Suchergebnisse

Preface -- Part I: Introduction -- Group decision making -- Consensus reaching process -- Part II: Consensus models with the minimum adjustments and aggregation operators -- Minimum adjustments consensus models -- Maximum expert consensus models -- Part III: Consensus in the multiple attribute group decision making -- Consensus rule I: minimizing the distance between original and adjusted preferences -- Consensus rule II: minimizing the number of adjusted preference values -- Interactive multiple attribute consensus framework to support consensus reaching -- Part IV: Consensus in the group decision making with preference relations -- Individual consistency issues in preference relations -- Consensus framework with minimum adjustments to integrate the individual consistency -- Part V: Consensus in the linguistic group decision making -- Linguistic consensus model with minimum adjustments -- Minimizing adjusted simple terms in the hesitant linguistic consensus reaching -- Part VI: Simulation experiments and comparison analysis -- Simulation experiments and comparison analysis -- Appendix.

In social network group decision making (SNGDM), the consensus reaching process (CRP) is used to help decision makers with social relationships reach consensus. Many CRP studies have been conducted in SNGDM until now. This paper provides a review of CRPs in SNGDM, and as a result it classifies them into two paradigms: (i) the CRP paradigm based on trust relationships, and (ii) the CRP paradigm based on opinion evolution. Furthermore, identified research challenges are put forward to advance this area of research. ; National Natural Science Foundation of China (NSFC) 71571124 71725001 ; Sichuan University sksyl201705 ; Spanish Ministry of Economy and Competitiveness TIN2016-75850-R ; European Union (EU)

AbstractDue to the server bed shortage, which has raised ethical dilemmas in the earliest days of the COVID‐19 crisis, medical capacity investment has become a vital decision‐making issue in the attempt to contain the epidemic. Furthermore, economic strength has failed to explain the significant performance difference across countries in combatting COVID‐19. Unlike common diseases, epidemic diseases add substantial unpredictability, complexity, and uncertainty to decision‐making. Knowledge miscalibration on epidemiological uncertainties by policymaker's over‐ and underconfidence can seriously impact policymaking. Ineffective risk communication may lead to conflicting and incoherent information transmission. As a result, public reactions and attitudes could be influenced by policymakers' confidence due to the level of public trust, which eventually affects the degree to which an epidemic spreads. To uncover the impacts of policymakers' confidence and public trust on the medical capacity investment, we establish epidemic diffusion models to characterize how transmission evolves with (and without) vaccination and frame the capacity investment problem as a newsvendor problem. Our results show that if the public fully trusts the public health experts, the policymaker's behavioral bias is always harmful, but its effect on cost increment is marginal. If a policymaker's behavior induces public reactions due to public trust, both the spread of the epidemic and the overall performance will be significantly affected, but such impacts are not always harmful. Decision bias may be beneficial when policymakers are pessimistic or highly overconfident. Having an opportunity to amend initially biased decisions can debias a particular topic but has a limited cost‐saving effect.

This work was supported in part by the NSF of China under grants 71171160 and 71571124, in part by the SSEM Key Research Center at Sichuan Province under grant xq15b01, in part by the FEDER funds under grant TIN2013-40658-P, and in part by Andalusian Excellence Project under grant TIC-5991. ; The consensus reaching process (CRP) is a dynamic and iterative process for improving the consensus level among experts in group decision making. A large number of non-cooperative behaviors exist in the CRP. For example, some experts will express their opinions dishonestly or refuse to change their opinions to further their own interests. In this study, we propose a novel consensus framework for managing non-cooperative behaviors. In the proposed framework, a self-management mechanism to generate experts' weights dynamically is presented and then integrated into the CRP. This self-management mechanism is based on multi-attribute mutual evaluation matrices (MMEMs). During the CRP, the experts can provide and update their MMEMs regarding the experts' performances (e.g., professional skill, cooperation, and fairness), and the experts' weights are dynamically derived from the MMEMs. Detailed simulation experiments and comparison analysis are presented to justify the validity of the proposed consensus framework in managing the non-cooperative behaviors. ; National Natural Science Foundation of China 71171160 71571124 ; SSEM Key Research Center at Sichuan Province xq15b01 ; European Union (EU) TIN2013-40658-P ; Andalusian Excellence Project TIC-5991

AbstractThe outbreak of pandemics such as COVID‐19 can result in cascading effects for global systemic risk. To combat an ongoing pandemic, governmental resources are largely allocated toward supporting the health of the public and economy. This shift in attention can lead to security vulnerabilities which are exploited by terrorists. In view of this, counterterrorism during a pandemic is of critical interest to the safety and well‐being of the global society. Most notably, the population flows among potential targets are likely to change in conjunction with the trend of the health crisis, which leads to fluctuations in target valuations. In this situation, a new challenge for the defender is to optimally allocate his/her resources among targets that have changing valuations, where his/her intention is to minimize the expected losses from potential terrorist attacks. In order to deal with this challenge, in this paper, we first develop a defender–attacker game in sequential form, where the target valuations can change as a result of the pandemic. Then we analyze the effects of a pandemic on counterterrorism resource allocation from the perspective of dynamic target valuations. Finally, we provide some examples to display the theoretical results, and present a case study to illustrate the usability of our proposed model during a pandemic.

Preference–approval structure combines the preference information of both ranking and approval, which extends the ordinal preference model by incorporating two categories of choice alternatives, that is, acceptable (good) and unacceptable (bad), in the preference modeling process. In this study, we present some axioms that imply the existence of a unique distance function of preference–approval structures. Based on theoretical analysis and simulation experiments, we further study a preferences aggregation model in the group decision-making context based on the proposed axiomatic distance function. In this model, the group preference is defined as a preference–approval structure that minimizes the sum of its distances to all preference–approval structures of individuals in the group under consideration. Particularly, we show that the group preference defined by the axiomatic distance–based aggregation model has close relationships with the simple majority rule and Cook and Seiford's ranking.

AbstractIn defensive resource allocation problems, the defender usually collects some forecast information about the attacker. However, the forecast information may be incorrect, which means that there could be a risk associated with the defender using it in their decision making. In this article, we propose a forecast and risk control (FRC) framework to manage the risk in defensive resource allocation with forecast information. In the FRC framework, we introduce a new measure of risk and three types of defense plans: riskless defense plan, risky defense plan, and risk‐control defense plan. Several desirable properties based on the concepts of reward and penalty show that the risk‐control defense plan is a general form to support defensive resource allocation. Subsequently, we study a specific defensive allocation problem with forecast information and develop an optimization model that considers the forecast information and the defender's risk tolerance level in order to obtain the risk‐control defense plan with maximum reward. Furthermore, we provide some numerical analysis to illustrate the effects of forecast information and risk tolerance level on the risk‐control defense plan. Finally, a numerical case study is presented to demonstrate the usability of a risk‐control defense plan.