Multi-agent Collaborative Model Research Articles

Multi-agent collaborative model of e-waste recycling

E-waste production is exploding as a result of rising demand for electronics and a faster rate of replacement. A crucial, yet unresolved issue is how to encourage cooperation among primary agents in the recycling of e-waste. To examine a multi-agent collaborative model of e-waste recycling among local governments, producers, and consumers, this research employs the techniques of evolutionary games and system dynamics. Evolutionary games are used to analyze the strategy choice results, while system dynamics are adopted to depict the decision-making processes. Overall, we find that rewards and penalties affect the stability and efficiency of the collaborative model of e-waste recycling and that the greater the correlation between the rewards-and-penalties mechanism and the strategies, the more stable the model is. Specifically, the results show that: (1) static rewards-and-penalties mechanism is likely to breed mutants and thus is not conducive to stable cooperation; (2) dynamic rewards-and-penalties mechanism can inhibit the instability of e-waste recycling model, but only 60% of producers are willing to build an e-waste recycling system; (3) dynamic rewards-and-penalties optimizing mechanism can ensure that producers and consumers wholeheartedly work for e-waste recycling consistently without local governments regulation; (4) according to the sensitivity analysis, increasing the amount of rewards and penalties can facilitate faster evolution of collaboration in the e-waste recycling system. Finally, this study makes implications for promoting efficiency and stability in e-waste recycling among local governments, producers, and consumers.

Collaborative Satellite-Terrestrial Edge Computing Network for Everyone-Centric Customized Services

The collaborative Satellite-Terrestrial network plays an important role in the future 6G mobile communication network, which can realize the global ubiquitous connection and support everyonecentric customized services. Taking into account the multi-layer heterogeneity of the Satellite-Terrestrial network, Mobile Edge Computing (MEC) technology can provide users with support for various data business computing services. Most of the existing satellite edge computing regards satellites as relay networks and neglects the deployment of MEC servers on satellites. We first propose a collaborative Satellite-Terrestrial edge computing network that deploys edge servers on LEO satellites. Then, we introduce the collaborative Satellite-Terrestrial network architecture and design a central-edge multiagent collaboration model. Furthermore, it integrates SDN and NFV to achieve optimal control for the collaborative network. Finally, extensive simulations show that the proposed strategy that jointly tasks offloading and resource allocation can achieve higher performance with low complexity.

Health-Related Rumor Control through Social Collaboration Models: Lessons from Cases in China during the COVID-19 Pandemic.

Widely spread health-related rumors may mislead the public, escalate social panic, compromise government credibility, and threaten public health. Social collaboration models that maximize the functions and advantages of various agents of socialization can be a promising way to control health-related rumors. Existing research on health-related rumors, however, is limited in studying how various agents collaborate with each other to debunk rumors. This study utilizes content analysis to code the text data of health-related rumor cases in China during the COVID-19 pandemic. The study found that socialized rumor-debunking models could be divided into the following five categories: the government-led model, the media-led model, the scientific community-led model, the rumor-debunking platform-led model, and the multi-agent collaborative model. In addition, since rumors in public health crises often involve different objects, rumor refutation requires various information sources; therefore, different rumor-debunking models apply. This study verifies the value of socialized collaborative rumor debunking, advocates and encourages the participation of multiple agents of socialization and provides guidance for establishing a collaborative rumor-debunking model, thereby promoting efficient rumor-debunking methods and improving the healthcare of society.

A Multi-Agent Collaborative Model for Bayesian Opportunistic Channel Accessibility in Railway Cognitive Radio

A Multi-Agent Collaborative Model for Bayesian Opportunistic Channel Accessibility in Railway Cognitive Radio

Managing security risks for inter-organisational information systems: a multiagent collaborative model

Information sharing across organisations is critical to effectively managing the security risks of inter-organisational information systems. Nevertheless, few previous studies on information systems security have focused on inter-organisational information sharing, and none have studied the sharing of inferred beliefs versus factual observations. In this article, a multiagent collaborative model (MACM) is proposed as a practical solution to assess the risk level of each allied organisation’s information system and support proactive security treatment by sharing beliefs on event probabilities as well as factual observations. In MACM, for each allied organisation’s information system, we design four types of agents: inspection agent, analysis agent, control agent, and communication agent. By sharing soft findings (beliefs) in addition to hard findings (factual observations) among the organisations, each organisation’s analysis agent is capable of dynamically predicting its security risk level using a Bayesian network. A real-world implementation illustrates how our model can be used to manage security risks in distributed information systems and that sharing soft findings leads to lower expected loss from security risks.