An Influence Network-Based Consensus Model for Large-Scale Group Decision Making with Linguistic Information

The vast majority of the existing social network-based group decision-making models require extra information such as trust/distrust, influence and so on. However, in practical decision-making process, it is difficult to get additional information apart from opinions of decision makers. For large-scale group decision making (LSGDM) problem in which decision makers articulate their preferences in the form of comparative linguistic expressions, this paper proposes a consensus model based on an influence network which is inferred directly from preference information. First, a modified agglomerative hierarchical clustering algorithm is developed to detect subgroups in LSGDM problem with flexible linguistic information. Meanwhile, a measure method of group consensus level is proposed and the optimal clustering level can be determined. Second, according to the preference information of group members, influence network is constructed by determining intra-cluster and inter-cluster influence relationships. Third, a two-stage feedback mechanism guided by influence network is established for the consensus reaching process, which adopts cluster adjustment strategy and individual adjustment strategy depending on the different levels of group consensus. The proposed mechanism can not only effectively improve the efficiency of consensus reaching of LSGDM, but also take individual preference adjustment into account. Finally, the feasibility and effectiveness of the proposed method are verified by the case of intelligent environmental protection project location decision.

Three-Dimensional Prescribed-Time Pinning Group Cooperative Guidance Law

In order to overcome the drawbacks of the convergence time boundary dependent on tuning parameters in existing finite/fixed-time cooperative guidance law, this paper presents a three-dimensional prescribed-time pinning group cooperative guidance scheme that ensures multiple unpowered missiles to intercept multiple stationary targets. Firstly, combining a prescribed-time scaling function with pinning group consensus theory, the prescribed-time consensus-based cooperative guidance law is proposed. Secondly, the prescribed-time convergence of the proposed pinning group consensus-based cooperative guidance law proves that the convergence can be achieved at a specified time, regardless of initial conditions and parameters. Furthermore, the design steps including two stages of the proposed guidance law are given for engineering application. Extensive simulations are carried out in three cases to verify the properties. Simulation results show the effectiveness and superiority of the proposed prescribed-time consensus-based cooperative guidance scheme.

Reports of the Death of Expertise May Be Exaggerated: Limits on Knowledge Resistance in Health and Medicine

During the past decade the idea that expertise is dead, or at best moribund, has become commonplace. Knowledge resistance appears to be growing more politicized and is increasing across a wide range of science-based topics, such as agriculture, evolution and genetics, vaccination, and climate change; even flat-earth beliefs are undergoing a renaissance. But in many of these areas, denying expert authority is cost-free in everyday behavior, making it more rational for people to prize identity and group affiliation over realism. To probe the health of expertise in a domain with everyday consequences for knowledge resistance, we conducted three incentive-compatible studies of laypeople’s preferences for sources of information they would read about specific medical conditions (e.g., heart disease, cancer, COVID-19). We found quite rational preference patterns, by which people preferred sources based on experts (physicians and scientists) over non-experts (celebrities and politicians) and group consensus (professional societies, polls) over individual opinions. These findings held most strongly for issues of personal medical concern, but were robust for less concerning health conditions, and for the highly politicized topic of COVID-19. Individuals who scored higher in intellectual humility and preferences for rational over experiential thinking were more likely to prefer the most expert sources. Expertise retains broad respect in the medical domain, at least when one’s own health is at stake.

A maximizing dominance degree model based on trust relationships and TODIM method to reach group consensus

To obtain the suitable alternative(s) for the organization, this paper proposes a more practical method to solve the decision-making problems in society. That is combined with the TODIM (TOmada de decisão interativa multicrit e ´ rio). The maximizing dominance degree model to reach consensus is proposed with two following components: (1) constructing the complete trust relationships network; (2) the maximizing dominance degree feedback mechanism to reach group consensus. Therefore, firstly owing to the complexity of the trust relationships network, judging the direct and indirect trust propagation paths among the decision makers (DMs) to construct the complete trust relationships network and identifying the highest value of Trust Score (TS) as the leader is possible. Then identify the inconsistent DM based on the established consensus index. During the feedback process, inconsistent DMs adopt the feedback mechanism based on the dominance degree of the leader until the group consensus is reached. Later, the corresponding ranking result is calculated by the TODIM method. Finally, a numerical example is applied to illustrate the effectiveness and feasibility of the optimal model.