conditional probability is generated to divide the concerned region into three parts: positive, negative and boundary region. The calculation of the multi-granularity ball is independence of the neighborhood radius parameter. Subsequently, the joint entropy is developed by using the defined multi-granularity to obtain the optimal decision. Furthermore, the relative utility function is utilized to capture the preferences of decision makers, the classification and ranking of each object are determined by calculating the threshold value and expected utility value. Evaluation information from all decision makers are combined and fused. Finally, a novel TWD model is developed to address the challenges of incomplete multi-attribute decision-making problems involving multiple decision makers. The comparative case analyses and experimental evaluations have demonstrated the stability, validity, and feasibility of our model.

СРАВНИТЕЛЬНЫЙ АНАЛИЗ МЕТОДОВ РЕШЕНИЯ ТРАНСПОРТНЫХ МНОГОКРИТЕРИАЛЬНЫХ ЗАДАЧ

The paper provides a comparative analysis of approaches to solving multicriteria problems, based on advanced methods that take into account the preferences of decision makers

How False Memory and True Memory Affect Decision Making in Older Adults: A Dissociative Account.

Remembering past rewarding experiences plays a crucial rule in guiding people's decision making in the future. However, as people age, they become less accurate in remembering past events and more susceptible to forming false memories. An important question is how the decline of episodic memory and the increase of false memory may affect older adults' decision-making performance. The current study used a newly developed paradigm in which the Deese-Roediger-McDermott (DRM) false memory paradigm was combined with a reward learning task to create robust false memories of rewarding experiences. Participants learned that some DRM picture lists brought them a monetary reward and some DRM picture lists did not bring reward. Later, their memories were tested and decision-making preferences were measured. We found that older and younger adults had almost equivalent false and true memories under the rewarding context, but older adults showed significantly lower decision-making preferences for lure pictures and rewarded pictures than younger adults. Furthermore, true and false memories were a stronger predictor of decision-making preferences for younger than for older adults. These results together suggest an age-related dissociation between memory and decision making that older adults may be less efficient in using their memory to guide decision making than younger adults. Future research may further investigate its underlying mechanisms and develop potential interventions aiming at strengthening the connection between memory and decision making in older adults to help improve their decision-making performance.

How different attributes are weighted in professionals' decision-making in Pediatric Dentistry-a protocol for guiding discrete choice experiment focused on shortening the evidence-based practice implementation for dental care.

Important evidence has been constantly produced and needs to be converted into practice. Professional consumption of such evidence may be a barrier to its implementation. Then, effective implementation of evidence-based interventions in clinical practice leans on the understanding of how professionals value attributes when choosing between options for dental care, permitting to guide this implementation process by maximizing strengthens and minimizing barriers related to that. This is part of a broader project investigating the potential of incorporating scientific evidence into clinical practice and public policy recommendations and guidelines, identifying strengths and barriers in such an implementation process. The present research protocol comprises a Discrete Choice Experiment (DCE) from the Brazilian oral health professionals' perspective, aiming to assess how different factors are associated with professional decision-making in dental care, including the role of scientific evidence. Different choice sets will be developed, either focusing on understanding the role of scientific evidence in the professional decision-making process or on understanding specific attributes associated with different interventions recently tested in randomized clinical trials and available as newly produced scientific evidence to be used in clinical practice. Translating research into practice usually requires time and effort. Shortening this process may be useful for faster incorporation into clinical practice and beneficial to the population. Understanding the context and professionals' decision-making preferences is crucial to designing more effective implementation and/or educational initiatives. Ultimately, we expect to design an efficient implementation strategy that overcomes threats and potential opportunities identified during the DCEs, creating a customized structure for dental professionals. https://osf.io/bhncv .

Personalized multi-attribute recommendation method based on partial order set

With the rapid development of big data and continuous optimization of online shopping platforms, personalized recommendation has become a standard feature of recommendation methods. In order to effectively provide personalized recommendations to customers, improve recommendation accuracy, and customer satisfaction, it is necessary to consider customers’ preferences for multiple product attributes when making product recommendations. However, existing recommendation methods require precise calculation of product attribute weights, which is computationally expensive, complex, and often results in unstable weight values. This paper proposes a multi-attribute recommendation method based on consumer decision preference information that overcomes the need for weights and reflects personalized customer preferences. Based on the acquisition of customer product attribute preference sequences, a partial order relation for recommended products is constructed using partial order set theory. Finally, the recommended products are determined through the partial order Hasse diagram, where the top layer elements of the Hasse diagram represent the recommended product set. This method addresses challenges that traditional content-based recommendations cannot overcome. The experiment in this paper uses a dataset of 30,000 records from Beeradvocate beer reviews. The experimental results show that, compared to traditional multi-attribute recommendation methods, this method only requires decision-maker preference information to complete product recommendations, requiring less information and having lower computational costs, resulting in more robust results.

Role Preferences in Treatment Decisions Among Patients with Relapsing-remitting Multiple Sclerosis (P1-6.001)

Role Preferences in Treatment Decisions Among Patients with Relapsing-remitting Multiple Sclerosis (P1-6.001)

Social preferences and psychopathy in a sample of male prisoners—a pilot study

Social decisions are influenced by a person’s social preferences. High psychopathy is defined by antisocial behaviour, but the relationship between psychopathy and social preferences remains unclear. In this study, we used a battery of economic games to study social decision-making and social preferences in relation to psychopathy in a sample of 35 male prison inmates, who were arrested for sexual and severe violent offenses (mean age = 39 years). We found no evidence for a relationship between social preferences (measured with the Dictator and Ultimatum Games, Social Value Orientation, and one-shot 2 × 2 games) and psychopathy (measured by the overall Hare Psychopathy Checklist-Revised score and both factors). These results are surprising but also difficult to interpret due to the small sample size. Our results contribute to the ongoing debate about psychopathy and social decision-making by providing crucial data that can be combined with future datasets to reach large sample sizes that can provide a more nuanced understanding about the relationship between psychopathy and social preferences.

Stochastic multi-attribute group decision-making for long-term comprehensive operation of cascade reservoirs under multiple uncertainties

Study regionThe Three Gorges cascade (TGC) reservoirs, China. Study focusSince uncertainties inherent in inflow forecasting, elevation-storage, discharge capacity, flood routing, and the preferences of decision makers (DMs) persist in the attribute measurements (AMs) and weights (AWs), problems where multiple DMs select the best compromise scheme for the long-term comprehensive operation of cascade reservoirs considering numerous attributes become a stochastic multi-attribute group decision-making (MAGDM) process. To represent uncertain AMs and AWs and consider the bounded rationality of DMs, we propose a stochastic MAGDM with integrated stochastic multicriteria acceptability analysis (SMAA) and gray cumulative prospect theory (GCPT), namely, SMAA-GCPT. After quantifying the uncertainties, SMAA-GCPT is used to model uncertain AMs and imperfect AWs with calculable probability distributions and map the sampled AMs and AWs to utility values to reflect the overall performance assuming bounded rationality. New hydrological insights for the regionExperiments on the TGC indicate that SMAA-GCPT can effectively differentiate alternative schemes and select the best compromise scheme to harmonize the conflicting benefits and risks of the TGC, with higher reliability than SMAA-2 and SMAA-GRA. The influence of uncertain AMs on the reliability of SMAA-GCPT is more obvious than that of uncertain AWs. The decision-making error risk is primarily sensitive to the inflow forecasting uncertainty, followed by the flood routing uncertainty, and is less affected by the uncertainties in elevation-storage and discharge capacity curves.

Conflict analysis of multiple decision-makers ecological environment governance of water resources based on graph model with heterogeneous preference in China's South-to-North water transfer project

Ecological environment governance of water resources is the foundation and key measures to ensure good water quality in China's South-to-North Water Transfer Project. However, as a limited rational economic person, considering their economic benefit, different involvers in China's South-to-North Water Transfer Project show different types of preference relations and make different action decisions as well as play conflicts with each other, which have a negative effect on ecological environmental governance. To overcome above-described shortcomings, heterogeneous preferences are integrated into graph model for conflict resolution (GMCR) to resolve ecological environment governance conflict of water resources. Specifically, first, four different structures of preference expression, including preference orderings, utility functions, multiplicative preferences, and additive preferences, are used to represent decision-maker's (DM's) preference information over states. Then, a novel preference ranking method is designed to calculate the ordinal preferences over all states by using matrix representation. Based on this, a set of heterogeneous stability definitions is proposed in GMCR to describe human diversified choice and behavior in detail, and it is applied to the ecological environment governance conflict of water resources in China's South-to-North Water Transfer Project. The heterogeneous stability analysis result showed that incentive policies used by water resources agencies can effectively encourage local governments and enterprises to actively participate in the ecological environment governance, and promote the ecological environment governance conflict to evolve into a state of equilibrium.

Factors influencing caterers' selection of cooking oils in Sekondi-Takoradi, Ghana

Background: Cooking oil is a plant or animal liquid fat used mostly in frying, baking, and other types of cooking methods. Oils are the basis for many recipes of caterers. There is a variety of cooking oils on the market, each with their specific benefits and nutritional value. This study explored factors that caterers consider when choosing cooking oils and how frequently they use them. Method: Forty-five hotels and restaurants were sampled from the Sekondi-Takoradi Metropolitan area: 30 hotels (3 four-star, 5 three-star, 22 two-star) and 15 restaurants. Self-administered questionnaires were used to gather data from 198 respondents. Descriptive statistics were used to analyze the data. Results: The study found out that caterers and clients mainly chose cooking oils based on nutritional value, quality, flavour/taste, and price. Respondents considered quality metrics on oil labels such as smoke point and fatty acid profiles, when selecting good frying oils to purchase. Conclusion: The accuracy of the cooking oil information obtained from advertisements is unclear. Further consumer education on interpreting and verifying claims made in commercial advertising may be beneficial. The research offers insights into the decision-making process and preferences caterers employ when selecting cooking oils and their typical frying frequencies.

Rational satisficing heuristics as determinants of online search behavior

We design a set of satisficing heuristic algorithms that mimic the online information retrieval behavior of rational decision makers (DMs) as reflected in their click through rates (CTRs). We illustrate how basic heuristic algorithms formalizing binary decision trees composed by 21 nodes and requiring DMs to observe one satisficing alternative lack the structural capacity to characterize the retrieval process. The algorithm requiring DMs to observe two satisficing alternatives –formalizing binary decision trees composed by 111 nodes – provides a sufficient approximation to their CTRs. Adding a third alternative – accounting for 351 nodes – delivers an almost identical set of CTRs to those displayed by DMs. The mimicking quality of the heuristic algorithms prevails as alternatives are added up to include the ten ranked within the first page of search results, incorporating 2,047 nodes to formalize the corresponding retrieval process. The set of algorithms bridges the gap between purely rational decision theory and heuristic behavior, illustrating how the CTRs observed can be generated by rational DMs performing a sequential search process while aiming to observe two or three satisficing alternatives. The decision-tree algorithmic structures presented are sufficiently malleable to introduce any potential modification to the beliefs and preferences of DMs and study its consequences in terms of CTRs.

Evaluation of intercity road passenger transportation using a novel double-frontier game-regret-cross-efficiency

The Cross-Efficiency (CE) method, based on Data Envelopment Analysis (DEA), is a commonly used approach to measure the efficiency of Decision-Making Units (DMUs). Various secondary goal models can yield different efficiency results. The recently developed Game CE (GCE) method aims to address this issue and achieve convergence efficiencies. However, the conventional GCE was developed under the unrealistic assumption that Decision Makers (DMs) are perfectly rational, as it relies on the arithmetic mean aggregation method. Additionally, the GCE evaluates DMUs based on efficient reference points, disregarding the pessimistic perspective. The study introduces a new aggregation method called Regret-Consensus Aggregation (RCA) that captures the subjective preferences of DMs. GCE-RCA scores are obtained by incorporating RCA into the GCE. A new method called Game Cross In-Efficiency (GCIE) with RCA (GCIE-RCA) has been developed to incorporate anti-efficient reference points into the evaluation process. A Double-Frontier GCE based on the RCA (DFGCE-RCA) is developed from two perspectives. The DFGCE-RCA is implemented to evaluate the performance of the Iranian Inter-City Road Passenger Transportation (IC-RPT) system based on six key performance indicators. DFGCE-RCA has several advantages in addition to those of GCE. Firstly, considering two perspectives provides more comprehensive results. Additionally, it can capture the subjective perspectives of DMs. Lastly; it enables the aggregation process to reach a consensus.

Exploring the medical decision-making patterns and influencing factors among the general Chinese public: a binary logistic regression analysis

ObjectiveWith the ongoing evolution of the healthcare system and shifts in cultural paradigms, there is a pressing need to delve into the medical decision-making behaviors of general Chinese public and understand their underlying motivations. This research seeks to elucidate the prevailing tendencies in these decision-making processes and to empirically validate the pivotal factors that shape their choices, offering valuable insights for healthcare policymakers and institutions.MethodA comprehensive survey was administered to 2,696 Chinese residents to examine their medical decision-making patterns. These patterns were classified into two primary categories: Unilateral Decision-making (Doctor-dominant, Family-centric, and Patient-driven subtypes) and Collaborative Decision-making (Doctor-led, Doctor-Patient, Patient-Family, and Doctor-Patient-Family subtypes). Binary logistic regression analysis was employed to empirically pinpoint the significant factors influencing these decision-making frameworks.ResultsThe study's analysis reveals distinct preferences in medical decision-making among Chinese residents. In the Collaborative Decision-making category, chosen by 70.81% of participants, the subtypes are as follows: Doctor-led (29.90%), Doctor-Patient (13.54%), Patient-Family (2.93%), and Doctor-Patient-Family (24.44%). The Unilateral Decision-making, preferred by 29.19%, includes Doctor-dominant (23.22%), Family-centric (1.74%), and Patient-driven (4.23%) models. The preference for Collaborative Decision-making is associated with higher educational levels, specific marital statuses (particularly married but childless), and choices of rural residents' basic medical insurance or occupational basic medical insurance. In contrast, Unilateral Decision-making correlates with males, individuals with religious beliefs, certain occupational roles (like civil servants), and holders of commercial or publicly funded medical insurance.ConclusionThis study elucidates the complex interplay of socio-cultural and individual determinants shaping medical decision-making in China. The findings reveal a marked inclination towards collaborative models, closely linked to educational level, marital status, and specific insurance types, reflecting an evolving trend towards participatory healthcare. Simultaneously, the persistence of unilateral models, influenced by gender, religious beliefs, and occupational roles, highlights the heterogeneity within Chinese healthcare preferences. These insights are crucial for policymakers and healthcare practitioners, underscoring the need for adaptable, culturally attuned healthcare frameworks that cater to this diversity, thereby enhancing patient engagement and healthcare efficacy.

Multi-Objective Bayesian Optimization with Active Preference Learning

There are a lot of real-world black-box optimization problems that need to optimize multiple criteria simultaneously. However, in a multi-objective optimization (MOO) problem, identifying the whole Pareto front requires the prohibitive search cost, while in many practical scenarios, the decision maker (DM) only needs a specific solution among the set of the Pareto optimal solutions. We propose a Bayesian optimization (BO) approach to identifying the most preferred solution in the MOO with expensive objective functions, in which a Bayesian preference model of the DM is adaptively estimated by an interactive manner based on the two types of supervisions called the pairwise preference and improvement request. To explore the most preferred solution, we define an acquisition function in which the uncertainty both in the objective function and the DM preference is incorporated. Further, to minimize the interaction cost with the DM, we also propose an active learning strategy for the preference estimation. We empirically demonstrate the effectiveness of our proposed method through the benchmark function optimization and the hyper-parameter optimization problems for machine learning models.

Proportional Aggregation of Preferences for Sequential Decision Making

We study the problem of fair sequential decision making given voter preferences. In each round, a decision rule must choose a decision from a set of alternatives where each voter reports which of these alternatives they approve. Instead of going with the most popular choice in each round, we aim for proportional representation, using axioms inspired by the multi-winner voting literature. The axioms require that every group of α% of the voters, if it agrees in every round (i.e., approves a common alternative), then those voters must approve at least α% of the decisions. A stronger version of the axioms requires that every group of α% of the voters that agrees in a β fraction of rounds must approve β⋅α% of the decisions. We show that three attractive voting rules satisfy axioms of this style. One of them (Sequential Phragmén) makes its decisions online, and the other two satisfy strengthened versions of the axioms but make decisions semi-online (Method of Equal Shares) or fully offline (Proportional Approval Voting). We present empirical results for these rules based on synthetic data and U.S. political elections. We also run experiments using the moral machine dataset about ethical dilemmas. We train preference models on user responses from different countries and let the models cast votes. We find that aggregating these votes using our rules leads to a more equal utility distribution across demographics than making decisions using a single global preference model.

Corporate Governance Indicators and Innovation in the Chinese Market

The importance of innovation as an indispensable driving force for the development of a quality economy, for the sustainability of national economies and for the support of enterprises in breaking through development bottlenecks cannot be overstated. Innovation is a key growth strategy for all businesses. It is centered on the chief executive officer (CEO) and is directly related to the cognitive level and decision-making preferences of executives. Based on the principal-agent theory and Upper Echelons Theory, this paper constructs a model to evaluate the impact of CEOs on corporate innovation using a sample of Chinese listed companies from 2008 to 2022. Based on China's political and economic system, this paper selects six relevant characteristics of CEOs, including gender, age, educational background, tenure, compensation and political affiliation to decide whether the CEO is suitable for the firm. At the same time, this paper also measures corporate innovation standards by the number of patents filed by the company, and thus finds that the CEO index has a positive and significant impact on corporate innovation. In addition, in order to address the endogeneity issue, the paper also applies a series of robustness tests, including alternative measures of firm innovation and alternative models (Poisson models), and finds that the results are consistent with the main regression results. Finally, the paper provides empirical evidence as a means of demonstrating the key driving role played by CEOs in corporate innovation. The implications of the above findings for corporate governance, including board shareholders and policymakers, are clear.

Oral health-related quality of life and reasons for discontinuing partial removable dental prosthesis usage: a cross-sectional study with one to seven years of follow-up

BackgroundIn partial edentulous individuals, a partial removable dental prosthesis (PRDP) is a common dental replacement option to improve oral function and quality of life. However, some patients discontinue using their denture over time. The aim of this study was to determine the prevalence and characteristics of partial edentulous patients who no longer wear their dentures, explore their reasons, and assess their oral health-related quality of life (OHRQoL).MethodologyThis cross-sectional study, conducted at Chulalongkorn University Dental School from 2013 to 2019, involved patients who received PRDP treatment. They were contacted via phone calls and asked about their denture usage. Eligible participants were patients who had stopped or rarely used their PRDPs. Data on oral status, health insurance, and PRDP variables were collected from hospital records. Telephone interviews were conducted to collect the initial reasons for seeking PRDP treatment, reasons for discontinuation, desire for a new PRDP, and OHRQoL. The Oral Impacts on Daily Performances index was used to assess the OHRQoL. The score was dichotomized into the absence or presence of oral impacts. Chi-square tests and multivariable binary logistic regression were employed to determine the associations between oral impacts and various factors in the participants who discontinued PRDP usage.ResultsAmong the 975 contacted participants, 175 (17.9%) discontinued using their PRDPs. Most of these individuals had at least 20 remaining natural teeth and/or 4 posterior occluding pairs. The primary initial reason for seeking PRDP treatment was often based on a dentist’s suggestion. Although many participants reported no impact on OHRQoL and did not express the need for new PRDPs, those experiencing oral impacts were more likely to seek replacements.ConclusionsWith up to 7 years follow-up duration, individuals with partial edentulism and sufficient remaining functional dentition without oral impacts were more inclined to discontinue PRDP usage. Those with maxillary anterior teeth loss were less likely to discontinue using their PRDP. The primary initial reason for seeking PRDP treatment was often a dentists’ suggestion. However, the individuals reporting oral impacts expressed their needs for new denture replacements. This highlights the significance of incorporating patient needs and preferences in prosthodontic decision-making.