Preference Aggregation Research Articles

Median voter dynamics in a laboratory experiment on voting over redistribution

This paper reports results from a laboratory experiment that investigates the prevalence of median voter dynamics in the Meltzer-Richard redistribution mechanism. I focus on the model's microfoundations and analyze how individuals translate material incentives into proposed tax rates and how these individual proposals get aggregated into a collective group choice under two different voting rules; majority rule and voting by veto. My experimental results show that material incentives do not fully determine individual proposals. In addition, personal characteristics and justice attitudes constitute additional facets of individual motivations. Median voter dynamics are prevalent under both voting rules at least when looking at aggregate behavior. Both decision rules thus lead to an un-biased aggregation of voters' preferences. Moreover, the experimental results show only minor behavioral differences between decisions employing majority rule and collective choices using voting by veto.

Joining a migrant caravan: herd behaviour and structural factors

The largest migrant caravan headed from the Northern Triangle of Central America to the southern border of the United States left Honduras on 13 October 2018. When it crossed the border into Mexico, many people wondered why more than 7000 Central Americans were moving to a country with such adverse immigration policies. This research answers the question by interviewing migrants from the Caravan during their travel through Mexico, and combining previously documented factors, such as the search for jobs, better wages, preserving their lives from threats from criminal groups, migration policies and economic interests, with novel explanations derived from herd behaviour in the Caravan. This theoretical addition of herd behaviour allows us to better understand the aggregation of individual preferences that have economic incentives and contributes to the academic debate on the factors that drive collective migrations.

SAPEVO-H² a Multi-Criteria Systematic Based on a Hierarchical Structure: Decision-Making Analysis for Assessing Anti-RPAS Strategies in Sensing Environments

Regarding high-level and complex decision-making scenarios, the study presents an extensive approach to the Simple Aggregation of Preferences Expressed by Ordinal Vectors-Multi Decision Making method (SAPEVO-M). In this context, the modeling proposal, named SAPEVO-Hybrid and Hierarchical (SAPEVO-H²), the objective of this study, based on the concepts of multi-criteria analysis, provides the evaluation of alternatives under the light of multiple criteria and perceptions, enabling the integration of the objectives of a problem, which are transcribed into attributes and structured in a hierarchical model, analyzing qualitative and quantitative data through ordinal and cardinal entries, respectively. As a case study, a decision analysis concerning the defense strategies against anti-Remotely Piloted Aircraft Systems (RPAS) strategies for the Brazilian Navy is carried out. Using the technique of the causal maps approach based on Strategic Options Development and Analysis (SODA) methodology, the problematic situation is structured for numerical implementation, demonstrating the performance of objectives and elements of a hierarchical structure. As a result, rankings concerning objectives and anti-RPAS technologies, based on the treatment of subjective information, are presented. In the end, the main contribution of the study and its limitations are discussed, along with the conclusions and some proposals for future studies.

Integrating individual preferences into collective argumentation

AbstractIn the field of collective argumentation, multiple agents may have different knowledge representations and individual preferences. In order to obtain a reasonable collective outcome for the group, either individual frameworks should be merged or individual preferences should be aggregated. However, framework merging and preference aggregation are different procedures, leading to disagreements on collective outcomes. In this paper, we figure out a solution to combine framework merging, argumentative reasoning and incomplete preference aggregation together. Furthermore, a couple of rational postulates are proposed to be the criteria for the rationality of collective outcomes obtained based on our approach.

Expertise-Structure and Risk-Appetite-Integrated Two-Tiered Collective Opinion Generation Framework for Large-Scale Group Decision Making

The generation of collective preference assessments occupies a critical position in deriving accurate and reliable alternative rankings in the context of large-scale group decision making (LSGDM). In general, the collective opinion generation framework entails the following three phases, which are clustering analysis, weighting clusters, and preference aggregation. However, the clustering of experts has been frequently based on preference similarities among them without taking into account individual opinions in which knowledge elicitation plays a crucial role. The traditional collective opinion generation framework suffering from this drawback may result in unreliable decision outcomes. To this end, we propose an expertise-structure and risk-appetite-integrated two-tiered collective opinion generation framework to address this concern. The first tier of the two-tiered collective opinion generation framework divides the entire expert group into several subgroups based on individual expertise structures, which are extracted from hesitant fuzzy linguistic term set (HFLTS)-based preference assessments, and it then weighs the resulting clusters in accordance with the overall expertise levels. The second-tier clusters the first-tier subgroups conditioned on the indicator of individual assessment similarities and gathers the generated subgroup preference constructs in the use of the risk appetite-oriented power average operator. In addition, the notion of proportional HFLTSs was introduced to manifest collective evaluations in second-tier subgroups to eliminate information loss and distortion. The effectiveness and flexibility of the proposed collective opinion generation algorithm are eventually illustrated by a case study and a comparison analysis.

Trimming extreme reports in preference aggregation

Eliminating – or trimming – extreme reports before aggregating them is usually motivated by the perception that it constitutes a remedy for strategic misreporting. This work focuses on the strategic calculus of voting when using trimmed-mean mechanisms for preference aggregation. Contrary to the above presumption, we show, both formally and experimentally that, under such mechanisms, voters persistently resort to strategic polarization for all but the most extreme levels of trimming. Furthermore, we show that while trimming promotes efficiency (i.e. the average payoff increases), it also leads to more inequality (i.e. the minimum payoff decreases). While experimental results provide some support for the idea that trimming can mitigate strategic misreporting, the extent of this non-equilibrium behavior is not enough to neutralize trimming's predicted effect on efficiency and inequality.

A Clustering Multi-Criteria Decision-Making Method for Large-Scale Discrete and Continuous Uncertain Evaluation.

In recent years, Dempster-Shafer (D-S) theory has been widely used in multi-criteria decision-making (MCDM) problems due to its excellent performance in dealing with discrete ambiguous decision alternative (DA) evaluations. In the general framework of D-S-theory-based MCDM problems, the preference of the DAs for each criterion is regarded as a mass function over the set of DAs based on subjective evaluations. Moreover, the multi-criteria preference aggregation is based on Dempster's combination rule. Unfortunately, this an idea faces two difficulties in real-world applications: (i) D-S theory can only deal with discrete uncertain evaluations, but is powerless in the face of continuous uncertain evaluations. (ii) The generation of the mass function for each criterion relies on the empirical judgments of experts, making it time-consuming and laborious in terms of the MCDM problem for large-scale DAs. To the best of our knowledge, these two difficulties cannot be addressed with existing D-S-theory-based MCDM methods. To this end, this paper proposes a clustering MCDM method combining D-S theory with the analytic hierarchy process (AHP) and the Silhouette coefficient. By employing the probability distribution and the D-S theory to represent discrete and continuous ambiguous evaluations, respectively, determining the focal element set for the mass function of each criterion through the clustering method, assigning the mass values of each criterion through the AHP method, and aggregating preferences according to Dempster's combination rule, we show that our method can indeed address these two difficulties in MCDM problems. Finally, an example is given and comparative analyses with related methods are conducted to illustrate our method's rationality, effectiveness, and efficiency.

External validity of multi-criteria preference data obtained from non-random sampling: measuring cohesiveness within and between groups

An important component of multi-criteria decision analysis (MCDA) in the public sector is the elicitation and aggregation of preference data collected via surveys into the relative importance of the criteria for the decision at hand. These aggregated preference data, usually in the form of mean weights on the criteria, are intended to represent the preferences of the relevant population overall. However, random sampling is often not feasible for public-sector MCDA for logistical reasons, including the expense involved in identifying and recruiting participants. Instead, non-random sampling methods such as convenience, purposive or snowball sampling are widely used. Nonetheless, provided the preference data collected are sufficiently ‘cohesive’ in terms of the extent to which the weights of the individuals belonging to the various exogenously defined groups in the sample are similar, non-random sampling can still produce externally valid aggregate preference data. We explain a method for measuring cohesiveness using the Kemeny and Hellinger distance measures, which involve measuring the ‘distance’ of participants’ weights (and the corresponding rankings of the criteria) from each other, within and between the groups respectively. As an illustration, these distance measures are applied to data from a MCDA to rank non-communicable diseases according to their overall burden to society. We conclude that the method is useful for evaluating the external validity of preference data obtained from non-random sampling.

Deep neural network-based multi-stakeholder recommendation system exploiting multi-criteria ratings for preference learning

A commercially viable multi-stakeholder recommendation system maximizes the utility gain by learning the personalized preferences of multiple stakeholders, such as consumers and producers. Existing multi-stakeholder studies rely on a consumer-item interaction matrix to evaluate the producers' preferences and utility gain. However, these methods result in a negligible boost in producers' utility, as consumer-item interaction provides only a limited insight into producers' preferences. Instead, an independent producer-item interaction matrix may better represent the needs and interests of producers. The deep neural networks have recently achieved encouraging results in a recommendation by estimating user preferences and learning user-item non-linear features. The multi-stakeholder recommendation system may employ this strength of the deep neural network to combine consumer-producer preferences and generate the optimal estimate of their common interest. Hence this study proposes a deep neural network-based multi-stakeholder recommendation system model for aggregating consumer and producer preferences. Next, a multi-criteria rating-based interaction matrix is proposed to learn the producers' preference over an item. Further, we perform deep neural network-based model training to generate the cumulative preference matrix by learning and aggregating the preferences of consumer and producer stakeholders. This work performs extensive experiments over Movie Lens-100 K and 1 M datasets with numerous activation functions, hidden layer configuration, and optimizers. The prediction accuracy, ranking, and utility gain-based evaluation results validate the success of the proposed model in developing a multi-criteria matrix for producers' and deep neural network-based multi-stakeholder preference aggregation over the baseline models.

Preference Compromise and Parent Satisfaction With Schools in Choice Markets: Evidence From Kansas City, Missouri

Parents desire several features when choosing a school, but they often compromise on some preferences in favor of others. We develop a novel measure of aggregate preference compromise by examining the discrepancy between ideal preferences and those specified under real-world constraints. Relying on data from a representative sample in Kansas City, MO, we find that low-income and less educated parents and those from historically marginalized racial/ethnic backgrounds make a greater degree of preference compromise than other parents do. This is associated with lower satisfaction with chosen schools, suggesting that compromises matter. Less privileged families are also geographically more restricted in making school choices and have more limited access to better performing schools, which can aggravate preference compromises. Implications are discussed.

Satisfying complex top- k fairness constraints by preference substitutions

Given m users (voters), where each user casts her preference for a single item (candidate) over n items (candidates) as a ballot, the preference aggregation problem returns k items (candidates) that have the k highest number of preferences (votes). Our work studies this problem considering complex fairness constraints that have to be satisfied via proportionate representations of different values of the group protected attribute(s) in the top- k results. Precisely, we study the margin finding problem under single ballot substitutions , where a single substitution amounts to removing a vote from candidate i and assigning it to candidate j and the goal is to minimize the number of single ballot substitutions needed to guarantee that the top-k results satisfy the fairness constraints. We study several variants of this problem considering how top- k fairness constraints are defined, (i) MFBinaryS and MFMultiS are defined when the fairness (proportionate representation) is defined over a single, binary or multivalued, protected attribute, respectively; (ii) MF-Multi2 is studied when top- k fairness is defined over two different protected attributes; (iii) MFMulti3+ investigates the margin finding problem, considering 3 or more protected attributes. We study these problems theoretically, and present a suite of algorithms with provable guarantees. We conduct rigorous large scale experiments involving multiple real world datasets by appropriately adapting multiple state-of-the-art solutions to demonstrate the effectiveness and scalability of our proposed methods.

A comprehensive framework for preference aggregation Parrondo's paradox.

Individuals can make choices for themselves that are beneficial or detrimental to the entire group. Consider two losing choices that some individuals have to make on behalf of the group. Is it possible that the losing choices combine to give a winning outcome? We show that it is possible through a variant of Parrondo's paradox-the preference aggregation Parrondo's paradox (PAPP). This new variant of Parrondo's paradox makes use of an aggregate rule that combines with a decision-making heuristic that can be applied to individuals or parts of the social group. The aim of this work is to discuss this PAPP framework and exemplify it on a social network. This work enhances existing research by constructing a feedback loop that allows individuals in the social network to adapt its behavior according to the outcome of the Parrondo's games played.

Energy storage strategy analysis based on the Choquet multi-criteria preference aggregation model: The Portuguese case

With the increase in renewable energy generation and its problems related to output instability, storage systems must be implemented in parallel to account for this effect. Therefore, it is valuable to deepen the study of these technologies’ performances in their several application tiers, thus understanding the potential of each alternative, both per tier and as a whole. For this reason, a collaborative multi-criteria decision-aiding framework is proposed to rank the various available options in several layers of the energy storage market, constructed alongside experts and policy-makers from each tier that serve as actors of the decision-making process and using Portugal as a case study. Based on the Choquet multi-criteria preference aggregation model, to the best of the authors’ knowledge, this framework is an unprecedented application in the energy sector. Beyond a critical review of the results, a scenario analysis was performed to explore interesting future possibilities that may aid governments to make decisions in the search for an energy sustainable development. Chemical storage solutions, such as Hydrogen and Methane, as well as several electrochemical batteries, especially Lithium- and Nickel-based ones, were the standout energy storage solutions. Chemical storage was shown to have the desired characteristics for the Long-term grid tier. Meanwhile, batteries, including Redox Flow in the first case, have overperformed in the Microgrid and Mobility tiers. No standout solutions appeared in the Short-term grid tier. Unsurprisingly, the aforementioned chemical storage systems, batteries, and Hot Water have presented themselves as the most politically interesting technologies, due to their multipurpose uses and intrinsic characteristics.

Analysis of the innovation capacity of Mexican regions with the multiple criteria hierarchy process

This study attempts to use multicriteria decision aiding (MCDA) tools to analyse the innovation capacity of 32 regions in Mexico. In today’s competitive world, innovation in science and technology is the key to the growth and productivity of the regions. Understanding the current state of innovation capacity and identifying the factors that influence said capacity allows the government to make region-specific policies for future growth and development. However, measuring such a complex concept involves a large number of criteria, and to understand the impact of a region’s innovation capacity under a subset of criteria or with respect to high-level views it is necessary to gain in-depth insight for future policy design. To address this issue, we adopt the multicriteria hierarchy process (MCHP), which allows the decision-maker to express preferences of sub- group criteria and individual analysis by using subsets of criteria and different dimensions of the problem. Further, with the aim of managing the weighting of criteria and preference aggregation within the MCHP framework, we employ the hierarchical version of the deck of cards method for weight definition, the hierarchical ELECTRE III to aggregate preferences, and the distillation procedure to exploit the preference model. Using this methodological framework, the innovation capacity of 32 regions in Mexico is analysed under 52 decision criteria.

Prediction Model of Music Popular Trend Based on NNS and DM Technology

The popular trend of today’s music can be obtained by deep excavation, analysis, and prediction of the audience’s preferences. Using huge music library resources and user behavior to form music big data and truly realizing the aggregation of audience preferences determine the popular development trend of music. Therefore, this paper will apply data mining (DM) technology, introduce neural network (NNS) theory, establish a prediction model of music fashion trend, predict and evaluate the music fashion trend according to the selected evaluation index, find the change of music fashion trend in time, and provide decision-making basis for music fashion trend. In this paper, the prediction of music popularity trend based on NNS and DM technology is studied. In the prediction of the number of songs played by 10 artists, the NNS algorithm proposed in this paper reduces the prediction effect from the original 0.074 and 0.045 to 0.044 and 0.032, respectively, and the error rates are reduced by 35.7% and 29.4%, respectively, compared with the learning algorithm and the decision tree algorithm. Among the three methods, the NNS algorithm in this paper has the highest accuracy. Therefore, it can be proved that the model proposed in this paper is more suitable for predicting the trend of music popularity. In the end, it can accurately control the trend of pop music and also realize the aggregation of user preferences to determine the trend of pop music.

What Do We Learn about Voter Preferences from Conjoint Experiments?

AbstractPolitical scientists frequently interpret the results of conjoint experiments as reflective of majority preferences. In this article, we show that the target estimand of conjoint experiments, the average marginal component effect (AMCE), is not well defined in these terms. Even with individually rational experimental subjects, the AMCE can indicate the opposite of the true preference of the majority. To show this, we characterize the preference aggregation rule implied by the AMCE and demonstrate its several undesirable properties. With this result, we provide a method for placing bounds on the proportion of experimental subjects who prefer a given candidate feature. We describe conditions under which the AMCE corresponds in sign with the majority preference. Finally, we offer a structural interpretation of the AMCE and highlight that the problem we describe persists even when a model of voting is imposed.

Reproducing size distributions of swarms of barchan dunes on Mars and Earth using a mean-field model

We apply a mean-field model of interactions between migrating barchan dunes, the CAFE model, which includes spontaneous calving, aggregation, fragmentation, and mass-exchange, yielding a steady-state size distribution that can be resolved for different choices of interaction parameters. The CAFE model is applied to empirically measured distributions of dune sizes in two barchan swarms in the north circumpolar region of Mars, three swarms in Morocco, and one in Mauritania, each containing more than 1000 bedforms. When the sizes of bedforms are rescaled by the mean size in each zone two attractor states appear, with the Tarfaya zones all displaying a common distribution and the Martian and Mauritanian zones sharing a different distribution. Comparison of these attractor states with the outputs of the CAFE model reveals that the Tarfaya-type distribution results from a preference for aggregation and fragmentation interactions whereas the Mars–Mauritania distribution is more likely a result of exchange–dominated interactions. We observe that there appears to be a greater number of collisions happening in Tarfaya than in the other areas which is consistent with a greater rate of aggregation–fragmentation processes as suggested by our model. Our comparison with the CAFE model also predicts a universal rule for the outputs of the different types of interactions with exchange interactions favouring the production of two dunes roughly equal in size. Fragmentation interactions often result in three bedforms with volumes approximately in the ratio 1:1:2. Finally, we find that spontaneous calving of dunes does not play an important role in shaping the size distributions in barchan swarms.

Comprehensive Rules-Based and Preferences Induced Weights Allocation in Group Decision-Making with BUI

Decision-makers’ subjective preferences can be well modeled using preference aggregation operators and related induced weights allocation mechanisms. However, when several different types of preferences occur in some decision environment with more complex uncertainties, repeated uses of preferences induced weights allocation sometimes become unsuitable or less reasonable. In this work, we discuss a common decision environment where several invited experts will offer their respective evaluation values for a certain object. There are three types of preferences which will significantly affect the weights allocations from experts. Instead of unsuitably performing preference induced weights allocation three times independently and then merging the results together using convex combination as some literatures recently did, in this work, we propose some organic and comprehensive rules-based screen method to first rule out some unqualified experts and then take preference induced weights allocation for the refined group of experts. A numerical example in business management and decision-making is presented to show the cognitive reasonability and practical feasibility.

Distance-Based Decision Making, Consensus Building, and Preference Aggregation Systems: A Note on the Scale Constraints

Distance metrics and their extensions are widely accepted tools in supporting distance-based decision making, consensus building, and preference aggregation systems. For several models of this nature, it may be necessary to elucidate the problem output in the original input domain. When a particular parameter of interest is desired to be produced in this original domain, i.e., the scale, the decision makers simply resort to constraints that function in parallel with this goal. However, there exist some cases where such a membership is guaranteed by the mathematical properties of the distance metric utilized. In this paper, we argue that the scale constraints utilized in this manner under the distance-metric optimization framework are, in some cases, completely redundant. We provide necessary mathematical proofs and illustrate our arguments through an abstract physical system, examples, a case study, and a brief computational experiment.

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.