Although the multi-agent model has been used to analyze several economic and management problems, and the research results are regarded more profoundly, they all rely on certain scenarios. Once the scenarios are shifted to an unknown one, the results cannot be matched. In this paper, a new research method named exploratory computational experiment is introduced to resolve the problems coming from the social complex system, where individual's behaviors are irrational, diverse, and complex, and collective behavior is dynamical, complex, and critical. Firstly, the foundation of the computational experiment is introduced, then several important problems, how individuals make the decision under complex environment, how collective behavior have emerged when different conflicts co-exist, and how to evaluate collect behaviors, are analyzed. To specify this new method, two examples of how to design a scientific mechanism to make the traffic system more effective and how is the evolution law of giant components in scale-free networks if the parameters are changed continuously. The results show that multi-agent modeling based on irrational behaviors controlled by individual dynamical game radius and memory length limited can describe the social problem more accurately, the exploratory computational experiment can give us more profound conclusions.
This paper constructs a non-cooperative/cooperative stochastic differential game model to prove that the optimal strategies trajectory of agents in a system with a topological configuration of a Multi-Local-World graph would converge into a certain attractor if the system's configuration is fixed. Due to the economics and management property, almost all systems are divided into several independent Local-Worlds, and the interaction between agents in the system is more complex. The interaction between agents in the same Local-World is defined as a stochastic differential cooperative game; conversely, the interaction between agents in different Local-Worlds is defined as a stochastic differential non-cooperative game. We construct a non-cooperative/cooperative stochastic differential game model to describe the interaction between agents. The solutions of the cooperative and noncooperative games are obtained by invoking corresponding theories, and then a nonlinear operator is constructed to couple these two solutions together. At last, the optimal strategies trajectory of agents in the system is proven to converge into a certain atractor, which means that strategies trajectory are certainty as time tends to infinity or a large positive integer. It is concluded that the optimal strategy trajectory with a nonlinear operator of cooperative/noncooperative stochastic differential game between agents can make agents in a certain Local-World coordinate and make the Local-World payment maximize, and can make the all Local-Worlds equilibrated; furthermore, the optimal strategy of the coupled game can converge into a particular attractor that decides the optimal property.
This paper introduces a method of nearest neighbor phase space reconstruction based on symbolic dynamics to study the causality between urbanization and economic growth at different regional levels in Shandong Province, and finds that there is the strong positive relationship between urbanization and economic growth in Shandong Province, indicating that the development of urbanization can drive the economic growth. Then, according to the results of correlation analysis and the principle of “deleting strong and reserving weak”, the paper selects the explanatory variable and explained variable to explore the hidden causal mechanisms that drive the development of urbanization in Shandong Province. The results show that: 1) the pattern causality between the explanatory variable and the explained variable is dominated by dark causality at the provincial level; 2) the pattern causality between the explanatory variable and the explained variable is dominated by dark causality at the Jinan economic circles and the Lunan economic circles, but the positive causality is dominated at the Jiaodong economic circles; 3) the types of causality between the same evaluation index and PU in different regions are different, and furthermore the degrees of positive, negative and dark causality are different at two levels and three regions. The conclusion shows that although there is an obvious positive interaction between urbanization and economic growth, the influences of many factors are neither positive nor negative causality, but dark causality. This study provides important support for accurately grasping the driving mechanism of urbanization development and economic growth.
Credit problems are the main bottleneck in the development of e-commerce. Both the time and degree of e-commerce credit control directly affect its economic benets. From the perspective of the criticality of complex systems, this paper explores the control node of e-commerce credit behaviour. Implementing control in this node can not only ensure the stable development of the credit network but also optimise the control cost. This paper constructs a credit behaviour model for the four regulatory behaviours of the transaction subject, analyses the evolution law of the credit network, and determines the critical state of the network. Finally, the paper’s empirical analysis and simulation experiments prove that when the false information in the credit network in the empirical data is 32%, the implementation of credit control has an ecient control effect. Nevertheless, 32% is not a universal result; the specific critical point value needs to be recalculated according to the theoretical derivation of the critical point combined with the actual network. These research results can help regulators obtain the highest regulatory return with the lowest regulatory investment.
e nite-time pinning synchronization control problem is studied for coupled complex networks with time-varying delays. Based on the nite-time stability theorem, a nite-time tractive synchronous controller is designed. In addition, the selection process of tractive nodes is developed to control as few nodes as possible such that all nodes are synchronized in the network in nite time. At the same time, sucient conditions of the nite-time constraint synchronization of the drive-response network are obtained using the Lyapunov stability theory and the matrix inequality method. e eectiveness of the proposed controller is veri ed by numerical simulation. is approach can be applied to large-scale complex networks with time-varying delays.
Complex adaptive system with Agent’s behavior and Agent’s local topological configuration co-evolved has several dynamical Local-Worlds changing due to Agents’ behavior, the optimal strategy in a stable system structure and the invariable distribution of the optimal strategies in a dynamical system structure are the most important to master the property of the system. To draw a conclusion about this, a stochastic differential game, the cooperative stochastic differential game between agents in a same Local-World and non-cooperative stochastic differential game between agents in different Local-Worlds be included, is constructed to describe Agents’ behavior in short time-scale, then a Markov process, whose state consists Agent’s behavior and Agent’s local graphic topology configuration, 6 sub-processes coupled with 6 different behaviors included, is constructed to describe the interaction property in dynamical case. After resolve the optimal strategy in the short time-scale, for arbitrary configuration, by coupling the Nash equilibrium solution of non-cooperative game and Pareto optimal strategy of cooperative game by a non-linear operator, the maximum payoff coupled with the optimal strategy in short time-scale should be introduced to design the preferential attachment mechanism and growth mechanism. Then, the invariable distribution of the system can be obtained by invoking the method of stochastic process. It is conclude that: (1) in the short time-scale, the optimal strategy of arbitrary agents can be determined by considering the character of the cooperative/non-cooperative stochastic differential game, furthermore, the optimal strategy of coupled game can converge into a certain attractor that decide the optimal property in this time-scale. (2) in the long time-scale, agent decides its owner behavior according to the evolution way of random complex networks that driven by preferential attachment and volatility mechanism with its payment, which makes this complex adaptive system evolve, the corresponding invariant distribution can be determined by agents’ behaviors, system’s topology configuration, the noise of agent’s behavior and the system population; when the behavior noise tend to 0 the invariant distribution can converge into a certain rate function, however, when the population ten to infinity, the invariant distribution can converge into another ratio function, so, small noise of behavior is not identical to large population as far as this system is considered.
A random Multi-Local-World complex networks directed model is constructed to mine the evolution laws of topological properties for this complex engineering system according to the rules about what the behaviors operate in complex system is gotten by calculating the avalanche-size distribution. It concludes that: It is the robustness and vulnerability that behaviors show at the same moment in complex system, when facing random attacking and intention attacking respectively.
Separating the management system into several Local-Worlds, the multi-players cooperation game and noncooperation game in a graph are constructed to describe the interaction between agents in a certain Local-World and between different Local-Worlds respectively. Furthermore, the corresponding analytic processes that consist of two different game theory models are specified, then the profits, the optimal decision of agent, in this system with cooperative stochastic differential game model between Agents and non-cooperative stochastic differential game model between Super-Agents considered simultaneously, are given.
Purpose – The purpose of this paper is to improve the behaviors coordination mechanism, to maintain the system’s long time-scale and stable competitive capability, when the agents in the system focus on cooperating with each other.
Design/methodology/approach – Effort level for every agent, whose dynamics can be described as a stochastic partial differential equation, and the incentive of effort as the control of the corresponding agent, are introduced to describe agents’ behavior abstracted. The cooperative stochastic differential game model is constructed: first, the optimal resolve trajectory mapping with profit maximization of the system are obtained, then the transitory imputation coupled with effort initial state of the system by introducing dynamic Shapley value imputation method. Based on the results obtained, the profit distribution strategies and the equilibration incentive compensation mechanism are given, due to the evolution law of the payoff and the state variable.
Findings – It is concluded that: the transitory compensation to agent for efforts and incentive, which can be changed with the system state at current and in history and in future changed, would guarantee the realization of the Shapley value imputation throughout the game horizon.
Originality/value – In this paper, the interactivity between agents in the system is considered first. The dynamical Shapley imputation mechanism and the transitory compensatory mechanism are provided to make the imputation more stable and feasible.