Large-scale group decision-making (LSGDM) deals with complex decision- making problems which involve a large number of decision makers (DMs). Such a complex scenario leads to uncertain contexts in which DMs elicit their knowledge using linguistic information that can be modelled using different representations. However, current processes for solving LSGDM problems commonly neglect a key concept in many real-world decision-making problems, such as DMs' regret aversion psychological behavior. Therefore, this paper introduces a novel consensus based linguistic distribution LSGDM (CLDLSGDM) approach based on a statistical inference principle that considers DMs' regret aversion psychological characteristics using regret theory and which aims at obtaining agreed solutions. Specifically, the CLDLSGDM approach applies the statistical inference principle to the consensual information obtained in the consensus process, in order to derive the weights of DMs and attributes using the consensus matrix and adjusted decision-making matrices to solve the decision-making problem. Afterwards, by using regret theory, the comprehensive perceived utility values of alternatives are derived and their ranking determined. Finally, a performance evaluation of public hospitals in China is given as an example in order to illustrate the implementation of the designed method. The stability and advantages of the designed method are analyzed by a sensitivity and a comparative analysis.

In the process of reaching consensus, it is necessary to coordinate different views to form a general group opinion. However, there are many uncertain factors in this process, which has brought different degrees of influence in group decision-making. Besides, these uncertain elements bring the risk of loss to the whole process of consensus building. Currently available models not account for these two aspects. To deal with these issues, three different modeling methods for constructing the two-stage mean-risk stochastic minimum cost consensus models (MCCMs) with asymmetric adjustment cost are investigated. Due to the complexity of the resulting models, the L-shaped algorithm is applied to achieve an optimal solution. In addition, a numerical example of a peer-to-peer online lending platform demonstrated the utility of the proposed modeling approach. To verify the result obtained by the L-shaped algorithm, it is compared with the CPLEX solver. Moreover, the comparison results show the accuracy and efficiency of the given method. Sensitivity analyses are undertaken to assess the impact of risk on results. And in the presence of asymmetric cost, the comparisons between the new proposed risk-averse MCCMs and the two-stage stochastic MCCMs and robust consensus models are also given.

In this paper we study the class of claims problems where the amount to be divided is perfectly divisible and claims are made on indivisible units of several items. Each item has a price, and the available amount falls short to be able to cover all the claims at the given prices. We propose several properties that may be of interest in this particular framework. These properties represent the common principles of fairness, efficiency, and non-manipulability by merging or splitting. Efficiency is our focal principle, which is formalized by means of two axioms: and . We show that some combinations of the properties we consider are compatible, others are not.

This paper proposes a new representation for the Public Help Index (briefly, PHI ). Based on winning coalitions, the PHI index was introduced by Bertini et al. in (2008). The goal of this article is to reformulate the PHI index using null player free winning coalitions. The set of these coalitions unequivocally defines a simple game. Expressing the PHI index by the winning coalitions that do not contain null players allows us in a transparent way to show the parts of the power assigned to null and non-null players in a simple game. Moreover, this new representation may imply a reduction of computational cost (in the sense of space complexity) in algorithms to compute the PHI index if at least one of the players is a null player. We also discuss some relationships among the Holler index, the PHI index, and the index (based on null player free winning coalitions) proposed by Álvarez-Mozos et al. in (2015).

Negotiations and conflicts do not evolve smoothly but are discontinuous involving transitions, break-, and turning points that change the flow of the negotiation. Given that these departures may be decisive in determining whether the involved parties come to a successful conclusion, several scholars have pointed out the importance of investigating whether impasse and settlement dyads exhibit different turning point profiles. To address this question, we extended Druckman's (J Confl Resolut 45:519-544, 2001) turning point model by integrating interlocking action-reaction sequences that initiate and (dis)confirm the departure from zero-sum bargaining. Furthermore, we consider social signals as previously not addressed class of events triggering the turning point. We propose and show that social signals act as precipitants to substantive change at the offer level and that how negotiators enact the action-reaction sequences discriminates between successful and unsuccessful dyads.

Massive open online courses (MOOC) are free learning courses based on online platforms for higher education, which not only promote the open sharing of learning resources, but also lead to serious information overload. However, there are many courses on MOOCs, and it can be difficult for users to choose courses that match their individual or group preferences. Therefore, a combined weighting based large-scale group decision-making approach is proposed to implement MOOC group recommendations. First, based on the MOOC operation mode, we decompose the course content into three stages, namely pre-class, in-class, and post-class, and then the curriculum-arrangement-movement- performance evaluation framework is constructed. Second, the probabilistic linguistic criteria importance through intercriteria correlation method is employed to obtain the objective weighting of the criterion. Meanwhile, the word embedding model is utilized to vectorize online reviews, and the subjective weighting of the criteria are acquired by calculating the text similarity. The combined weighting then can be obtained by fusing the subjective and objective weighting. Based on this, the PL-MULTIMIIRA approach and Borda rule is employed to rank the alternatives for group recommendation, and an easy-to-use formula for group satisfaction is proposed to evaluate the effect of the proposed method. Furthermore, a case study is conducted to group recommendations for statistical MOOCs. Finally, the robustness and effectiveness of the proposed approach were verified through sensitivity analysis as well as comparative analysis.

Crowdsourcing and crowd voting systems are being increasingly used in societal, industry, and academic problems (labeling, recommendations, social choice, etc.) due to their possibility to exploit "wisdom of crowd" and obtain good quality solutions, and/or voter satisfaction, with high cost-efficiency. However, the decisions based on crowd vote aggregation do not guarantee high-quality results due to crowd voter data quality. Additionally, such decisions often do not satisfy the majority of voters due to data heterogeneity (multimodal or uniform vote distributions) and/or outliers, which cause traditional aggregation procedures (e.g., central tendency measures) to propose decisions with low voter satisfaction. In this research, we propose a system for the integration of crowd and expert knowledge in a crowdsourcing setting with limited resources. The system addresses the problem of sparse voting data by using machine learning models (matrix factorization and regression) for the estimation of crowd and expert votes/grades. The problem of vote aggregation under multimodal or uniform vote distributions is addressed by the inclusion of expert votes and aggregation of crowd and expert votes based on optimization and bargaining models (Kalai-Smorodinsky and Nash) usually used in game theory. Experimental evaluation on real world and artificial problems showed that the bargaining-based aggregation outperforms the traditional methods in terms of cumulative satisfaction of experts and crowd. Additionally, the machine learning models showed satisfactory predictive performance and enabled cost reduction in the process of vote collection.

The decision to share information is a common phenomenon in individuals' daily social media use (e.g., Twitter, micro-blogs). However, research on the information to be shared mainly focuses on short texts, and the research on long texts/article sharing is relatively limited. Based on the elaboration likelihood model (ELM), this study established a conceptual model to reveal the determinants of users' behavior in sharing articles. Data on 1311 articles were collected on WeChat, China's most popular social media, and were processed using multiple linear regression. We found that both the central path and the peripheral path of the ELM affect users' decision-making about article-sharing behavior, and that amount of reading and perceived usefulness have the greatest impact. The rhetorical title, the number of pictures, and the number of fans have a negative impact on users' decision-making about article-sharing behavior. Further, the factors that affect users' online-community sharing and sharing with friends are also different. This study is one of the first to apply ELM to examine the influencing factors of users' decisions about sharing general articles on social media, contributing to the research on the decision-making behavior of users sharing long texts on social media.

In this paper, we apply the Analytic Hierarchy Process approach to conflict resolution in the context of the Russia-Ukraine conflict. We build models that illustrate the evaluation criteria, strategic and sub-criteria, and concessions for each party in this negotiation. Ratings are used to evaluate the degree to which concessions contribute or take away from successful resolution of the conflict. Afterwards, gain ratios are built to determine the benefit-cost scores so that concessions may be traded that result in equitable solutions. The approach presented here demonstrates for the first time why all concessions that parties to a conflict may offer might not trade all at once. A Max-Min optimization approach is used to maximize the gain to both parties of the conflict while minimizing the disparity in gain between the two.

A popular framework of the supplier selection process is usually characterized by problem definition, criteria formulation, supplier screening, and supplier selection. The literature review suggested limitations of this framework as it ignores the screening of criteria (beyond criteria weighing) and evaluators (buyers) and its inability to guide the supplier selection problems where a measure of confidence or trust is needed to confirm the reliability of the selected supplier. While extending de Boer's influential supplier selection framework, the current study argues that the supplier selection problem is not merely about ranking suppliers based on given criteria; instead, it involves evaluating criteria and evaluators as well. Guided by the theory of statistics and the Ordinal Priority Approach (OPA), the study pioneers a probabilistic approach of supplier evaluation and selection under incomplete information using a novel Confidence Level measure. The study suggests, the probability that a supplier shortlisted for selection is actually the optimum choice or not can be explained through a probability distribution, called W-distribution, therefore, confidently preventing the decision-makers from selecting the sub-optimum suppliers. The study presents a novel contribution to the theory of multiple-attribute decision-making through the OPA. The proposed approach can help build intelligent decision support systems to aid managers while providing them with early warning tools and suggestions to improve confidence in their selection.

Crowdsourcing holds great potential: macro-task crowdsourcing can, for example, contribute to work addressing climate change. Macro-task crowdsourcing aims to use the wisdom of a crowd to tackle non-trivial tasks such as wicked problems. However, macro-task crowdsourcing is labor-intensive and complex to facilitate, which limits its efficiency, effectiveness, and use. Technological advancements in artificial intelligence (AI) might overcome these limits by supporting the facilitation of crowdsourcing. However, AI's potential for macro-task crowdsourcing facilitation needs to be better understood for this to happen. Here, we turn to affordance theory to develop this understanding. Affordances help us describe action possibilities that characterize the relationship between the facilitator and AI, within macro-task crowdsourcing. We follow a two-stage, bottom-up approach: The initial development stage is based on a structured analysis of academic literature. The subsequent validation & refinement stage includes two observed macro-task crowdsourcing initiatives and six expert interviews. From our analysis, we derive seven AI affordances that support 17 facilitation activities in macro-task crowdsourcing. We also identify specific manifestations that illustrate the affordances. Our findings increase the scholarly understanding of macro-task crowdsourcing and advance the discourse on facilitation. Further, they help practitioners identify potential ways to integrate AI into crowdsourcing facilitation. These results could improve the efficiency of facilitation activities and the effectiveness of macro-task crowdsourcing.

Based on previous evidence, the use of blockchain for improving Supply Chains (SCs) regarding humanitarian projects has received attention over the past five years. The present study is innovative in investigating crucial parameters affecting the using of Blockchain Technology (BT) in Humanitarian Supply Chains (HSCs). More precisely, this study emphasizes parameters that affect blockchain in the HSCs and presents a new fuzzy large-scale group decision-making trial and evaluation laboratory (fuzzy large-scale group-DEMATEL) approach to analyze the interdependence of contributing factors for using BT in HSCs. This method consists of two stages: (1) clustering the large-scale group-experts into small subgroups by their characteristics, and (2) identifying the key factors affecting BT in HSCs with a novel fuzzy large-scale group-DEMATEL approach. According to experts, in this study, among the 25 evaluated factors, disintermediation has been identified as the most important one, followed by anonymity and security. A closer look reveals that 13 and 12 factors have been "cause" and "effect" factors, respectively. Our research can be used to promote the effectiveness of using BT in HSCs, so as to promote the proper distribution of relief materials in practical disasters.

In this article, we review the use of Artificial Intelligence to provide intelligent dispute resolution support. In the early years there was little systematic development of such systems. Rather a number of ad hoc systems were developed. The focus of these systems was upon the technology being utilised, rather than user needs. Following a review of historic systems, we focus upon what are the important components of intelligent Online Dispute Resolution systems. Arising from this review, we develop an initial model for constructing user centric intelligent Online Dispute Resolution systems. Such a model integrates Case management, Triaging, Advisory tools, Communication tools, Decision Support Tools and Drafting software. No single dispute is likely to require all six processes to resolve the issue at stake. However, the development of such a hybrid ODR system would be very significant important starting point for expanding into a world where Artificial Intelligence is gainfully used.

Online peer-to-peer (P2P) sales of used and or high-value goods are gaining more and more relevance today. However, since potential buyers cannot physically examine the product quality during online sales, information asymmetries and consequently uncertainty and mistrust that already exist in offline sales are exacerbated in online markets. Authenticated data platforms have been proposed to solve these problems by providing authenticated data about the negotiation object, integrating it into text-based channels secured by IT. Yet, we know little about the dynamics of online negotiations today and the impact of the introduction of authenticated data on online negotiation behaviors. We address this research gap based on two experimental studies along with the example of online used car trade. We analyze users' communicative and strategic actions in current P2P chat-based negotiations and examine how the introduction of authenticated data affects these behaviors using a conceptional model derived from literature. Our results show that authenticated data can promote less complex negotiation processes and more honest communication behavior between buyers and sellers. Further, the results indicate that chats with the availability of authenticated data can positively impact markets with information asymmetries. These insights provide valuable contributions for academics interested in the dynamics of online negotiations and the effects of authenticated data in text-based online negotiations. In addition, providers of trade platforms who aim to advance their P2P sales platforms benefit by achieving a competitive advantage and a higher number of customers.

We present a consensus improvement mechanism based on prospect theory and quantum probability theory (QPT) that enables the manifestation of irrational and uncertain behaviors of decision makers (DMs) in linguistic distribution group decision making. In this framework, the DMs pursue the possibility of working with different partial agreements on prospect values. Considering that the reference information should be comprehensive and accurate as it guides information modification and affects consensus efficiency, objective and subjective information is integrated to obtain the information. Several studies have verified that the interference effect will occur when the brain beliefs flow towards the different decision classification paths. To address this problem, QPT is introduced into the information integration and the optimized value of the interference term can be acquired by the designed multi-objective programming model based on the maximum individual utility. Finally, as the reference point changes during the preference adjustment process, a dynamic reference point-oriented consensus model is constructed to obtain the optimized modification. A case study is performed on the emergency plan for the selection of designated hospitals, and comparative analyses are performed to demonstrate the feasibility and advantages of the proposed model. Several important insights are offered to simulate the most likely possibility of consciousness flowing into different decision classifications for DMs and moderators.

This study examines the impacts of process frames and salience of a turning point on negotiators' responses to a departure during the negotiation process. Results show that individuals negotiating within an integrative-cooperative (as opposed to a distributive-competitive frame) are more likely to interpret the departure as a turning point and match the other's offer. Similarly, results show that making the departure salient by clearly articulating the intent, content, and function of the turning point offer increases negotiators' propensity to embrace the mutually beneficial turning point offer. The findings are discussed in light of negotiators' awareness of events during the negotiation process, their (mis)matching of favorable offers, and relational order theory.

The recent outbreak of COVID-19 posed discontinuous disruption to traditional learning modes worldwide. In order to keep social distance, online collaborative learning has become a necessity during the pandemic. However, our understanding of students' well-being and satisfaction with online collaborative learning is limited, especially during the COVID-19 period. Leveraging expectation confirmation theory, this study focuses on the triggers and inhibitors of students' cognitive load during online collaborative learning process and their subsequent satisfaction with the learning mode during the pandemic. We used a mixed-method approach in this study. We conducted a qualitative study with interview data and a quantitative study with surveys. The results indicate several psychological and cognitive antecedents of students' cognitive load during online collaborative learning. Findings also indicate that a high level of cognitive load will decrease students' perceived usefulness of the online learning platform and expectation confirmation, thus leading to a low level of satisfaction with online collaborative learning. This study can provide theoretical and practical implications for a better understanding of online student groups' satisfaction with online collaborative learning during the COVID-19 period.