The Opinion Dynamics and Bounded Confidence model on Flocking movement world

Abstract

We present and analyze a model of Opinion Dynamics and Bounded Confidence on the Flocking movement world. There are two systems for interaction. The theorem of 'Flocking' limits the agent's movement around the world and 'Bounded Confidence' chooses the agents to exchange the opinion. Every time step, agent i looks for the agents in its eyeshot and adjusts their opinion based on the algorithm of Bounded Confidence. When the exchange ends, every agent moves itself in a specifically direction according to Flocking theorem. We simulated the opinion formation process using the proposed model, results show the system is more realistic than the classic BC model.

A social network based approach for consensus achievement in multiperson decision making

“…Apart from that, we should bear in mind that providing suggestions does not mean that the agents will take them into account. In this sense, as aforementioned, it has been proved that agents tend to provide more attention to those who have opinions closer to them [13,16,35]. In this section we present a new similarity-consistency-confidence social network in which the agents opinion propagation is designed to provide recommendations in order to bring closer the agents opinions and at the same time increase the coherence and the quality of the provided opinions.…”

“…In the following we propose a new feedback model based on the HK [35] opinion dynamic model that assumes that in a social network the agents that are close will communicate between each other.…”

“…Every agent in the network receive a recommendation the preferences of the nodes connected to him/her fused by means of a Knowledge Degree Induced Ordering Weighting Averaging KD-IOWA Operator, that allocates more importance to those agents in the vicinity that presents higher KD. We have chosen to use the IOWA operator [61] instead of a pondered average as it is used in others opinion dynamics model [35,14] since in group decision making scenarios it has been proved its effectiveness for both trading of the information depending on certain criteria and dealing against malicious behaviour or manipulation [15]. Some examples of IOWA are the consistency based IOWA operator, [28], in which the reordering of arguments to aggregate as well as the computation of the aggregation weights are obtained using consistency degrees or the Consitency-Confidence IOWA, CC-IOWA [54] that strikes the balance between agents'consistency and confidence for the case of IFPRs.…”

“…With this regard, in the propagation process of public opinion, the formers have a profound impact on the opinion formation of the followers even helping to the faster propagations of opinions in a network [2]. Bounded confidence based approaches construct opinion dynamics models [56,35] to analyse the influence of opinion leaders in social networks revealing that, as long as the confidence levels of ordinary agents in a social group are sufficiently high, even if the initial opinions of the ordinary agents are dissimilar to those of the opinion leaders, the opinion leaders are eventually able to guide the ordinary agents to accept their desired opinions and so to reach a consensus solution.…”

Leader’s opinion priority bounded confidence model for network opinion evolution

“…[6~9] Agents refer to all those within their own thresholds of trust, and then the average of these views as their new perspective. Researcher Li Shusong et al [7] proposed a model of network public opinion evolution based on flocking strategy, and put forward the concept of "vision". Researcher Zhang LAN [8] proposed the evolutionary model of limited trust based on spatial constraints.…”