Aggregation Of Opinions Research Articles

Aggregation of opinions and risk measures

A ranking ≿ over a set of alternatives is an aggregation of experts' opinions (AEO) if it depends on the experts' assessments only. We study both those rankings that result from pooling Bayesian experts and those that result from pooling possibly non-Bayesian experts. In the non-Bayesian case, we allow for the simultaneous presence of experts that may display very different attitudes toward uncertainty. We show that a unique axiom along with a mild regularity condition fully characterize those AEO rankings which are “generalized averages” of experts' opinions, in the sense that the average is obtained by using a capacity rather than a probability measure. We call these rankings non-linear pools. We consider a number of special cases such as linear pools (Stone, 1961), concave/convex pools (Crès et al., 2011), quantiles and pools of equally reliable experts. We then apply our results to the theory of risk measures. Our application can be viewed as a generalization of the robust approach (Glasserman and Xu, 2014) to risk measurement in that it allows both for a more general notion of “model” and more general aggregation rules. We show that a wide class of risk measures can be regarded as non-linear pools. Not only does this class include all coherent risk measures (Artzner et al., 1999), but also measures like the Value-at-Risk, which fail subadditivity. We also briefly discuss the possibility of extending our findings to include the convex risk measures of Föllmer and Schied (2002) as well as their non-subadditive extensions.

Aggregation of opinions in networks of individuals and collectives

We study the formation of opinions in a bipartite network of firms' boards and directors theoretically. A director and a board are connected provided the director is a board member. Opinions are sets of beliefs about the likelihood of different states of the world tomorrow. Our basic assumption is that boards as well as directors aggregate opinions of each other: a production plan is better than another for a board (director) provided every director (board of which she is a member) finds it better. Opinions are stable provided aggregation does not result in revision of opinions. We show that for connected networks: opinions are stable if and only if they are unambiguous and identical; and repeated aggregation leads to stable opinions. Hence, there will eventually be a single society-wide intersubjective “truth”.

A new decision making model based on Rank Centrality for GDM with fuzzy preference relations

Preference aggregation in Group Decision Making (GDM) is a substantial problem that has received a lot of research attention. Decision problems involving fuzzy preference relations constitute an important class within GDM. Legacy approaches dealing with the latter type of problems can be classified into indirect approaches, which involve deriving a group preference matrix as an intermediate step, and direct approaches, which deduce a group preference ranking based on individual preference rankings. Although the work on indirect approaches has been extensive in the literature, there is still a scarcity of research dealing with the direct approaches. In this paper we present a direct approach towards aggregating several fuzzy preference relations on a set of alternatives into a single weighted ranking of the alternatives. By mapping the pairwise preferences into transitions probabilities, we are able to derive a preference ranking from the stationary distribution of a stochastic matrix. Interestingly, the ranking of the alternatives obtained with our method corresponds to the optimizer of the Maximum Likelihood Estimation of a particular Bradley-Terry-Luce model. Furthermore, we perform a theoretical sensitivity analysis of the proposed method supported by experimental results and illustrate our approach towards GDM with a concrete numerical example. This work opens avenues for solving GDM problems using elements of probability theory, and thus, provides a sound theoretical fundament as well as plausible statistical interpretation for the aggregation of expert opinions in GDM.

Research on the Design of a Hexagonal Shaft Straightening Machine Based on Quality Function Development and Evidence Theory

In response to the problem that weights in product planning and design are difficult to accurately reflect the real needs of users, a design method for hexagonal shaft straightening machines based on quality function configuration and evidence theory is proposed. First, based on the method of quality function deployment and evidence theory, the demand indicator and its importance are determined by market research and the Kano model. Second, to solve the conflict problem in the process of expert opinion aggregation, combined with evidence theory, taking expert knowledge as evidence, the possible value space of causal strength among nodes constitutes the recognition framework, which is used to synthesize the knowledge of multiple experts. The indicator correlation degree is reflected by using a correlation weight matrix, and effective solutions to the relevant theoretical problems and algorithms, such as those for competitive indicator evaluation, weight calculation and planning target value cases, are proposed. Finally, according to the analysis results for the various structural elements of the house of quality, a hexagonal shaft straightening machine function for master planning house of quality was constructed, and propose a design and build the main experimental platform. The relevant test results show that the technical specifications of the hexagonal shaft straightening machine basically meet the requirements of the overall planning target values, verifying the effectiveness and feasibility of the proposed method.

Revealing Opinions for COVID-19 Questions Using a Context Retriever, Opinion Aggregator, and Question-Answering Model: Model Development Study.

BackgroundCOVID-19 has challenged global public health because it is highly contagious and can be lethal. Numerous ongoing and recently published studies about the disease have emerged. However, the research regarding COVID-19 is largely ongoing and inconclusive.ObjectiveA potential way to accelerate COVID-19 research is to use existing information gleaned from research into other viruses that belong to the coronavirus family. Our objective is to develop a natural language processing method for answering factoid questions related to COVID-19 using published articles as knowledge sources.MethodsGiven a question, first, a BM25-based context retriever model is implemented to select the most relevant passages from previously published articles. Second, for each selected context passage, an answer is obtained using a pretrained bidirectional encoder representations from transformers (BERT) question-answering model. Third, an opinion aggregator, which is a combination of a biterm topic model and k-means clustering, is applied to the task of aggregating all answers into several opinions.ResultsWe applied the proposed pipeline to extract answers, opinions, and the most frequent words related to six questions from the COVID-19 Open Research Dataset Challenge. By showing the longitudinal distributions of the opinions, we uncovered the trends of opinions and popular words in the articles published in the five time periods assessed: before 1990, 1990-1999, 2000-2009, 2010-2018, and since 2019. The changes in opinions and popular words agree with several distinct characteristics and challenges of COVID-19, including a higher risk for senior people and people with pre-existing medical conditions; high contagion and rapid transmission; and a more urgent need for screening and testing. The opinions and popular words also provide additional insights for the COVID-19–related questions.ConclusionsCompared with other methods of literature retrieval and answer generation, opinion aggregation using our method leads to more interpretable, robust, and comprehensive question-specific literature reviews. The results demonstrate the usefulness of the proposed method in answering COVID-19–related questions with main opinions and capturing the trends of research about COVID-19 and other relevant strains of coronavirus in recent years.

Mental models for assessing coastal social-ecological systems following disasters

Understanding abrupt change in social-ecological systems (SES), such as following major disasters, is key for restoration, adaptation, and sustainability. We describe an approach and case study using fuzzy cognitive mapping (FCM) and mental models to collectively represent local perceptions of Hurricane Irma in Key West, Florida. Using in-person FCM interviews, we sought to answer three overarching questions: 1) how did residents perceive SES relationships post-disaster, 2) how did these perceptions vary across three stakeholder groups consisting of those working in ocean recreation, resorts and inns, and non-tourism sectors, and 3) how did the aggregation of diverse opinions and perceptions affect the understanding of SES trade-offs regarding the coastal region’s resilience? Across all respondents, housing was the most commonly mentioned concept occurring in 39% of all models. Coral reefs were included in 71% of ocean recreation models, whereas tourism was included in 50% of resorts and inns models. While mangroves, seawalls, and beaches were rarely to never mentioned until prompted by the interviewer, scenario analyses revealed that shorelines play an important role in residents’ perceptions of how Hurricane Irma collectively impacted people and the environment. Sensitivity analyses revealed that the community, composed of a diverse representation of stakeholders, viewed core SES components similarly and trade-offs were acknowledged. Yet, different stakeholder groups perceived the SES in which they live in different ways, with each group perceiving greater damage to specific system components based on direct experience and knowledge. Our study demonstrates an approach for representing the collective perceptions of communities in post-disaster research, rebuilding, and restoration efforts.

Linear uncertain extensions of the minimum cost consensus model based on uncertain distance and consensus utility

Uncertainty theory adopts the belief degree and uncertainty distribution to ensure good alignment with a decision-maker’s uncertain preferences, making the final decisions obtained from the consensus-reaching process closer to the actual decision-making scenarios. Under the constraints of the uncertain distance measure and consensus utility, this article explores the minimum-cost consensus model under various linear uncertainty distribution-based preferences. First, the uncertain distance is used to measure the deviation between individual opinions and the consensus through uncertainty distributions. A nonlinear analytical formula is derived to avoid the computational complexity of integral and piecewise function operations, thus reducing the calculation cost of the uncertain distance measure. The consensus utility function defined in this article characterizes the adjustment value and degree of aggregation of individual opinions. Three new consensus models are constructed based on the consensus utility and linear uncertainty distribution. The results show that, in complex group decision-making contexts, the uncertain consensus models are more flexible than traditional minimum-cost consensus models: compared with the high volatility of the adjusted opinions in traditional deterministic consensus models with crisp number-based preferences, the variation trends of both individual adjusted opinions and the collective opinion with a linear uncertainty distribution are much smoother and the fitting range is closer to reality. The introduction of the consensus utility not only reflects the relative changes of individual opinions, but also accounts for individual psychological changes during the opinion-adjustment process. Most importantly, it reduces the cost per unit of consensus utility, facilitates the determination of the optimal threshold for the consensus utility, and improves the efficiency of resource allocation.

A Biobjective Optimization Model for Expert Opinions Aggregation and Its Application in Group Decision Making

Expert opinions aggregation is a generic part of the group decision making (GDM) problem. The challenge of expert opinions aggregation is to reduce the subjectivity in the process as much as possible and improve the reliability of the aggregated opinion. Most of the existing literature try to eliminate the subjectivity but seldom consider the reliability of the aggregation result. In this article, we propose a new criterion that contains consensus level and confidence level to improve both objectivity (i.e., consensus) and reliability (i.e., no absurd result) with the experts' opinions being represented as probability density functions. Subsequently, the expert opinion aggregation problem is formulated as a biobjective optimization model. The Survey of Professional Forecasters is used as an example to examine the feasibility and accuracy of the proposed approach and the result shows that the new approach can provide a better estimation than that of the single objective model in the literature. To our best knowledge, the proposed criterion is new in the literature of GDM along with relevant problems. The proposed criterion is actually a pilot work to probe the problem of the quality of a GDM process, which is largely ignored in the field of GDM.

Aggregation of Opinions and Risk Measures

A ranking over a set of alternatives is an aggregation of experts' opinions (AEO) if it depends on the experts' assessments only. We study both those rankings that result from pooling Bayesian experts and those that result from pooling possibly non-Bayesian experts. In the non-Bayesian case, we allow for the simultaneous presence of experts that may display very different attitudes toward uncertainty. We show that a unique axiom along with a mild regularity condition fully characterize those AEO rankings which are generalized averages of experts' opinions, in the sense that the average is obtained by using a capacity rather than a probability measure. We call these rankings non-linear pools. We consider a number of special cases such as linear pools (Stone (1961)), concave/convex pools (Cres, Gilboa, and Vieille (2011)), quantiles, and pools of equally reliable experts. We then apply our results to the theory of risk measures. Our application can be viewed as a generalization of the robust approach (Glasserman and Xu (2013)) to risk measurement in that it allows both for a more general notion of model and more general aggregation rules. We show that a wide class of risk measures can be regarded as non-linear pools. Not only does this class include all coherent risk measures (Artzner et al. (1999)), but also measures like the Value-at-Risk, which fail subadditivity. We also briefly discuss the possibility of extending our findings to include the convex risk measures of Follmer and Schied (2002) as well as their non-subadditive extensions.

Type-2 fuzzy mixed-integer bi-level programming approach for multi-source multi-user water allocation under future climate change

Global climate change and increasing pressure of water shortage reinforce the necessity of optimal water management. In this study, a type-2 fuzzy mixed-integer bi-level programming (T2FMBP) approach is developed for the identification of reasonable water allocation policies by incorporating type-2 fuzzy sets, mixed-integer linear programming, bi-level programming and Stewart model into a general optimization framework. The T2FMBP model improves upon conventional bi-level programming for handling tradeoffs between two-level decision makers considering multiple water allocation processes. It is capable of addressing parameter uncertainties caused by natural conditions and human activities, and supporting in-depth analysis of water allocation schemes associated with future climate change. In this study, group opinion aggregation method for fuzzy number construction, critical value method for type-2 fuzzy number defuzzification, and fuzzy coordination method for solving bi-level programming are adopted. Then, the model was demonstrated for a real-world case study in the Zhanghe Irrigation District for multi-source multi-user water resources planning. The results show that the proposed model can tackle tradeoffs among hierarchical decision makers’ conflicting concerns under uncertainties characterized as type-2 fuzzy numbers. Besides, the results can help generate water allocation schemes among different water sectors as well as different crop growth stages effectively under three hydrological years (wet year, normal year, and dry year) and three representative concentration pathway (RCP) scenarios (RCP 2.6, RCP 4.5, and RCP 8.5). Furthermore, comparison was made between T2FMBP model and four single-level models with same constraints. The comparison reveals that T2FMNP model considers all the targets and makes excellent balance between two-level decision makers. A comparison between current situation and RCP scenarios will help make policies for foresight. The formulated framework is applicable for similar regions to determine water strategies in a changing environment, thus promote sustainable development of socio-economic and water resources system.

A Reliable Weighting Scheme for the Aggregation of Crowd Intelligence to Detect Fake News

Social networks play an important role in today’s society and in our relationships with others. They give the Internet user the opportunity to play an active role, e.g., one can relay certain information via a blog, a comment, or even a vote. The Internet user has the possibility to share any content at any time. However, some malicious Internet users take advantage of this freedom to share fake news to manipulate or mislead an audience, to invade the privacy of others, and also to harm certain institutions. Fake news seeks to resemble traditional media to establish its credibility with the public. Its seriousness pushes the public to share them. As a result, fake news can spread quickly. This fake news can cause enormous difficulties for users and institutions. Several authors have proposed systems to detect fake news in social networks using crowd signals through the process of crowdsourcing. Unfortunately, these authors do not use the expertise of the crowd and the expertise of a third party in an associative way to make decisions. Crowds are useful in indicating whether or not a story should be fact-checked. This work proposes a new method of binary aggregation of opinions of the crowd and the knowledge of a third-party expert. The aggregator is based on majority voting on the crowd side and weighted averaging on the third-party side. An experimentation has been conducted on 25 posts and 50 voters. A quantitative comparison with the majority vote model reveals that our aggregation model provides slightly better results due to weights assigned to accredited users. A qualitative investigation against existing aggregation models shows that the proposed approach meets the requirements or properties expected of a crowdsourcing system and a voting system.

Design of a Category Independent, Aspect Based Automated Opinion Analysis Technique for Online Product Reviews

ABSTRACT Customer feedback in the form of online reviews is an important source of information to manufacturers or service providers for evaluation of their products or services. Online reviews also help potential buyers in making their decisions. Manual checking of these huge amount of unstructured texts is time consuming. Several attempts have been made for opinion aggregation of online reviews but a generalized automated technique has yet to be developed. In this work, an efficient rule based technique for aspect wise summarization of online product reviews irrespective of their categories has been designed. The proposed technique develops the rules for extracting aspects and associating the opinion words to the respective aspects followed by effective grouping and summarization of aspect-opinion pairs into human interpretable form. The algorithm has been implemented on Amazon Product Reviews and evaluated against manually annotated ground truth. The result shows promising similarity with human judgement.

Aggregation of experts' opinions and conditional consensus opinion by the Steiner point

This paper considers the issue of aggregation of experts' opinions expressed through not necessarily independent and even conflicting probability distributions or multiple priors. The paper addresses the elicitation of the consensus distribution by introducing the Steiner point of the set of all common distributions among experts and derives its conditional probabilities on future events.

A Framework to Overcome Hesitancy of Decision-Makers in E-Government Web Site Evaluation

Considering the undeniable role of websites in the success of interactions between citizens and governments, evaluating e-government web sites is a worthwhile topic. This research has developed a multi-attribute-decision-making technique rely on Step-Wise Weight Assessment Ratio Analysis and hesitant fuzzy weighted geometric Heronian mean decision-making methods, for evaluating e-government websites. Proposed framework overcome the limitations of previous investigations such as the possibility of aggregation of expert opinions using group decision-making and modeling the hesitancy of the experts (due to the incompleteness of their knowledge or their doubts in evaluating different aspects of the model) through hesitant fuzzy sets. The designed framework was used in Iran to evaluate e-government websites. In this regard, after a careful and systematic review of previous researches, a list of dimensions and evaluation criteria was presented in the form of a comprehensive model. Then, an expert-based process was proposed to localize the model according to the conditions of the case study. The results of the research show that the final rankings of the decision-making method used in the hesitant environment rarely changed for different operators. So as the experts have confirmed, the model used in this study has acceptable stability.

Gender Bias in Opinion Aggregation

Gender Bias in Opinion Aggregation

Gender Bias in Opinion Aggregation

Gender biases have been documented in areas including hiring, promotion or performance evaluations. Many of these decisions are made by committees. We experimentally investigate whether committee deliberation contributes to gender biases. In our experiments participants perform a real effort task with subjective performance and rate the task performance of other participants. In a 3 $\times$ 2 design we vary the extent to which communication among raters is possible and whether or not the experiment is gender-blind. There is little evidence of gender bias in the absence of communication or with minimal communication. By contrast, gender bias is strong and highly statistically significant under open communication. In the latter case 60 percent of ratings received by men are revised upwards after deliberation compared to only 25 percent of ratings received by women. As a consequence women are ranked on average three positions lower after deliberation. We then test two interventions for open deliberation. Randomizing the order of speaking does not reduce gender bias, but an information intervention where raters are informed of gender bias in prior sessions does.

Expert judgments calibration and combination for assessment of river levee failure probability

In civil engineering, the utilisation of expert judgement is important for evaluating the reliability of structures whose failure behaviour is little known and difficult to quantify. This article presents the application of the approaches of elicitation, calibration and aggregation of expert opinions to evaluate the reliability of river levees. These approaches have seldom been used in the field of French hydraulic structures whereas there is growing interest for them in other countries, concerning structures reliability and other fields such as aerospace industry, nuclear industry, hydrology, statistics, economics, psychology etc. This article proposes a quantitative approach to the use of expert judgement for evaluating river levee failure probability. An application to the case of an existing river levee is presented. The approach developed assesses the expert opinions according to their calibration (statistical accuracy) and their informativeness, then aggregates them in a single calibrated opinion. Applying the proposed weighting and aggregation procedure reduces variability in the probability of failure estimate, then it provides a better probabilistic distribution of aggregated expert opinion than of individual expert opinions. The results allow identifying a trend of aggregated expert opinions that point towards over- or lack of confidence.

Consensus Opinion Model in Online Social Networks Based on Influential Users

A framework to consensus opinion model within a networked social group is put forward. The current research in opinion formation within the groups is largely based on the opinion aggregation of each user of the network. However, the consistency of users in aggregation, social power, and the impact of each individual user of the group for opinion formation are not considered. In this paper, we investigate a consensus opinion model in social groups based on the impact of influential users and aggregation methods. In order to reach the consensus model, we aggregate the users' opinions. To maintain consistency, we propagate the opinion through the users to reach an agreement. This propagation will consider the influential users' impacts that have a crucial effect on its process. A novel method is proposed to detect the influential users and opinion propagation based on them to derive the opinion toward the networked social group. In particular, we applied optimism and pessimism scores as the users' personality to discover the influential users in the network. Considering that, we propagate the opinion based on two facts: 1) the impact of influential users, derived by the presence of an extremely confident individual in the network and 2) the impact of neighbors, induced by the presence of the users who have a connection with the current user. Then, we proposed the opinion aggregation of the group induced by the weighted averaging operator and fuzzy techniques. In order to evaluate the validity of the method, we used enormous data sets of Epinions and Etsy which are signed and unsigned, respectively.

Effective aggregation of expert opinions to inform environmental management: An integrated fuzzy group decision-making framework with application to cadmium-contaminated water treatment alternatives evaluation

Application of group decision making to practical environmental management problems indicates that effectively avoiding the loss of decision information and reasonably aggregating expert views are two main challenges during the decision-making process. Hence, this paper focuses on the utilization of interval-valued triangular fuzzy numbers for expressing expert opinions and the introduction of Group-G1 and Hamming-based weighted polymerization operator to incorporate the fuzzy information provided by individual experts, into group consensus opinion. The developed integrated fuzzy group decision-making framework was then applied to the evaluation and selection of the most desirable technology for removing low-concentration cadmium ions from water. It has been determined that hydroxide precipitation was the most preferred option at the current stage because of its relative simplicity and high maturity. However, the rankings generated based on expert opinions suggested that adsorption technologies, especially biosorption, could also be preferred because of its low-cost and environment-friendly characteristics. Finally, the reliability of the resulting priority-rankings was investigated by sensitivity analysis. The result showed this fuzzy group decision-making framework is potentially of great value for developing robust environmental recommendations and thus enables experts to make informed decisions and achieve optimal results.

Mayors and “their” land: Revealing approaches to flood risk management in small municipalities

The multilevel adaptive governance of flood risk reduction has been emphasised in the last decades and supported by the requirements of European Flood Directive (EC, 2007), while assigning an increasingly important role to municipalities. By contrast, only fragmented efforts have addressed the personal (expert knowledge), financial, and institutional limits of municipalities to achieve the flood risk management (FRM) goals. Starting with thorough literature review, we emphasise the key gaps in current understanding of municipal FRM and note that scarce attention has been paid to small‐sized municipalities (both in area and population). Next, we present an empirical case study based on semi‐structured interviews with 17 mayors of small municipalities in a flood‐prone area of northern Czechia. The aggregation of the mayors' opinions about flood threat and current and planned FRM measures as well as the perceived barriers in their implementation enabled the classification of different FRM strategies at a municipal level (herein referred to as “self‐confidence,” “fatalism,” and “active scepticism”). These strategies are then discussed in terms of the mayors' leadership styles and the significant consequences on the effectiveness and efficiency of FRM at a municipal level.