Preference Aggregation Method Research Articles

Preference aggregation method in determining brightness threshold values for object recognition on optical images

Relevance. The need to automate the processing large volumes of geophysical information extracted from images obtained through aerial photography or space remote sensing. The main stage of image processing for object recognition is segmentation. It consists in dividing the image into two homogeneous, clearly distinguishable non-overlapping areas with known boundaries. Threshold segmentation methods are simple but effective and therefore popular. Aim. To describe the experimental studies results of application of the robust method of interval fusion with preference aggregation previously developed by the authors for selecting threshold values when segmenting objects in optical images. Objects. Coastline images taken by Earth remote sensing satellites. Methods. The analyzed image is divided into equal bands. Based on the brightness histogram of each band, its characteristic brightness interval is determined. For the obtained brightness intervals, the fusion result is calculated using the interval fusion with preference aggregation method. The latter is used as a threshold brightness value when forming a segmented image. Results. The results of experimental studies of 100 satellite images of the coastline showed that the proposed method provided correct separation of land and sea regions in 84 images. And the traditional methods of maximum entropy, arithmetic averaging and Otsu showed correct results only in 27, 76 and 73 cases, respectively. Evaluations of such the metrics as Precision, Recall and deviation of the calculated threshold from a known correct value showed the dominance of the proposed method over other tested ones in terms of segmentation quality.

Mining consensus sequence in multi-criteria group decision making with incomparability of alternatives and conflicts of experts

There exist latent conflicts in multi-criteria group decision making (MCGDM): 1) alternatives may have opposite performance under different criteria, thus showing incomparability; 2) the opinions of different experts may differ greatly, leading to incomparability of alternatives and a group decision at poor consensus. Non-reciprocal fuzzy preference relation (NRFPR) can represent the incomparability; however, the assessment, aggregation and prioritization of NRFPRs need to be improved. Additionally, for the consensus issue, existing methods yielded a maximum consensus sequence (MCS) without experts’ compromise attitudes. To fill these research gaps, this study applies NRFPRs in MCGDM to handle the incomparability of alternatives, and mine the MCS based on experts’ compromise attitudes and information exchange. First, a distance-based method is applied to enhance the indirect assessment of NRFPRs. A layered preference aggregation method is developed to aggregate NRFPRs and a straightforward conflict analysis method is proposed to perform prioritization. Afterwards, a compromise-or-not mechanism is introduced to derive experts’ compromise attitudes. Then, two kinds of conflicting items requiring additional information are identified. Discussions among experts are activated to exchange information so as to mine an MCS. We apply our method to the site selection of electric vehicle charging stations.

Research on the spatial optimal aggregation method of decision maker preference information based on Steiner-Weber point

The purpose of this paper is to provide a novel approach for the spatial aggregation of decision maker preference information. The optimal aggregation method of decision maker preference information based on spatial Steiner-Weber point can effectively aggregate the preference information of group members and achieve the optimization of group preference. The method comprises three key elements: the spatial mapping of the group preference, the spatial optimal aggregation model of the group preference, and the plant growth simulation algorithm (PGSA) is used to find the optimal aggregation points. Firstly, the group preference are mapped into a set of spatial multidimensional coordinates by using spatial mapping rules. Secondly, the spatial Steiner-Weber point is used as the prototype to construct the spatial aggregation model. Thirdly, the PGSA algorithm is used to find the spatial aggregation points, whose spatial weighted Euclidean distance to all the decision makers’ preference points is minimal. The optimal aggregation matrix is composed of these optimal aggregation points, which can accurately reflect the decision makers’ comprehensive opinions. Finally, the effectiveness and rationality of this method are verified by comparing with the classical group preference aggregation methods.

Visualization of preference aggregation based on Weber point in social network group decision-making problem

PurposeThis study aims to construct a Weber point-based model to complete the visualization of preference aggregation in group decision-making problem, in which decision-makers are associated with trust relationship.Design/methodology/approachThis study mainly comprises four parts: trust propagation, preference aggregation, opinion adjustment and alternative selection. Firstly, the incomplete trust between decision-makers is completed with trust transfer operators and propagation probability in trust propagation process. Secondly, a preference aggregation model based on Weber point is proposed to aggregate the group preference visually. Thirdly, opinions are adjusted to reach a consensus. Finally, the ranking of alternatives is determined by the correlation coefficient with the group preference as a reference.FindingsThe Weber point-based model proposed in this study can minimize the gap in the preference of alternatives between the group and all decision-makers, and realize the visualization of aggregation result. A case of plan selection is introduced to illustrate the feasibility and effectiveness of the proposed model.Originality/valueBy comparing the result with the weighted average-based preference aggregation method, the Weber point-based model proposed in this study can show the result of preference aggregation intuitively and improve group consensus.

Interval Data Fusion with Preference Aggregation for Balancing Measurement Accuracy and Energy Consumption in WSN

An effective way to conserve energy in wireless sensor networks is reducing the amount of data transmissions. However, this can affect the accuracy and reliability of the sensed data considerably. To provide energy-accuracy trade-off, data fusion technique can be applied exploiting temporal and spatial correlation of sensed data. In this paper, we propose a novel approach for balancing energy consumption and measurement accuracy in wireless sensor networks. The approach is a combination of accuracy enhancement algorithm SensAcc and active node selection algorithm ActiveNode, which are based on the robust interval fusion with preference aggregation (IF&PA) method. The approach is aimed at selecting minimum number of nodes that can provide data of sufficient volume and quality to maintain required accuracy. The performance of the proposed algorithms has been evaluated by both simulation and real data processing. Simulation results show that the proposed approach significantly enhances the network lifetime while providing highly accurate measurement outcomes. Results of real data processing demonstrate noticeable decrease of measurement uncertainty even for small number of sensor nodes.

Thermal preference-based control studies: review and detailed classification

In the last few years, a significant amount of research has focused on learning and modeling personalized thermal preferences to satisfy individuals in buildings. However, there is a disconnection between personal comfort studies and building thermal control research. The objective of this article is to review research studies that integrate personalized thermal preferences into building control systems, and their control performance (in terms of comfort and energy) through either simulation studies or field implementations. Existing studies can be categorized into two main categories, depending on the type of control integration: conventional, reactive (feedback) control and advanced model-based predictive control. A deeper analysis of the degree of integration, number of occupants considered, control setpoint type, and thermal preference aggregation method, leads to a more detailed classification of thermal preference-based control research. In the absence of a general consensus of robust implementation of thermal preference-based control in buildings, this categorization allows a better understanding of current focus, research gaps, and needs for future work.

Justified Representation for Perpetual Voting

We study the social choice setting of perpetual voting where, based on voter preferences, decisions have to be taken over a finite horizon of consecutive points in time (e.g, days). We consider two complementary settings: a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Static</i> setting, in which voter preferences remain static over time, and a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dynamic</i> setting, in which voter preferences may change over time. We adapt the well-established Justified Representation and Proportional Justified Representation axioms, commonly used in the social choice literature, to perpetual voting, resulting in two axiomatic variants for the static setting and four variants for the dynamic setting. We show that all of the axioms are always satisfiable, and that simple preference aggregation methods can be used to satisfy the axioms in both cases. We then conduct a large human study ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N=190$ </tex-math></inline-formula> ) aimed at identifying what potential voters (i.e., ordinary people) deem as desirable outcomes in simple perpetual voting settings. Our results show that approximately half of our participants consider different interpretations of fairness that correspond to our axiomatic framework. Taken jointly, our results can be used to help the research community identify appropriate aggregation methods to use in practice.

АГРЕГИРОВАНИЕ ПРЕДПОЧТЕНИЙ ПРИ ВЫБОРЕ ЦВЕТОВОЙ МОДЕЛИ ДЛЯ ЦИФРОВОГО ЦВЕТОМЕТРИЧЕСКОГО АНАЛИЗА СОСТАВА ВЕЩЕСТВ

Актуальность исследования обусловлена тем, что при количественном определении состава веществ все большую популярность приобретает цифровой цветометрический анализ. Это оптический аналитический метод, в котором первичным измерительным преобразователем является оптод, полученный из реагентов, иммобилизованных в твердотельную матрицу. При взаимодействии с определяемым компонентом в пробе оптод изменяет цвет; интенсивность окраски оптода пропорциональна количеству определяемого компонента. Полученный таким образом аналитический сигнал необходимо оцифровать и представить в одном из существующих цветовых пространств для формирования значения измеряемой величины. В научно-технической литературе не существует обоснованных рекомендаций, которые позволяли бы на систематической основе выбирать наиболее подходящее для конкретных требований цветовое пространство (модель). Цель: способствовать решению проблемы выбора такой цветовой модели для организации цифрового цветометрического анализа, которая для заданного набора аналитов обеспечивает наилучшие значения характеристик построенных для них градуировочных зависимостей аналитических сигналов. Объекты: используемые при цифровом описании цвета стандартные цветовые модели RGB, HSL, XYZ и L*a*b*. В качестве определяемых веществ были использованы тяжелые металлы: серебро, медь, железо, кобальт, хром, никель и сумма тяжелых металлов. Методы. Предложен и экспериментально обоснован с помощью агрегирования предпочтений метод выбора стандартной системы (модели) представления цвета, обеспечивающей проведение цифрового цветометрического анализа с наилучшими возможными значениями набора характеристик градуировочных зависимостей получаемых аналитических сигналов. Результаты. Метод позволяет выбирать стандартную цветовую модель, наилучшим образом подходящую для проведения цифрового цветометрического анализа, что, в свою очередь, способствует повышению достоверности цифрового цветометрического анализа.

Low-Distortion Social Welfare Functions

Work on implicit utilitarian voting advocates the design of preference aggregation methods that maximize utilitarian social welfare with respect to latent utility functions, based only on observed rankings of the alternatives. This approach has been successfully deployed in order to help people choose a single alternative or a subset of alternatives, but it has previously been unclear how to apply the same approach to the design of social welfare functions, where the desired output is a ranking. We propose to address this problem by assuming that voters’ utilities for rankings are induced by unknown weights and unknown utility functions, which, moreover, have a combinatorial (subadditive) structure. Despite the extreme lack of information about voters’ preferences, we show that it is possible to choose rankings such that the worst-case gap between their social welfare and that of the optimal ranking, called distortion, is no larger (up to polylogarithmic factors) than the distortion associated with much simpler problems. Through experiments, we identify practical methods that achieve nearoptimal social welfare on average.

Alternative Method for Processing of International Comparison Results

The CIPM Mutual Recognition Arrangement of National Standards, Calibration and Measurement Certificates issued to National Metrology Institutes (NMIs) defines the status of key comparisons as a priority tool for implementation of quality management systems in NMIs and the determination of the competence of a particular NMI based on its calibration and measurement capabilities in accordance with the unified database of key comparisons — KCDB.The general approach for evaluating the key comparisons results is presented in the International Committee for Weights and Measures procedures, as well as in the various articles. The question of the development and practical application of alternative methods for processing data of international comparisons is of the particular interest. It will allow to expand the methodological basis for confirming the calibration and measurement capabilities provided by the NMIs and published in KCDB.The paper considers the alternative approach for processing the international comparisons results, which based on preference aggregation method. It allows to determine reference value for evaluation the comparison data on the basis of ranking the obtained uncertainty intervals presented by National Metrology Institutes. Processing of COOMET.EM-K6.a Key Comparison data by the preference aggregation method is presented. The results are compared with the general approach and the considered method.

Data analysis of energy surveys by preference aggregation method

Systematic conducting energy surveys of power networks to identify a possible energy loss is the basis for energy saving and energy efficiency. In the paper, a preference aggregation based method is proposed to arrange the big data of instrumental investigations of auxiliary power consumption by substations of electric power distribution networks. This approach provides a compact integral ordinal scale estimate of substations’ energy consumption for own needs, which can provide a rapid decision making and visualization. Application of the proposed method for processing energy survey data of real backbone electric grids of Russia is discussed.

Robust design of flash evaporators for must concentration applications in wine production

In recent years, flash evaporation processes have received an increased attention in the wine industry for must concentration applications. Specific constraints related to the wine industry area had lead to many improvements of flash evaporators initially designed for seawater desalination. In particular the quality of the vintage, the transportability of the system and the environmental impact are of main interest. Moreover, the preliminary design of such systems must also consider robustness criteria. Obviously, variations of temperatures and flow rates of liquids at the inlet of the process can dramatically impact the quality of the product at the system outlet. In particular, deviations from the target values of temperature and alcoholic volume fraction can lead to a severe degradation of the vintage. As it is common in product design to have multiple performance measures, the robust design problem is addressed using a multi-objective approach. A trade-offs is introduced between two main design objectives: (i) the improvement in overall performance (product quality, transportability, environmental impact and costs) and (ii) the lowering of the sensitivity of the product quality under uncertainty. These main objectives are related to several elementary objectives corresponding to design criteria and a preference aggregation method is used to formulate the two different design objectives. Objectives are linked to weighting parameters values equivalent to priority levels. The selection of the most preferred design solution is discussed according to different trade-off strategies. The generation of the Pareto set is addressed by the non dominated sorting genetic algorithm NGSAII. From computing results, our recommendations concern the compromise between performance and robustness of flash evaporators. In this context, the final alcoholic volume fraction of the wine is the most sensitive parameter, which justify to maintain a high value of evaporative capacity when designing this type of system.

Numerical experimental investigation of comparison data evaluation method using preference aggregation

&lt;p class="Abstract"&gt;An integrated software for experimental testing preference aggregation method for interlaboratory comparison data processing is presented. The data can be obtained by a Monte-Carlo simulation and/or taken from real comparisons. Numerical experimental investigations with the software have shown that, as against traditional techniques of interlaboratory comparison data processing, the preference aggregation method provides a robust comparison reference value to be closer to a nominal value.&lt;/p&gt;

Integration of Data Envelopment Analysis-Based Preference Aggregation Method and α Particle Swarm Optimization Technique into Group Decision Model

AbstractThe design of sustainable infrastructure is a complex process because it involves various fields of expertise such as engineering, economy, environment, social, etc. To make sure experts from various fields are part of decision-making, a heterogeneous group is typically formed and the individual decisions of the group are combined simply by using an arithmetic mean, geometric mean, or by altering individual decisions to meet group consensus to generate a group decision. The drawbacks with these methods are inconsistency, dissatisfaction, and delayed decision process. This study proposes to use the Data Envelopment Analysis (DEA)-based preference aggregation method (commonly used by the business community) to combine the individual inputs into a group decision in conjunction with α constrained particle swarm optimization technique to generate consistent results in minimal time without altering individual decision-maker inputs. The proposed approach was evaluated by computing group decision weights ...

Bribery in Voting With Soft Constraints

We consider a multi-agent scenario where a collection of agents needs to select a common decision from a large set of decisions over which they express their preferences. This decision set has a combinatorial structure, that is, each decision is an element of the Cartesian product of the domains of some variables. Agents express their preferences over the decisions via soft constraints. We consider both sequential preference aggregation methods (they aggregate the preferences over one variable at a time) and one-step methods and we study the computational complexity of influencing them through bribery. We prove that bribery is NPcomplete for the sequential aggregation methods (based on Plurality, Approval, and Borda) for most of the cost schemes we defined, while it is polynomial for one-step Plurality.

Empirical properties of group preference aggregation methods employed in AHP: Theory and evidence

We study various methods of aggregating individual judgments and individual priorities in group decision making with the AHP. The focus is on the empirical properties of the various methods, mainly on the extent to which the various aggregation methods represent an accurate approximation of the priority vector of interest. We identify five main classes of aggregation procedures which provide identical or very similar empirical expressions for the vectors of interest. We also propose a method to decompose in the AHP response matrix distortions due to random errors and perturbations caused by cognitive biases predicted by the mathematical psychology literature. We test the decomposition with experimental data and find that perturbations in group decision making caused by cognitive distortions are more important than those caused by random errors. We propose methods to correct the systematic distortions.

Empirical Properties of Group Preference Aggregation Methods Employed in AHP: Theory and Evidence

We study various methods of aggregating individual judgments and individual priorities in group decision making with the AHP. The focus is on the empirical properties of the various methods, mainly on the extent to which the various aggregation methods represent an accurate approximation of the priority vector of interest. We identify five main classes of aggregation procedures which provide identical or very similar empirical expressions for the vectors of interest. We also propose a method to decompose in the AHP response matrix distortions due to random errors and perturbations caused by cognitive bias predicted by the mathematical psychology literature. We test the decomposition with experimental data and find that perturbations in group decision making caused by cognitive distortions are more important than those caused by random errors. We propose methods to correct systematic distortions.

Cross-ranking of Decision Making Units in Data Envelopment Analysis

Data Envelopment Analysis (DEA) is a mathematical model that evaluates the relative efficiency of Decision Making Units (DMUs) with multiple input and output. In some applications of DEA, ranking of the DMUs are important. For this purpose, a number of approaches have been introduced. Among them is the cross-efficiency method. The method utilizes the result of the cross-efficiency matrix and averages the cross-efficiency scores of each DMU. Ranking is then performed based on the average efficiency scores. In this paper, we proposed a new way of handling the information from the cross-efficiency matrix. Based on the notion that the ranking order is more important than individual efficiency score, the cross-efficiency matrix is converted to a cross-ranking matrix. A cross-ranking matrix is basically a cross-efficiency matrix with the efficiency score of each element being replaced with the ranking order of that efficiency score with respect to the other efficiency scores in a column. By so doing, each DMU assume the role of a decision maker and how they voted or ranked the other DMUs are reflected in their respective column of the cross-ranking matrix. These votes are then aggregated using a preference aggregation method to determine the overall ranking of the DMUs. Comparison with an existing cross-efficiency method indicates a relatively better result through usage of the proposed method.

A Methodology for Trading-Off Performance and Robustness Under Uncertainty

AbstractMathematical optimization plays an important role in engineering design, leading to greatly improved performance. Deterministic optimization, however, may result in undesired choices because it neglects uncertainty. Reliability-based design optimization (RBDO) and robust design can improve optimization by considering uncertainty. This paper proposes an efficient design optimization method under uncertainty, which simultaneously considers reliability and robustness. A mean performance is traded-off against robustness for a given reliability level of all performance targets. This results in a probabilistic multiobjective optimization problem. Variation is expressed in terms of a percentile difference, which is efficiently computed using the advanced mean value method. A preference aggregation method converts the multiobjective problem to a single-objective problem, which is then solved using an RBDO approach. Indifference points are used to select the best solution without calculating the entire Pareto frontier. Examples illustrate the concepts and demonstrate their applicability.

Effective Product Family Design Using Preference Aggregation

The development of product families, groups of products that share a common platform, is one way to provide product variety while keeping design and production costs low. The design of a product platform can be formulated as a multicriteria optimization problem in which the performances of individual products trade off against each other and against the objective of platform standardization. The problem is often solved in two stages: one to determine the values of the shared platform variables and a second to optimize the product family members with respect to specific targets. In the first stage, it is common to target the mean and variability of performance when fixing the values of platform variables. This paper contributes three new methods for platform development. The new methods are demonstrated on an electric motor example from the platform design literature, and the results are compared to those from existing methods. First, a preference aggregation method is applied to aggregate the multiple objectives into a single overall objective function. On the example problem, this approach gives superior results to existing techniques. Second, an alternative method that targets the minimum and maximum of the range of performance across the platform, instead of the mean and standard deviation, is proposed and shown to succeed where the existing method may fail. Third, a single-stage optimization approach which solves for both platform and nonplatform variables in a single pass is presented. This method delivers notably superior performance on the example problem but will, in general, incur greater computational expense.