Dynamic Cooperative Multi-Agent Model of Complex System Based-on Sequential Actions’ Logic

Jiang, Weijin; Zhong, Luo; Zhang, LianMei; Shi, Dongcheng

doi:10.3724/sp.j.1016.2013.01115

Cited by 7 publications

(6 citation statements)

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“…Family relocation behavior is mainly determined by family satisfaction; there is a negative correlation between the two under certain conditions. Family relocation Sustainability 2020, 12, 2070 5 of 15 behavior leads to a dynamic change in population density, per capita safety facilities, and safe livability, resulting in a multi-factor system that affects urban safety livability changes over time and cycles [14,15]. The interactive feedback of safe livability, resident satisfaction, and family relocation behavior in the system is shown in Figure 2.…”

Section: Model Overviewmentioning

confidence: 99%

“…The interactive feedback of safe livability, resident satisfaction, and family relocation behavior in the system is shown in Figure 2. and safe livability, resulting in a multi-factor system that affects urban safety livability changes over time and cycles [14,15]. The interactive feedback of safe livability, resident satisfaction, and family relocation behavior in the system is shown in Figure 2.…”

Section: Model Overviewmentioning

confidence: 99%

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Multi-Agent Simulation of Safe Livability and Sustainable Development in Cities

Pan

Yang

et al. 2020

Sustainability

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Urban livability is an important factor affecting the sustainable development of modern cities. Safe livability is an important part of urban livability. In view of this, this paper takes security as an example, and based on the actual data of Futian District, Shenzhen City, China, establishes a multi-agent simulation model of urban safe livability. The dynamic interaction feedback mechanism between decision-making behaviors of residents and urban safe livability under the influence of environment and policies has been explored, and residents' decision-making simulation of the change of urban safe livability is realized. Finally, the main factors influencing urban safe livability are summarized through simulation conclusions. The research can not only provide scientific suggestions for improving the safe livability of Shenzhen, it also provides strong support for the sustainable development of the city.Sustainability 2020, 12, 2070 2 of 15 detector model. Liu and co-workers explored the complex interdependence between emerging tourism and the livability of Chinese cities. Based on the conceptual model and statistical analysis of 35 large-and medium-sized cities in China from 2003 to 2012, the potential threat of overdevelopment of tourism to the livability of the city was deduced [4]. Paul and colleagues took Kolkata, India as the research object, and based their study on integrated urban geographic factors (IUGFs), evaluating the changes to livability in the city center to understand the impact of changes in livability on urban development [5]. Khorasani and Zarghamfard [6] analyzed the influence of spatial factors at local and regional scales on livability, using the urban village of Walamin as an example. The results showed that there was a significant relationship between village spatial factors and livability indicators. Furlan and Petruccioli [7] proposed a model called transit oriented development (TOD). This model improves urban livability through better integration of urban transportation and land use strategies, thereby maintaining sustainable urbanization. Most of the studies in the above literature only consider the meaning and measurement indicators of a more comprehensive livable city. They are limited to the concept and interpretation of the concept. They do not consider changes in the livability of the city under the influence of various factors. They only focus on static spatial equilibrium and the estimation of urban livability under quantitative indicators. Moreover, they ignore complex human factors and individual decision-making behaviors, and it is difficult to reflect the complexity of urban spatial systems. Therefore, in our study, computer technology is used to study the interaction between many environmental factors and complex human factors. Moreover, computer technology is of great significance to the study of urban safe livability.Multi-agent modeling [8] refers to abstracting the basic elements of complex systems into agents. Then, an agent simulation model corresponding to the real...

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Section: Model Overviewmentioning

confidence: 99%

Section: Model Overviewmentioning

confidence: 99%

Multi-Agent Simulation of Safe Livability and Sustainable Development in Cities

Pan

Yang

et al. 2020

Sustainability

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“…Sociology believes that trust is an aspect that all social relations will contain, which indicates the expectations of the future. It is more concerned about the behavioral interaction between individuals, emphasizing the impact of social interaction on trust [12][13][14]. And psychology has studied the trust from the psychology and behavior [15].…”

Section: Research On Trust and Distrust In Different Disciplinesmentioning

confidence: 99%

A Network Celebrity Identification and Evaluation Model Based on Hybrid Trust Relation

Jiang

Chen

2018

Teh. vjesn.

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Trust-based celebrity user identification is the key to the industry's reputation for electronic word of mouth. However, trust and mistrust are independent and coexistent concepts. In this context, we need to consider the existence of the two kinds of user relations brought about by the impact. This paper analyzes the characteristics of trust and distrust in social networks, and gives formal descriptions of trust networks, untrusted networks, and mixed trust networks. Based on the indicators such as degree distribution, correlation coefficient, and matching coefficient, the structural properties of mixed trust networks are studied. Based on the PageRank algorithm, the HTMM metrics affecting users under the mixed trust network environment are proposed. Finally, the validity of HTMM is verified through a real data set containing trust and distrust. Experimental results show that the results of HTMM's celebrity user identification method still have a low level of trust.

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“…For users trust score weighting Cr [．], this study proposes two methods, namely, user trust calculation methods and calculation method based on collaborative filtering [13].…”

Section: Ratings User Trust Weight Calculation Methodsmentioning

confidence: 99%

Multi Agent System-Based Dynamic Trust Calculation Model and Credit Management Mechanism of Online Trading

Jiang

Guo

et al. 2015

Human Centered Computing

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Abstract. At present, all kinds of malicious acts appear in C2C online auctions, particularly the phenomenon of trust lack and credit fraud is very outstanding. Therefore, how to build an effective trust model has become a burning problem. Based on analyzing limitations of the existing online trust transaction mechanism, and according to characteristics (such as dynamic, innominate and suppositional) of online transaction trust problem, the article proposes a dynamic trust calculation model and reputation management mechanism of online trading based on multi-Agent system.. The model consists of three parts. The first part is the trust of user domain, to put importance on the influence on current trust by recent credibility status, to motivate users to adopt a agreed cooperative strategy. The second part is the weighted average of reputation feedback score, The weighted part mainly considers the trust from the reputation feedback score person (the credibility of the feedback score), the value of the transaction (to prevent the "credit squeeze"), temporal discounted ("guard against the fluctuations of the credibility") and other factors; the third part is to give a weighting on the community contribution, according to the action taken by a user to the other members of the community in a time domain, to increase or decrease the user's trust to isolate the feedback submission of the credibility and punish the fraud. The paper builds the fraud limition mechanism which combine the prevention beforehand, coordination in the event and punishement afterwards. The mechanism makes the online transaction safe. Theoretic proof and experimental verification indicate the following three problems can be solved effectively: 1) solving the problem which is difficult to prevent and is that peculative user accumulates the little trusts and squeeze on the large trading; 2) preventing members from cheating by false trading or personation; 3) reducing the arbitration workload of the online business platform.

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Dynamic Cooperative Multi-Agent Model of Complex System Based-on Sequential Actions’ Logic

Cited by 7 publications

References 0 publications

Multi-Agent Simulation of Safe Livability and Sustainable Development in Cities

Multi-Agent Simulation of Safe Livability and Sustainable Development in Cities

A Network Celebrity Identification and Evaluation Model Based on Hybrid Trust Relation

Multi Agent System-Based Dynamic Trust Calculation Model and Credit Management Mechanism of Online Trading

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