Multiplicative Preference Research Articles

An indirect multicriteria group decision-making method with heterogeneous preference relations and reliabilities of decision-makers

This study proposes a new indirect method to solve multicriteria group decision-making (MCGDM) problems with heterogeneous preference relations and decision-makers’ reliabilities. To unify the heterogeneous preference relations, the transformation of multiplicative preference relations (MPRs), fuzzy preference relations (FPRs), intuitionistic fuzzy preference relations (IFPRs), and linguistic preference relations (LPRs) into distributed preference relations (DPRs) is analyzed. As consistency among DPRs is constructed under the assumption that decision-makers are bounded rationality, the abstract transformation of MPR, FPR, IFPR, and LPR into DPR is defined, and its reality depends on the preferences of decision-makers. Two important properties of abstract transformation, namely, internal consistency and restricted max-max transitivity, were theoretically verified. The reliabilities of decision-makers are involved in MCGDM by estimating the reliability of each criterion to improve solution quality. Based on the four types of abstract transformations, a process for analyzing MCGDM problems with heterogeneous preference relations and decision-makers’ reliabilities is developed. The proposed method is used to analyze a new business selection problem for an enterprise located in Changzhou, Jiangsu, China to demonstrate its applicability and validity.

Consensus reaching with minimum adjustment and consistency management in group decision making with intuitionistic multiplicative preference relations

Intuitionistic multiplicative preference relation (IMPR) is an efficient tool for describing the paired comparisons between objectives in group decision making (GDM). Existing GDM methods with IMPRs overlook the hesitancy of IMPRs and lack a feedback mechanism of adjustment in the consensus reaching process (CRP). Thus, to ensure that the adjusted IMPRs can provide reliable information for decision making, the hesitancy measure for IMPRs is first incorporated into the process of enhancing consistency and consensus. A new consistency index for IMPRs is defined based on distance measure. Some goal programming models are constructed for enhancing consistency of IMPR. For a collection of IMPRs with unacceptable consistency and consensus, based on the optimization model and the identification rule and direction rule (IR-DR), an interactive iterative algorithm is designed for enhancing consistency and consensus, which can greatly save the completion time of consensus reaching and maintain primitive information to the maximum. Subsequently, a distance-based aggregation model is constructed to derive the group IMPR by considering different experts’ decision-making principles. Thereby, a new GDM method with IMPRs considering minimum adjustment is proposed. Moreover, the rationality and effectiveness of the proposed method are verified through a numerical example and some related simulation experiments. Meanwhile its merits are illustrated by comparison analyses.

Group decision-making with multiplicative probabilistic linguistic preference relations based on consistency improvement and upgraded multiplicative DEA cross-efficiency

Multiplicative probabilistic linguistic preference relation (MPLPR) has been widely used by decision-makers (DMs) to tackle group decision-making (GDM) problems. However, due to the complexity of the decision-making circumstance and individual subjectivity of DMs, they often provide inconsistent MPLPRs which often lead to unreasonable decision results. To solve this problem, this paper investigates a novel approach to GDM with MPLPRs based on consistency improvement and upgraded multiplicative data envelopment analysis (DEA) cross-efficiency. First, the concept of sequential consistency of MPLPR is defined. Then, a consistency improvement algorithm is proposed, which can convert any unacceptable consistent MPLPR into an acceptable one. Furthermore, we use geometric averages to transform MPLPR into multiplicative preference relation (MPR). Meanwhile, considering the conservative psychology of DMs, an upgraded multiplicative DEA cross-efficiency model based on the pessimistic criterion is constructed, which can derive the priority vector of MPLPR. Therefore, we can obtain the rational ranking results for all alternatives. Finally, a case analysis of emergency logistics under COVID-19 is provided to illustrate the validity and applicability of the proposed approach.

An evaluation of preference stability within MSWO preference assessments for children with autism.

Previous research has commonly evaluated preference stability over time and across multiple preference assessment administrations. No studies have evaluated shifts in preference across consecutive rounds of a single preference assessment, where rounds refer to each time the experimenter resets the stimulus-presentation array. The purpose of the present study was to examine the stability of stimulus selections across successive rounds of a multiple-stimulus-without-replacement (MSWO) preference assessment with different classes of stimuli for children with autism. The study involved a secondary data analysis and calculation of preference stability across consecutive rounds using Spearman rank-order correlation coefficients (Spearman's ) for 17 participants across 40 MSWO preference assessments. Patterns of preference stability were observed in 24 out of the 40 assessments (60%) indicating that children's preferences in this study were slightly more likely to be classified as stable than other observed patterns of responding.

Multiple preferences induced aggregation with uncertainty influences in group evaluation of water resource management

In many multi criteria group decision making problems, the individual evaluation values offered by experts are with uncertainties. Therefore, when assigning weights to those experts using preferences induced weights allocation, we can have two types of bi-polar preferences. The first one is the optimism-pessimism preference over evaluation values; the second one is the uncertainty aversion preference over the attached numerical certainty/uncertainty degrees. When performing preferences induced weights allocation, the certainty/uncertainty degrees will affect the optimism-pessimism preference induced weights allocation because the magnitudes of those evaluation values might not be the exact ones. Moreover, the importance of those experts in multi criteria group decision making can also have influence over the two types of preference induced weights allocation processes, and the importance can also be with uncertainties and can be expressed using basic uncertain information. Therefore, to handle this situation with multiple inducing variables and uncertainties, we simultaneously consider the influence of the uncertainties attached to evaluation values and the influence of uncertain importance of experts, and thus we at the same time adopt the method of confidence threshold and the method of uncertain importance level function to propose some synthesized method to adjust the induced weights allocation processes. We also propose a complete multi criteria group decision making problems to show the feasibility and reasonability of the proposed decision model for the complex situation where both evaluation values and expert importance are expressed by basic uncertain information.

Revisiting the consistency improvement and consensus reaching processes of intuitionistic multiplicative preference relations

Revisiting the consistency improvement and consensus reaching processes of intuitionistic multiplicative preference relations

A Blockchain-Based Distributed Computational Resource Trading Strategy for Internet of Things Considering Multiple Preferences

The architecture of cloud–edge collaboration can improve the efficiency of Internet of Things (IoT) systems. Recent studies have pointed out that using IoT terminal devices as destinations for computing offloading can promote further optimized allocation of computational resources. However, in practice, this idea encounters the problem that participants might lack the motivation to take over computational tasks from others. Although the edge and the terminal are provided with symmetrical positions in collaborative offloading, their computational resources and capabilities are asymmetric. To mitigate this issue, this paper designs a distributed strategy for the trading of computational resources. The most prominent feature of our strategy is its multi-preference optimization objective that takes into account the overall satisfaction with task delay, energy cost, trading prices, and user reputation of participants. In addition, this paper proposes a system architecture based on the Blockchain-as-a-Service (BaaS) design to give full play to the good distributed technology features of blockchain, such as decentralization, traceability, immutability, and automation. Meanwhile, BaaS delivers decentralized identifier (DID) based distributed identity infrastructure for the distributed computational resource trading stakeholders as well. In the simulation evaluation, we compare our trading strategy based on a matching mechanism called multi-preference matching (MPM) to trading using the classical double auction (DA) matching mechanism. The results show that our computational resource trading strategy is able to offload and execut more tasks, achieving a better throughput compared to the DA-based strategy.

Interactive Multifactorial Evolutionary Optimization Algorithm with Multidimensional Preference Surrogate Models for Personalized Recommendation

Interactive evolutionary algorithms (IEAs) coupled with a data-driven user surrogate model (USM) have recently been proposed for enhancing personalized recommendation performance. Since the USM relies on only one model to describe the full range of user preferences, existing USM-based IEAs have not investigated how knowledge migrates between preference models to improve the diversity and novelty of recommendations. Motivated by this, an interactive multifactorial evolutionary optimization algorithm with multidimensional preference user surrogate models is proposed here to perform a multi-view optimization for personalized recommendation. Firstly, multidimensional preference user surrogate models (MPUSMs), partial-MPUSMs, and probability models of MPUSMs are constructed to approximate the different perceptions of preferences and serve for population evolution. Next, a modified multifactorial evolutionary algorithm is used for the first time in the IEAs domain to recommend diverse and novel items for multiple preferences. It includes initialization and diversification management of a population with skill factors, recommendation lists of preference grading and interactive model management of inheriting previous information. Comprehensive comparison studies in the Amazon dataset show that the proposed models and algorithm facilitate the mining of knowledge between preferences. Eventually, at the cost of losing only about 5% of the Hit Ratio and Average Precision, the Individual Diversity is improved by 54.02%, the Self-system Diversity by 3.7%, the Surprise Degree by 2.69%, and the Preference Mining Degree by 16.05%.

Complex economic decisions from simple neurocognitive processes: the role of interactive attention.

Neurocognitive theories of value-based choice propose that people additively accumulate choice attributes when making decisions. These theories cannot explain the emergence of complex multiplicative preferences such as those assumed by prospect theory and other economic models. We investigate an interactive attention mechanism, according to which attention to attributes (like payoffs) depends on other attributes (like probabilities) attended to previously. We formalize this mechanism using a Markov attention model combined with an accumulator decision process, and test our model on eye-tracking and mouse-tracking data in risky choice. Our tests show that interactive attention is necessary to make good choices, that most participants display interactive attention and that allowing for interactive attention in accumulation-based decision models improves their predictions. By equipping established decision models with sophisticated attentional dynamics, we extend these models to describe complex economic choice, and in the process, we unify two prominent theoretical approaches to studying value-based decision making.

Efforts To Improve The Quality Of Islamic Religious Education And Thoughts Through The Jigsaw Method In Namira Junior High School Probolinggo City

Efforts to improve the learning quality of Class VII Junior High School Students in Islamic Religious Education Subjects with the Jigsaw Type Learning Model at Namira Middle School in Probolinggo City for the 2021/2022 Academic Year. This research is a classroom action research that aims to improve the learning quality of students of SMP Namira City Probolinggo class VII-A with a jigsaw type of learning model. The subjects of this study were students of class VII-A SMP Namira Probolinggo City as many as 22 students. The information collection tool used is in the form of observation sheets and ordinary multiple preference objective tests, based on the results of observations in cycle I and cycle II activities, there is an increase in the quality of student learning. Universally obtained 50% of students in the first cycle and 90, 90% in the second cycle. This increase in learning quality took place because each student discussed the division of his group members into expert groups based on the teacher's direction, as well as in the group from which the students were eager to present the results of the discussion. Next, it was found that the increase in learning outcomes that appeared in the gain index criteria for each student were 11 moderate students and 11 high students in the first cycle, 20 high students in the second cycle.

Group decision-making with interval multiplicative preference relations

Group decision-making with interval multiplicative preference relations

Consensus Reaching with Minimum Adjustment and Consistency Management in Group Decision Making with Intuitionistic Multiplicative Preference Relations

Consensus Reaching with Minimum Adjustment and Consistency Management in Group Decision Making with Intuitionistic Multiplicative Preference Relations

Consistency and Consensus Enhancing in Group Decision Making with Interval-Valued Intuitionistic Multiplicative Preference Relations Based on Bounded Confidence

Consistency and Consensus Enhancing in Group Decision Making with Interval-Valued Intuitionistic Multiplicative Preference Relations Based on Bounded Confidence

Preferensi Sumber Informasi dalam Persiapan Olimpiade Sains Nasional Pada Kalangan Siswa SMA di Kota Surabaya

The preference of information sources in preparation for the National Science Olympiad among high school students in the city of Surabaya, has the aim of explaining and knowing how the preferences or selection of information sources among high school students are to prepare themselves to confront OSN. The number of sources of information widespread among high school students will lead to a choice of information sources that can later be used as references by students to meet academic needs and for the needs of the National Science Olympiad (OSN). The ability of students to access and obtain information sources effectively can be a provision that must be mastered by students in selecting information sources. That way, students will get sources of information according to their needs in preparing for OSN. This research uses descriptive quantitative research methods and purposive sampling. The data collection method was taken from questionnaires and secondary data. This study analyzes the selection of printed and non-printed sources of information and the factors behind respondents choosing information sources. The results obtained from this research are that students tend to have multiple preferences, it can be proven by as many as 49% of respondents choosing to use both sources of information (printed and non-printed), this shows different results from previous research and uses as the theory of this research proposed by Jhon Steppen B. Arenas, et al. .al. (2018) The factors behind the choice of information sources available by students are due to the availability of collections in locations where students make choices, and also the convenience provided when selecting information sources, both printed and non-printed sources of information to prepare for the National Science Olympiad ( OSN). The selection of information sources is also inseparable from the important role of the library, as a location to find sources of information that can support OSN preparation.

Sustainable evaluation of energy storage technologies for wind power generation: A multistage decision support framework under multi-granular unbalanced hesitant fuzzy linguistic environment

Energy storage technology (EST) plays a foundational role for dealing with the intermittency of wind power and accelerating the structural revolution of renewable energy systems. Generally, EST selection is treated as a multiple-criteria group decision-making problem. However, stakeholders are not allowed to express multiple preferences via personalized linguistic distribution assessment and their risk appetites have received less attention in the existing approaches. This study aims at prioritizing ESTs by developing a novel multistage support framework where multi-granular unbalanced hesitant fuzzy linguistic term sets (UHFLTSs) are adopted to depict and quantify stakeholders’ opinions based on personalized semantics and granularities. A sustainable index system is devised in four dimensions (economic, technical, environmental and social) and the extended best-worst method (BWM) under multi-granular UHFLTSs environment is combined with maximum deviation method to determine the hybrid weights of criteria. A novel approach linking multi-granular UHFLTSs with double parameters TOPSIS method integrating risk appetite and optimism preference of stakeholders is further proposed by constructing optimization model, which can simultaneously yield the credible experts’ weights and prioritize the most desirable technology. The application of the proposed framework is demonstrated through an empirical case. Eventually, sensitivity analysis and comparative analysis are implemented to verify the effectiveness and validity of our proposal.

Implementasi Model Pembelajaran Cooperative Learning Tipe STAD untuk Meningkatkan Hasil Belajar Siswa dalam Pembelajaran PAI dan Budi Pekerti di SMP Namira Kota Probolinggo

Efforts to improve the Learning Outcomes of Class VIII Junior High School Students in Islamic Religious Education Subjects with the STAD Type Cooperative Learning Model at Namira Middle School, Probolinggo City, for the 2022/2023 Academic Year. This research is a classroom action research that aims to improve student learning outcomes of SMP Namira City Probolinggo class VIII-B with the STAD Type Cooperative Learning learning model. The subjects of this study were students of class VIII-B SMP Namira Probolinggo City as many as 20 students. The information collection tool used is in the form of observation sheets and ordinary multiple preference objective tests, based on the results of observations in cycle I and cycle II activities, there is an increase in student learning outcomes. Universally obtained 75% of students in the first cycle and 100% in the second cycle. This increase in learning outcomes took place because each student discussed the division of his group members into expert groups based on the teacher's direction, as well as in the group from which students were eager to present the results of the discussion. Next, it was found that the increase in learning outcomes that appeared in the gain index criteria for each student were 5 incomplete, 12 medium students and 3 high students above 90 in the first cycle, 20 students completed and most of them increased in the second cycle.

Multiplicative Consistency Ascertaining, Inconsistency Repairing, and Weights Derivation of Hesitant Multiplicative Preference Relations

This article investigates multiplicative consistency ascertaining, inconsistency repairing, and weights derivation for hesitant multiplicative preference relations (HMPRs). First, the completely multiplicative consistency and weakly multiplicative consistency of HMPRs are defined. Based on them, 0–1 mixed programming models and simple algebraic operations are proposed to ascertain the multiplicative consistency of HMPRs. Then, some goal programming models are developed to generate the weights from consistent HMPRs and to revise inconsistent HMPRs. An integrated procedure to manage the multiplicative consistencies of HMPRs is designed. The proposed methods are also extended to accommodate incomplete HMPRs, and to estimate missing values. Finally, some numerical examples, a comparative analysis with existent approaches, and a simulation analysis are included to illustrate the practicality and effectiveness of the developed models.

Product pricing and green decision-making considering consumers' multiple preferences under chain-to-chain competition

PurposeIt is necessary to implement green supply chains. But green development needs to be gradual and coexist with ordinary products in the market. This paper aims to study the green and ordinary product pricing and green decision-making under chain-to-chain competition.Design/methodology/approachThis paper considers consumers' multiple preferences and takes two competitive supply chains with asymmetric channels as the research object. Through the construction of the game models involving different competitive situations, this paper studies the pricing, green decision-making and the supply chains' profits, and discusses the impact of consumer green preference, channel preference, green investment and competition on the decision-making and performance. Finally, this paper further studies the impact of the decision structure on the environmental and economic benefits of supply chains.FindingsThe results show that consumer green preference has an incentive effect on the green supply chain and also provides an opportunity for the regular supply chain to increase revenue. Specifically, consumers' preference for green online channels improves the product greenness, but its impact on the green retailer and regular supply chain depends on the green investment cost. Moreover, competition not only fosters product sustainability, but also improves supply chain performance. This paper also points out that the decentralization of the regular supply chain is conducive to the environmental attributes of the green product, while the environment-friendly structure of the green supply chain is different under different conditions. In addition, the profit of a supply chain under centralized decision is not always higher than that under decentralized decision.Originality/valueThe novelty of this paper is that it investigates the pricing of two heterogeneous alternative products and green decision-making for the green product under the competition between two supply chains with asymmetric channels, in which the green supply chain adopts dual channels and the regular supply chain adopts a single retail channel.

An Interactive Consensus Model in Group Decision Making with Heterogeneous Hesitant Preference Relations

This paper proposes an interactive consensus reaching model in the group decision making for heterogeneous hesitant preference relations (i.e., hesitant fuzzy preference relations, hesitant multiplicative preference relations, hesitant fuzzy linguistic preference relations). First, the consistencies of three hesitant preference relations are defined, respectively. Then, based on their definitions, three optimization models are constructed to obtain the weight vector of alternatives, based on which an interactive consensus adjustment algorithm is established based on the direct consensus framework. This framework adopts feedback mechanism to facilitate the information correction of decision makers. After several rounds of adjustment, the decision results with satisfactory consensus level are achieved. Finally, the practicability and effectiveness of the model are illustrated through a case study of mine accident emergency decision making.

From international organizations to local governments: how foreign environmental aid reaches subnational beneficiaries in Argentina, Brazil, and Mexico

ABSTRACT The fight against climate change increasingly connects International Organizations (IOs), national governments, and subnational governments. How are international funds to fight climate change and environmental degradation distributed to subnational beneficiaries? We develop a novel multilevel theory that poses that tension between the preferences of the IO and national governments helps explain the subnational distribution of environmental aid – even more than pure environmental or social need. Simply put, whomever contributes more to IO-sponsored green projects determines who gets funds at the subnational level. While we understand that both actors have multiple preferences associated with green aid allocation, our theory of multilevel fund allocation expects the IO to prioritize provinces and states with low-development. Conversely, national governments will prioritize domestic electoral interests. We test this theory with a new data from the largest international donor of environmental aid, the Global Environment Facility. Empirically, we focus on Argentina, Brazil, and Mexico between 1997 and 2017.