Coevolutionary spreading, the interdependent propagation of multiple-type information (or epidemics or social behaviors), has attracted both scientific and industrial attention due to its complex dynamics. While agent-based models (ABMs) are well-suited for modeling single-type contagion dynamics, they struggle to represent the microscopic interdependencies of co-evolving information types within different network topologies. This paper proposes a multi-information co-evolution propagation model based on self-organizing multi-agents, breaking through the limitations of traditional threshold spreading models and agent-based models. The model, which is validated through consistency with traditional SIR models under the circumstance of well-mixed agents, can be used to uncover the spreading mechanisms on different network topologies (such as ER, BA, WS) through a series of transmitting and recovering rules that act on each agent with social contagion behaviors and attributes. Furthermore, sophisticated spreading patterns, such as active counterattack and cooperative operation, are also explored based on this model to simulate the multi-information propagation process. These complex propagation simulations reveal some interesting phenomena: (1) When counterattacking the spread of a specific source information, blindly increasing the proportion of counterattackers or the information exclusion coefficient may not necessarily be the best choice, even without considering costs. (2) In networks with long-short loop structures, compared to the situation of single information dissemination, the coevolutionary spread of two types of information is more prone to avalanche phenomena, with the S (susceptible) state of information dropping sharply from a steady state of 60% to a steady state of 20% by the 10th generation. These findings provide actionable insights for controlling misinformation in social networks and optimizing public health interventions, emphasizing that "more intervention" does not always equate to "better control" in coevolutionary systems.

As a large, multiplicative, and diverse system, online social network allows people to study, share, collaborate, and spread rumors, which leads to the formation of online multiple information coevolution spreading networks (MICSNs). On these sophisticated networks, information spreads in an interactive way, and this coevolution spreading may cause emergence. When emergence is out of control, it may lead to some negative effects. So how to forecast emergence by stability monitoring is one of the most important bases for related issues. Most existing studies have focused only on separate macro-level factors or on the spreading principles of only one piece of information at a time. However, the coevolutionary spreading of several pieces of information leads to far greater monitoring and prediction difficulties. In this paper, by integrating mutual influencing factors (e.g., personal preferences, information acceptance, save endowment, and connection strength) a well-established two-stage feedback member model is proposed to reflect real situations of MICSNs. Based on this model, as an indicator of their states, a fixed point of MICSNs, stored information vector sum, is formally deducted. And the validity is verified by well-designed simulations, which compare value fluctuations of this fixed point with those of the irrational population and information existence time. Furthermore, the proposed fixed point can be used to monitor the states of MICSNs, alert administrators to potentially negative events, and provide theoretical guidance for public opinion analysis. © 2023 Elsevier Inc.

In this paper, the Crowd Intelligence Network Model is applied to the simulation of epidemic spread. This model combines the multi-layer coupling network model and the two-stage feedback member model to study the epidemic spread mechanisms under multiple-scene intervention. First, this paper establishes a multi-layer coupled network structure based on the characteristic of Social Network, Information Network, and Monitor Network, namely, the Crowd Intelligence Network structure. Then, based on this structure, the digital-self model, which has a multiple-scene effect and two-stage feedback structure, is designed. It has an emotional state and infection state quantified by using attitude and self-protection levels. This paper uses the attitude level and self-protection level to quantify individual emotions and immune levels, and discusses the impact of individual emotions on epidemic prevention and control. Finally, the availability of the Crowd Intelligence Network Model on the epidemic spread is verified by comparing the simulation trend with the actual spread trend of COVID-19. © The author(s) 2022.

The purpose of this study is to examine the influence of different parameters on the legitimacy rate and effective efficiency of crowd decision-making and to guide decision-making in real life. In this paper, a crowd decision representation method based on the preference domain is proposed for the large-scale simulation implementation of crowd decision in a crowd intelligence network, a simulation modeling is performed for the members participating in the decision, and a formal propulsion algorithm is perfected. Lastly, the influence of key parameters on the decision results is analyzed through a large-scale simulation experiment. This study analyzes the influence of key parameters, such as the number of candidates, number of voters, and voting legitimacy rate reference value, on the decision-making results and summarizes the selection range of key parameters under different results. Through the simulation experiment of crowd decision-making, this paper provides inspiration for researchers to explore the parameter sensitivity of crowd decision-making and provides guidance for crowd decision-making in social life. © The author(s) 2022.

Crowd phenomena are widespread in human society, but they cannot be observed easily in the real world, and research on them cannot follow traditional ways. Simulation is one of the most effective means to support studies about crowd phenomena. As modelbased scientific activities, crowd science simulations take extra efforts on member models, which reflect individuals who own characteristics such as heterogeneity, large scale, and multiplicate connections. Unfortunately, collecting enormous members is difficult in reality. How to generate tremendous crowd equivalent member models according to real members is an urgent problem to be solved. A crowd equivalence-based massive member model generation method is proposed. Member model generation is accomplished according to the following steps. The first step is the member metamodel definition, which provides patterns and member model data elements for member model definition. The second step is member model definition, which defines types, quantities, and attributes of member models for member model generation. The third step is crowd network definition and generation, which defines and generates an equivalent large-scale crowd network according to the numerical characteristics of existing networks. On the basis of the structure of the large-scale crowd network, connections among member models are well established and regarded as social relationships among real members. The last step is member model generation. Based on the previous steps, it generates types, attributes, and connections among member models. According to the quality-time model of crowd intelligence level measurement, a crowd-oriented equivalence for crowd networks is derived on the basis of numerical characteristics. A massive member model generation tool is developed according to the proposed method. The member models generated by this tool possess multiplicate connections and attributes, which satisfy the requirements of crowd science simulations well. The member model generation method based on crowd equivalence is verified through simulations. A simulation tool is developed to generate massive member models to support crowd science simulations and crowd science studies. © The author(s) 2022.

In the evolutionary game of the same task for groups, the changes in game rules, personal interests, the crowd size, and external supervision cause uncertain effects on individual decision-making and game results. In the Markov decision framework, a single-task multi-decision evolutionary game model based on multi-agent reinforcement learning is proposed to explore the evolutionary rules in the process of a game. The model can improve the result of a evolutionary game and facilitate the completion of the task. First, based on the multi-agent theory, to solve the existing problems in the original model, a negative feedback tax penalty mechanism is proposed to guide the strategy selection of individuals in the group. In addition, in order to evaluate the evolutionary game results of the group in the model, a calculation method of the group intelligence level is defined. Secondly, the Q-learning algorithm is used to improve the guiding effect of the negative feedback tax penalty mechanism. In the model, the selection strategy of the Q-learning algorithm is improved and a bounded rationality evolutionary game strategy is proposed based on the rule of evolutionary games and the consideration of the bounded rationality of individuals. Finally, simulation results show that the proposed model can effectively guide individuals to choose cooperation strategies which are beneficial to task completion and stability under different negative feedback factor values and different group sizes, so as to improve the group intelligence level.

Purpose: Collaboration is a common phenomenon in human society. The best way of collaborations can make the group achieve the best interests. Because of the low cost and high repeatability of simulation, it is a good method to explore the best way of collaborations by means of simulation. The traditional simulation is difficult to adapt to the crowd intelligence network simulation, so the crowd collaborations simulation is proposed. Design/methodology/approach: In this paper, the atomic swarm intelligence unit and collective swarm intelligence unit are proposed to represent the behavior of individuals and groups in physical space and the interaction between them. Findings: To explore the best collaboration mode of the group, a framework of crowd collaborations simulation is proposed, which decomposes the big goal into the small goals by constructing the cooperation chain and analyzes the cooperation results and feeds them back to the next simulation. Originality/value: Two kinds of swarm intelligence units are used to represent the simulated individuals in the group, and the pattern is used to represent individual behavior. It is suitable for the simulation of collaboration problems in various types and situations. © 2020, Rui Yang and Hongbo Sun.

Purpose: Evolution can be easily observed in nature world, and this phenomenon is a research hotspot no matter in natural science or social science. In crowd science and technology, evolutionary phenomenon exists also among many agents in crowd network systems. This kind of phenomenon is named as crowd co-evolutionary, which cannot be easily studied by most existing methods for its nonlinearity. This paper aims to proposes a novel simulation framework for co-evolution to discover improvements and behaviors of intelligent agents in crowd network systems. Design/methodology/approach: This paper introduces a novel simulation framework for crowd co-evolutions. There are three roles and one scene in the crowd. The scene represented by a band-right to a ringless diagram. The three roles are unit, advisor and monitor. Units find path in the scene. Advisors give advice to units. Monitors supervise units’ behavior in the scene. Building a network among these three kinds member, influencing individual relationships through information exchange, and finally enable the individual to find the optimal path in the scene. Findings: Through this simulation framework, one can record the behavior of an individual in a group, the reasons for the individual's behavior and the changes in the relationships of others in the group that cause the individual to do so. The speed at which an individual finds the optimal path can reflect the advantages and disadvantages of the relationship change function. Originality/value: The framework provides a new way to study the evolution of inter-individual relationships in crowd networks. This framework takes the first-person perspective of members of the crowd-sourced network as the starting point. Through this framework, the user can design relationship evolution methods and mathematical models for the members of different roles, so as to verify that the level of public intelligence of the crowd network is actually the essence of the rationality of the membership relationship. © 2020, Hongbo Sun and Kun Wang.

Purpose: The era of crowd network is coming and the research of its steady-state is of great importance. This paper aims to establish a crowd network simulation platform and maintaining the relative stability of multi-source dissemination systems. Design/methodology/approach: With this simulation platform, this paper studies the characteristics of "emergence," monitors the state of the system and according to the fixed point judges the system of steady-state conditions, then uses three control conditions and control methods to control the system status to acquire general rules for maintain the stability of multi-source information dissemination systems. Findings: This paper establishes a novel steady-state maintenance simulation framework. It will be useful for achieving controllability to the evolution of information dissemination and simulating the effectiveness of control conditions for multi-source information dissemination systems. Originality/value: This paper will help researchers to solve problems of public opinion control in multi-source information dissemination in crowd network. © 2020, Zhong Wang, Hongbo Sun and Baode Fan.

Purpose: Crowd network systems have been deemed as a promising mode of modern service industry and future economic society, and taking crowd network as the research object and exploring its operation mechanism and laws is of great significance for realizing the effective governance of the government and the rapid development of economy, avoiding social chaos and mutation. Because crowd network is a large-scale, dynamic and diversified online deep interconnection, its most results cannot be observed in real world, and it cannot be carried out in accordance with traditional way, simulation is of great importance to put forward related research. To solve above problems, this paper aims to propose a simulation architecture based on the characteristics of crowd network and to verify the feasibility of this architecture through a simulation example. Design/methodology/approach: This paper adopts a data-driven architecture by deeply analyzing existing large-scale simulation architectures and proposes a novel reflective memory-based architecture for crowd network simulations. In this paper, the architecture is analyzed from three aspects: implementation framework, functional architecture and implementation architecture. The proposed architecture adopts a general structure to decouple related work in a harmonious way and gets support for reflection storage by connecting to different devices via reflection memory card. Several toolkits for system implementation are designed and connected by data-driven files (DDF), and these XML files constitute a persistent storage layer. To improve the credibility of simulations, VV&A (verification, validation and accreditation) is introduced into the architecture to verify the accuracy of simulation system executions. Findings: Implementation framework introduces the scenes, methods and toolkits involved in the whole simulation architecture construction process. Functional architecture adopts a general structure to decouple related work in a harmonious way. In the implementation architecture, several toolkits for system implementation are designed, which are connected by DDF, and these XML files constitute a persistent storage layer. Crowd network simulations obtain the support of reflective memory by connecting the reflective memory cards on different devices and connect the interfaces of relevant simulation software to complete the corresponding function call. Meanwhile, to improve the credibility of simulations, VV&A is introduced into the architecture to verify the accuracy of simulation system executions. Originality/value: This paper proposes a novel reflective memory-based architecture for crowd network simulations. Reflective memory is adopted as share memory within given simulation execution in this architecture; communication efficiency and capability have greatly improved by this share memory-based architecture. This paper adopts a data-driven architecture; the architecture mainly relies on XML files to drive the entire simulation process, and XML files have strong readability and do not need special software to read. © 2020, Jialin Zou, Kun Wang and Hongbo Sun.