Consensus-trust Driven Bidirectional Feedback Mechanism for Improving Consensus in Social Network Large-group Decision Making

2023 | Group Decision and Negotiation | Citations: 0

Authors: Gai, Tiantian; Cao, Mingshuo; Chiclana, Francisco; Zhang, Zhen; Dong, Yucheng; Herrera-Viedma, Enrique; Wu, Jian

Abstract: This paper proposes a consensus-trust driven framework of bidirectional interact ... Expand

Abstract: This paper proposes a consensus-trust driven framework of bidirectional interaction for social network large-group decision making. Firstly, the concepts of interaction consensus threshold and interaction trust threshold are defined, which are used to discriminate the interaction modes between subgroups into four categories. Corresponding hybrid feedback strategies are designed in which the consensus level and trust level of subgroups are regarded as reliable resources to facilitate the achievement of group consensus. Secondly, a minimum adjustment bidirectional feedback model considering cohesion is developed to help the interacting subgroups reach mutual consensus with minimum opinion modification. Finally, the proposed consensus framework is applied to a blockchain platform selection problem in supply chain to demonstrate the effectiveness and applicability of the model. Collapse

Topics: blockchain supply chain management decision making social network

Methods: computational algorithm simulation social network analysis trade-off analysis method multi objective optimization

Driving risk prevention in usage-based insurance services based on interpretable machine learning and telematics data

2023 | Decision Support Systems | Citations: 0

Authors: Li, Hong-Jie; Luo, Xing-Gang; Zhang, Zhong-Liang; Jiang, Wei; Huang, Shen-Wei

Abstract: Usage-based insurance (UBI) adjusts premiums based on an individual policyholder ... Expand

Abstract: Usage-based insurance (UBI) adjusts premiums based on an individual policyholder’s dynamic risk evaluation, incentivizing policyholders to maintain safe driving behavior in pursuit of a lower insurance premium. Although post-trip interventions can improve driving behavior significantly, most interventions do not provide risk mitigation strategies tailored to long-term driving behavior. This study proposes a novel approach for driving risk prevention in UBI services, providing risk analysis services and tailoring driving suggestions for policyholders. Advanced prediction models and model interpretation methods were adopted to assess and analyze personal driving risks, respectively. A multi-objective counterfactual model was developed to generate risk mitigation strategies for policyholders. The significance of driving behavior in risk mitigation and its actionability were assessed to provide realistic and actionable suggestions for policyholders. The case study, based on a real dataset from an insurance company in China, showed that the proposed approach can be directly embedded in the existing UBI service framework to provide personalized feedback and services to policyholders. Collapse

Topics: shapley additive explanation decision support system Python decision making logistics management

Methods: experiment computational algorithm machine learning XGBoost factor analysis

Theory-driven Bilateral Dynamic Preference Learning for Person and Job Match: A Process-oriented Multi-step Multi-objective Method

2023 | International Conference on Information Systems | Citations: 0

Authors: Shi, Xiaowei; Wang, Cong; Wei, Qiang

Abstract: Person-job matching is a typical dynamic process with bilateral interactions be ... Expand

Abstract: Person-job matching is a typical dynamic process with bilateral interactions between job seekers and jobs, along with sample imbalance issues. These characteristics pose significant challenges when designing an intelligent person-job match method. In this paper, we propose a novel process-oriented view of the person-job matching problem and formulate it as a multi-step multi-objective bilateral match learning problem. Our method combines profile features and historical sequential behaviors to learn the bilateral attributes and dynamic preferences, with multimodal data integrated through various attention mechanisms, such as the orthogonal multi-head and gated mechanisms. The method includes a sequence update module to learn the bilateral preferences and their updates sensitive to feedback. Furthermore, the multi-step constraint effectively solves the problem of imbalanced samples through partial relationships and information transmission between multi-objectives. Abundant experiments show that our method outperforms state-of-the-art methods in providing successful matches and improving recruitment efficiency. Collapse

Topics: decision making database system data integration privacy

Methods: machine learning experiment qualitative interview digital trace data trade-off analysis method

Will they take this offer? A machine learning price elasticity model for predicting upselling acceptance of premium airline seating

2023 | Information & Management | Citations: 0

Authors: Thirumuruganathan, Saravanan; Emadi, Noora Al; Jung, Soon-gyo; Salminen, Joni; Robillos, Dianne Ramirez; Jansen, Bernard J.

Abstract: Employing customer information from one of the world’s largest airline companies ... Expand

Abstract: Employing customer information from one of the world’s largest airline companies, we develop a price elasticity model (PREM) using machine learning to identify customers likely to purchase an upgrade offer from economy to premium class and predict a customer’s acceptable price range. A simulation of 64.3 million flight bookings and 14.1 million email offers over three years mirroring actual data indicates that PREM implementation results in approximately 1.12 million (7.94%) fewer non-relevant customer email messages, a predicted increase of 72,200 (37.2%) offers accepted, and an estimated $72.2 million (37.2%) of increased revenue. Our results illustrate the potential of automated pricing information and targeting marketing messages for upselling acceptance. We also identified three customer segments: (1) Never Upgrades are those who never take the upgrade offer, (2) Upgrade Lovers are those who generally upgrade, and (3) Upgrade Lover Lookalikes have no historical record but fit the profile of those that tend to upgrade. We discuss the implications for airline companies and related travel and tourism industries. Collapse

Topics: electronic mail marketing management price management customer satisfaction application programming interface

Methods: autoencoder data transformation machine learning experiment principal component analysis

Theories: optimization theory decision theory

All that Glitters is not Gold: Understanding the Impacts of Platform Recommendation Algorithm Changes on Complementors in the Sharing Economy

2023 | International Conference on Information Systems | Citations: 0

Authors: Tong, Jack; Zhang, Xiaowei; Luo, Xueming; Lin, Zhijie; Li, Jing

Abstract: Sharing platforms often leverage recommendation algorithms to reduce matching c ... Expand

Abstract: Sharing platforms often leverage recommendation algorithms to reduce matching costs and improve buyer satisfaction. However, the economic impacts of different recommendation algorithms on the business operations of complementors remains unclear. This study uses natural quasi-experiments and proprietary data from a homecooked food-sharing platform with two recommendation algorithms: word-of-mouth recommendation (WMR) and botler personalization recommendation (BPR). Results show the WMR negatively affects revenue while BPR has a positive effect. The contrast revenue effects have been attributed to capacity constraints for complementors and matching frictions for consumers. WMR encourages sellers to specialize in high-quality products but limits new product development. BPR promotes innovation to suit diverse customer tastes but may reduce quality. This reflects the exploration-exploitation tradeoff: WMR exploits existing competences, while BPR explores new products to satisfy personal preferences. The authors discuss implications for how to utilize recommendation algorithms and artificial intelligence for the prosperity of sharing economy platforms. Collapse

Topics: sharing platform innovation management service quality word of mouth mobile application

Methods: computational algorithm longitudinal research robustness check machine learning quasi experiment

Towards Just-In-Time Arrival for Container Ships by the Integration of Prediction Models

2023 | HICSS | Citations: 0

Authors: Yu, J.; Voß, S.

Abstract: Within the context of green shipping, the concept of Just-In-Time (JIT) arrival ... Expand

Abstract: Within the context of green shipping, the concept of Just-In-Time (JIT) arrival has attracted much attention. Research achieves the JIT arrival for container ships by combining the berth allocation and quay crane assignment problem (BACAP) and the vessel speed optimization (VSO), both subject to the data exchange. Many prediction models of the research to date generally aim to reduce the uncertainty of the communicated estimated time of arrivals. There is a lack of research that simultaneously assesses the application effect of prediction models on both plans of the BACAP and the VSO. Therefore, this paper proposes a twostage model that integrates the prediction of the vessel arrival time with the optimization of the BACAP-VSO. The application in our specific case study shows that the random forest performs best in the first stage. The results are forwarded to the second stage and lead to a reduction of the service delay, fuel consumption cost, and vessel emissions. Collapse

Topics: Python missing data

Methods: optimization model machine learning method computational algorithm experiment case study

Group Multi-Objective Optimization Under Imprecision and Uncertainty Using a Novel Interval Outranking Approach

2022 | Group Decision and Negotiation | Citations: 0

Authors: Fernández, Eduardo; Gómez-Santillán, Claudia; Rangel-Valdez, Nelson; Cruz-Reyes, Laura

Abstract: This paper deals with group multi-objective optimization problems with imperfect ... Expand

Abstract: This paper deals with group multi-objective optimization problems with imperfect information (imprecision, uncertainty, ill-definition, arbitrariness). Since collective preference, belief, and attitude towards conservatism are ill-defined concepts, the handling of imperfect information is a crucial issue in group decision making. Imperfect knowledge is modeled here using a novel interval-based outranking approach that permits us to handle the levels of conservatism from the group members and its dependence on the set of criteria. We propose two different methods that are appropriate for decision structures in the form of teams and committees. In these structures, there is a special actor (the so-called Supra Decision Maker, SDM) with authority for creating an aggregation of the group members’ preferences, beliefs,and levels of conservatism, and for taking the final collective decision. In the first method, the SDM behaves as an “altruistic dictator”. The SDM creates an “altruistic” aggregation of preferences, beliefs, and levels of conservatism in an outranking model based on interval numbers. This model is then used to identify a final solution that is a best compromise for the SDM, taking into account the interval-based aggregated information from group members. In the second method, each group member uses an interval outranking model to solve an individual optimization problem, and to identify his or her best solution. Based on these individual best solutions, the SDM behaves democratically, searching for a solution that maximizes the measures of group satisfaction and dissatisfaction; the solution is improved through several rounds of consensus-reaching. The potential of both methods is illustrated with a realistically sized example of many-objective project portfolio optimization. Collapse

Topics: decision making operating system

Methods: computational algorithm trade-off analysis method ELECTRE multi objective optimization optimization model

Dynamic Reference Point-Oriented Consensus Mechanism in Linguistic Distribution Group Decision Making Restricted by Quantum Integration of Information

2022 | Group Decision and Negotiation | Citations: 0

Authors: Tan, Xiao; Zhu, Jianjun; Wu, Tong

Abstract: We present a consensus improvement mechanism based on prospect theory and quantu ... Expand

Abstract: 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. Collapse

Topics: decision making user attitude

Methods: multi objective optimization trade-off analysis method optimization model cross sectional research computational algorithm

Theories: decision theory behavioral theory prospect theory probability theory

Expect the Unexpected: Engaging Users via Serendipitous Recommendations

2021 | International Conference on Information Systems | Citations: 0

Authors: Cui, Wei; Rajan, Vaibhav; Jiang, Zhenhui

Abstract: Recommender systems enable users to discover items of interest from a large set ... Expand

Abstract: Recommender systems enable users to discover items of interest from a large set of alternatives. Most recommender systems employ accuracy-oriented algorithms to predict user preferences. Overemphasis on accuracy leads to monotony in the items predicted, resulting in low customer satisfaction. Hence, to improve user experience, it is crucial to inject serendipity into recommendations by discovering users' latent preferences and recommending items that are both relevant and unexpected. However, there is a lack of serendipity-related labelled data and previous serendipitous recommendation algorithms are unable to strike a trade-off between serendipity and accuracy. We address the challenges by presenting a new dataset and designing a novel deep learning-based recommendation algorithm. Our experiments demonstrate that our model improves over state-of-the-art methods, in both recommendation accuracy and serendipity. We outline our plan to conduct a comprehensive user evaluation, through surveys and eye-tracking experiments, to understand how serendipitous recommendations impact user behavior in e-commerce. Collapse

Topics: collaborative filtering recommender system knowledge sharing user behavior electronic commerce

Methods: survey computational algorithm experiment deep learning oculometry

An Innovative Indicator System and Group Decision Framework for Assessing Sustainable Development Enterprises

2021 | Group Decision and Negotiation | Citations: 0

Authors: Deng, Xiang; Cheng, Xiang; Gu, Jing; Xu, Zeshui

Abstract: Empirical research suggests that companies pursuing sustainable development have ... Expand

Abstract: Empirical research suggests that companies pursuing sustainable development have higher credit ratings and lower equity costs. However, a review of the literature shows that no consensus has been reached regarding sustainable development indicators or appropriate decision-making methods for investment in sustainable development enterprises (SDEs). To address this gap, we build a set of sustainable development indicators to guide investors in assessing SDEs, allowing for more informed investment decisions regarding these potential opportunities. Next, to account for the subjectivity of the indicators and the relative weight of investors in group decision-making, we combine a q-rung orthopair fuzzy set with the multiplicative multi-objective optimization by ratio analysis method and introduce an optimization-based consensus model. Finally, we verify the validity of the proposed framework using a practical example. This framework has practical significance for real-world SDE investment and contributes to the literature by introducing a new approach to multi-attribute group decision-making problems. Collapse

Topics: decision making corporate governance fuzzy logic emergency management evaluation criteria

Methods: robustness check normalization data transformation multi objective optimization trade-off analysis method

Theories: fuzzy set theory