Cascading failure prevention of complex systems based on brittleness entropy game

A non-cooperative game model based on brittleness entropy is formulated for preventing cascading failure of complex systems.Subsystems of a complex system are mapped to the players of the game.The influence of collapsed subsystems to other subsystems is also taken into account in the definition of payoff function except for their own entropy increase.This influence is named brittleness entropy.Each player has two optional strategies;rational for negative entropy and irrational for negative entropy.The model is designed to identify the players who select an irrational strategy for negative entropy.The players who select the irrational strategy for negative entropy continue to compete for negative entropy after the recovery of ordered state and make other subsystems can' t get enough negative entropy to reduce entropy increase.It leads to cascading failure of the complex system in the end.Genetic algorithm is used to seek the solution of game model,and the simulation result verifies the effectiveness of the proposed model.The model provides a new way to prevent cascading failure of complex systems.

Complex systems and network science:a survey

Complex systems widely exist in nature and human society.There are complex interactions between system elements in a complex system,and systems show complex features at the macro level,such as emergence,self-organization,uncertainty,and dynamics.These complex features make it difficult to understand the internal operation mechanism of complex systems.Networked modeling of complex systems is a favorable means of understanding complex systems.It not only represents complex interactions but also reflects essential attributes of complex systems.This paper summarizes the research progress of complex systems modeling and analysis from the perspective of network science,including networked modeling,vital node analysis,network invulnerability analysis,network disintegration analysis,resilience analysis,complex network link prediction,and the attacker-defender game in complex networks.In addition,this paper presents some points of view on the trend and focus of future research on network analysis of complex systems.

Load-Balance Mechanism for WSNs Based on Minority Game with Dormancy Strategy

Load-balance is an important issue in wireless sensor networks (WSNs), especially in WSNs with hierarchical structure. Energy consumed unevenly will bring the production of hot spots. Hot spot will cause WSNs to divide some unconnected sub-networks and shorten the lifetime of WSNs. To tackle this problem, a load-balance mechanism is proposed based on minority game (MG) with dormancy strategy. This mechanism can cause the rich behaviors of cooperation , prolong lifetime of WSNs, and keep energy consumed evenly. This dormancy mechanism can save energy of nodes by keeping in sleep temperately . Simulation results show that the proposed strategy can efficiently enhance the lifetime of cluster and the lifetime of whole WSNs.

Some Remarks About Financial Market Modelling Using a Minority Game Approach

The methods adopted by static physics corroborating the existence of electromagnetic forces are applicable to the theory of financial markets. Perceived from a classically physical angle, the financial market is defined as a system composed of several individual entries cooperating upon electromagnetic principles. The approach concerned gives rise to a certain model of financial market, otherwise known as a minority game. In the case of minority game, the allocation of securities and funds is conditioned exclusively upon the fluctuation of prices, where a higher tendency to purchase goods and stocks results in the scale being more profitable and vice versa. Thus players from a minority group gain a prevailing position.

Self organizing optimization and phase transition in reinforcement learning minority game system

Whether the complex game system composed of a large number of artificial intelligence(AI)agents empowered with reinforcement learning can produce extremely favorable collective behaviors just through the way of agent self-exploration is a matter of practical importance.In this paper,we address this question by combining the typical theoretical model of resource allocation system,the minority game model,with reinforcement learning.Each individual participating in the game is set to have a certain degree of intelligence based on reinforcement learning algorithm.In particular,we demonstrate that as AI agents gradually becomes familiar with the unknown environment and tries to provide optimal actions to maximize payoff,the whole system continues to approach the optimal state under certain parameter combinations,herding is effectively suppressed by an oscillating collective behavior which is a self-organizing pattern without any external interference.An interesting phenomenon is that a first-order phase transition is revealed based on some numerical results in our multi-agents system with reinforcement learning.In order to further understand the dynamic behavior of agent learning,we define and analyze the conversion path of belief mode,and find that the self-organizing condensation of belief modes appeared for the given trial and error rates in the AI system.Finally,we provide a detection method for period-two oscillation collective pattern emergence based on the Kullback–Leibler divergence and give the parameter position where the period-two appears.

Recent advances on micro-control for near-critical complex systems

In this paper,we review some existing control methodologies for complex systems with particular emphasis on those that are near critical.Due to the shortage of the classical control theory in handling complex systems,the reviewed control methods are mainly associated with machine learning techniques,game-theoretical approaches,and sparse control strategies.Additionally,several interesting and promising directions for future research are also proposed.

Ethnic Minority Games Come To Guizhou

Ateam of barefoot young peoplet ake to the water on 7.5-meter-long bamboo poles and maintain their stability with ease as they glide across the Hongfeng Lake in southwest China’s Guizhou Province.But unlike local merchants who long agou sed large pieces of bamboo as makeshift floats to get their wares to market,thesey oung people are professional,full-timea thletes in the ethnic minority sport of single bamboo drifting.

Chaotic-periodic transition in a two-sided minority game

Phase transitions are being used increasingly to probe the collective behaviors of social human systems. In this study, we propose a different way of investigating such transitions in a human system by establishing a two-sided minority game model. A new type of agents who can actively transfer resources are added to our artificial bipartite resource-allocation market. The degree of deviation from equilibria is characterized by the entropy-like quantity of market complexity. Under different threshold values, Qth, two phases are found by calculating the exponents of the associated power spectra. For large values of Qth, the general motion of strategies for the agents is relatively periodic whereas for low values of Qth, the motion becomes chaotic. The transition occurs abruptly at a critical value of Qty. Our simulation results were also tested based on human experiments. The results of this study suggest that a chaotic-periodic transition related to the quantity of market information should exist in most bipartite markets, thereby allowing better control of such a transi- tion and providing a better understanding of the endogenous emergence of business cycles from the perspective of quantum mechanics.

Diffusion mechanism simulation of cloud manufacturing complex network based on cooperative game theory

Cloud manufacturing is a specific implementation form of the "Internet + manufacturing" strategy. Why and how to develop cloud manufacturing platform(CMP), however, remains the key concern of both platform operators and users. A microscopic model is proposed to investigate advantages and diffusion forces of CMP through exploration of its diffusion process and mechanism. Specifically, a three-stage basic evolution process of CMP is innovatively proposed. Then, based on this basic process, a more complex CMP evolution model has been established in virtue of complex network theory, with five diffusion forces identified. Thereafter, simulations on CMP diffusion have been conducted. The results indicate that, CMP possesses better resource utilization,user satisfaction, and enterprise utility. Results of simulation on impacts of different diffusion forces show that both the time required for CMP to reach an equilibrium state and the final network size are affected simultaneously by the five diffusion forces. All these analyses indicate that CMP could create an open online cooperation environment and turns out to be an effective implementation of the "Internet + manufacturing" strategy.

The mechanism for the self-adaptation behavior in the evolutionary minority game model

In this paper, we shall present our studies of a generalized evolutionary minority game model in which the agents are divided into several groups. The performance of the individual agent is averaged in each group. We find that there are three different effects in this generalized model, i.e.(1) group averaging effect, (2) left-right asymmetric effect, and (3) self-interaction effect. The former two effects favor the cautious agents, while the last one favors the extreme agents. In most cases, both the analytic results and the numericul simulations demonstrate that the group averaging effect is dominantly important and therefore the performance of the cautious agents is better than that of the extreme agents. However, when the number of groups is sufficiently large, the generalized model can be somehow reduced to the conventional evolutionary minority game model. As the parameters vary in the generalized model, the importance of the above three effects is exchangeable and different types of population distribution emerge.

System dynamics of behaviour-evolutionary mix-game models

In real financial markets there are two kinds of traders： one is fundamentalist, and the other is a trend-follower. The mix-game model is proposed to mimic such phenomena. In a mix-game model there are two groups of agents： Group 1 plays the majority game and Group 2 plays the minority game. In this paper, we investigate such a case that some traders in real financial markets could change their investment behaviours by assigning the evolutionary abilities to agents： if the winning rates of agents are smaller than a threshold, they will join the other group; and agents will repeat such an evolution at certain time intervals. Through the simulations, we obtain the following findings： （i） the volatilities of systems increase with the increase of the number of agents in Group 1 and the times of behavioural changes of all agents; （ii） the performances of agents in both groups and the stabilities of systems become better if all agents take more time to observe their new investment behaviours; （iii） there are two-phase zones of market and non-market and two-phase zones of evolution and non-evolution; （iv） parameter configurations located within the cross areas between the zones of markets and the zones of evolution are suited for simulating the financial markets.

Cooperation influenced by the correlation degree of two-layered complex networks in evolutionary prisoner’s dilemma games

An evolutionary prisoner＇s dilemma game is investigated on two-layered complex networks respectively representing interaction and learning networks in one and two dimensions. A parameter q is introduced to denote the correlation degree between the two-layered networks. Using Monte Carlo simulations we studied the effects of the correlation degree on cooperative behaviour and found that the cooperator density nontrivially changes with q for different payoff parameter values depending on the detailed strategy updating and network dimension. An explanation for the obtained results is provided.

Modeling Financial Market with Minority Game

There are two main paradigms in financial study： one is classical theories such as CAPM, APT and EMH, the other is Behavioral Finance. However, both sides have certain disadvantages： The former emphasizes rationality and equilibrium while neglects the effect of investors＇ psychology and interaction on price evolution; the latter focuses on investors＇ irrationality and heterogeneity, whereas denies equilibrium and systematism. So we need a comprehensive and realistic model which simultaneously consists of heterogeneous agents, dynamical interaction and evolutionary equilibrium. The Minority Game was first introduced by physicists and is a powerful and rather simple tool dealing with the problem how the equilibrium could be dynamically attained under such circumstance as heterogeneous agents interacting with each other. In this paper, we attempt to apply this inter-discipline theory to model financial markets.

Exploring Biocomplexity in Cancer: A Comprehensive Review

Living objects have complex internal and external interactions. The complexity is regulated and controlled by homeostasis, which is the balance of multiple opposing influences. The environmental effects finally guide the self-organized structure. The living systems are open, dynamic structures performing random, stationary, stochastic, self-organizing processes. The self-organizing procedure is defined by the spatial-temporal fractal structure, which is self-similar both in space and time. The system’s complexity appears in its energetics, which tries the most efficient use of the available energies;for that, it organizes various well-connected networks. The controller of environmental relations is the Darwinian selection on a long-time scale. The energetics optimize the healthy processes tuned to the highest efficacy and minimal loss (minimalization of the entropy production). The organism is built up by morphogenetic rules and develops various networks from the genetic level to the organism. The networks have intensive crosstalk and form a balance in the Nash equilibrium, which is the homeostatic state in healthy conditions. Homeostasis may be described as a Nash equilibrium, which ensures energy distribution in a “democratic” way regarding the functions of the parts in the complete system. Cancer radically changes the network system in the organism. Cancer is a network disease. Deviation from healthy networking appears at every level, from genetic (molecular) to cells, tissues, organs, and organisms. The strong proliferation of malignant tissue is the origin of most of the life-threatening processes. The weak side of cancer development is the change of complex information networking in the system, being vulnerable to immune attacks. Cancer cells are masters of adaptation and evade immune surveillance. This hiding process can be broken by electromagnetic nonionizing radiation, for which the malignant structure has no adaptation strategy. Our objective is to review the different sides of living complexity and use the knowledge to fight against cancer.

Evolutionary snowdrift game on heterogeneous Newman-Watts small-world network

We study the evolutionary snowdrift game in a heterogeneous Newman-Watts small-world network. The heterogeneity of the network is controlled by the number of hubs. It is found that the moderate heterogeneity of the network can promote the cooperation best. Besides, we study how the hubs affect the evolution of cooperative behaviours of the heterogeneous Newman-Watts small-world network. Simulation results show that both the initial states of hubs and the connections between hubs can play an important role. Our work gives a further insight into the effect of hubs on the heterogeneous networks.

Strategic games on a hierarchical network model

Among complex network models,the hierarchical network model is the one most close to such real networks as world trade web,metabolic network,WWW,actor network,and so on.It has not only the property of power-law degree distribution,but also the scaling clustering coefficient property which Barabási-Albert(BA)model does not have.BA model is a model of network growth based on growth and preferential attachment,showing the scale-free degree distribution property.In this paper,we study the evolution of cooperation on a hierarchical network model,adopting the prisoner's dilemma(PD)game and snowdrift game(SG)as metaphors of the interplay between connected nodes.BA model provides a unifying framework for the emergence of cooperation.But interestingly,we found that on hierarchical model,there is no sign of cooperation for PD game,while the fre-quency of cooperation decreases as the common benefit decreases for SG.By comparing the scaling clustering coefficient prop-erties of the hierarchical network model with that of BA model,we found that the former amplifies the effect of hubs.Considering different performances of PD game and SG on complex network,we also found that common benefit leads to cooperation in the evolution.Thus our study may shed light on the emergence of cooperation in both natural and social environments.

Simulation of Life Game Based on Cellular Automata

Vs2010 is used as the development environment, so as to realize the visual programming of Game of Life, and explore the life evolution process of cell group in different sizes and states. According to cell forms such as circulation and disappearance, it reflects the complex changes of Game of Life. Setting different initial states through the code and observing the final generated graphics, you can see that the complex and simple initial states can achieve the same result. It is also concluded that a suitable initial state can reach the final state in fewer steps, which can greatly simplify the evolution process. The entire system is completely closed and has certain limitations. Meanwhile, the evolution process of symmetrical initial state is also symmetrically distributed. I introduce random quantities into the system to make the simulation results closer to the actual situation. By setting a random initial state to make the chaotic and disorderly situation simple, the concept of “determinism and randomness” can be better expressed. In the process of change, some local structures remain fixed, and some local structures present periodic cycles. These structures interact in complex ways to understand the concept of “whole and part”. The game of life enlightens us: the simplest logical rules can produce complex and interesting activities, and a complex system may be iterated by simple rules.

Memory-Based Boolean Game and Self-Organized Phenomena on Networks

We study a memory-based Boolean game （MBBG） taking place on a regular ring, wherein each agent acts according to its local optimal states of the last M time steps recorded in memory, and the agents in the minority are rewarded. One free parameter p between 0 and 1 is introduced to denote the strength of the agent willing to make a decision according to its memory. It is found that giving proper willing strength p, the MBBG system can spontaneously evolve to a state of performance better than the random game; while for larger p, the herd behaviour emerges to reduce the system profit. By analysing the dependence of dynamics of the system on the memory capacity M, we find that a higher memory capacity favours the emergence of the better performance state, and effectively restrains the herd behaviour, thus increases the system profit. Considering the high cost of long-time memory, the enhancement of memory capacity for restraining the herd behaviour is also discussed, and M =5 is suggested to be a good choice.

Dynamic behaviours of mix-game model and its application

In this paper a minority game （MG） is modified by adding into it some agents who play a majority game. Such a game is referred to as a mix-game. The highlight of this model is that the two groups of agents in the mix-game have different bounded abilities to deal with historical information and to count their own performance. Through simulations, it is found that the local volatilities change a lot by adding some agents who play the majority game into the MG, and the change of local volatilities greatly depends on different combinations of historical memories of the two groups. Furthermore, the analyses of the underlying mechanisms for this finding are made. The applications of mix-game mode are also given as an example.

Agile Scheduling for MES in a Complex Information Environment

Since Manufacturing Execution System (MES) is a bridge which links the upper planning system of the enterprise and the control system of the shop floor, various kinds of the information with different characteristics flow through the system. The information environment of MES and its effect on MES scheduling are analyzed. A methodological proposal is given to address the problem of agile scheduling in a complex information environment, based on which a microeconomic market and game theoretic model-based scheduling approach is presented. The future development of this method is also discussed.