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Emergence is a common phenomenon, and it is also a general and important concept in complex dynamic systems like artificial societies. Usually, artificial societies are used for assisting in resolving several complex social issues (e.g., emergency management, intelligent transportation system) with the aid of computer science. The levels of an emer...
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... the initialization, all agents randomly live in the environment, which consists of 32 × 32 grids. Figure 9 presents the bird-flocking processes. In this case, we mainly concentrate on the levels of the emergence of flocking. ...
Context 2
... entropy H C (t) could be calculated with Equation (8), and H C_Max = -log 2 1/300 ≈ 5.044. Real-time metric E C (t) of flocking emergence could be shown in Figure 11, and Figure 9 also presents the corresponding E C (t) according to different flocking processes at various simulation ticks. In the Figure 11, we could see that metric E C (t) is increasing with some fluctuations. ...
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The quantitative assessment of the state and dynamics of a social system is a very difficult problem. This issue is important for both practical and theoretical reasons such as establishing the efficiency of social action programs, detecting possible community needs or allocating resources. In this paper we propose a new general theoretical framewo...
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... Indeed, in times of disorder or chaos, Entropy increases with decreasing order, and emergency increases with increasing order: this happens because people within the organisation understood the emergency, and the organisation improve themself to respond to it [39]. ...
Having a good reputation is paramount for most organisations and companies. In fact, having an optimal corporate image allows them to have better transaction relationships with various customers and partners. However, such reputation is hard to build and easy to destroy for all kind of business commercial activities (B2C, B2B, B2B2C, B2G). A misunderstanding during the communication process to the customers, or just a bad communication strategy, can lead to a disaster for the entire company. This is emphasised by the reaction of millions of people on social networks, which can be very detrimental for the corporate image if they react negatively to a certain event. This is called a firestorm. In this paper, I propose a well-organised strategy for firestorm attacks on organisations, also showing how an adversary can leverage them to obtain private information on the attacked firm. Standard business security procedures are not designed to operate against multi-domain attacks; therefore, I will show how it is possible to bypass the classic and advised security procedures by operating different kinds of attack. I also propose a different firestorm attack, targeting a specific business company network in an efficient way. Finally, I present defensive procedures to reduce the negative effect of firestorms on a company.
... Entropy rate, and the information structure about Hashtags on social networks can also describe cultural and socials dynamics, also adding information request coming from society. This data also suggests a strategic way to improve the diffusion of the information; the entropy rate of some information (especially institutional and political information and/or in emergency case [28]) should not be at the medium between HEE and LEE, but tactically it should be slightly above the average, so as to be compressible to many people, and at the same time, alluring, because the average information spread is more entropic. Also, Minkyoung et al. (2013) [29], have studied the dynamics of information diffusion across social networks, confirming this logic, showing topics with controversial subjects, which seem to lead to longer discussions representing personal opinions, like the HEEs case. ...
This paper aims to show how some popular topics on social networks can be used to predict online newspaper views, related to the topics. Newspapers site and many social networks, become a good source of data to analyse and explain complex phenomena. Understanding the entropy of a topic, could help all organizations that need to share information like government, institution, newspaper or company, to expect an higher activity over their channels, and in some cases predict what the receiver expect from the senders or what is wrong about the communication. For some organization such political party, leaders, company and many others, the reputation and the communication are (for most of them) the key part of a more and complex huge system. To reach our goal, we use gathering tools and information theory to detect and analyse trends topic on social networks, with the purpose of proved a method that helps organization, newspapers to predict how many articles or communication they will have to do on a topic, and how much flow of views they will have in a given period, starting with the entropy-article ratio. Our work address the issue to explore in which entropy-rate, and through which dynamics, a suitable information diffusion performance is expected on social network and then on newspaper. We have identified some cross-cutting dynamics that, associated with the contexts, might explain how people discuss about a topic, can move on to argue and informs on newspapers sites.
... Many computer researchers such as [37] have used entropy to identify and analyze the system order. Moreover, according to [39] many researchers, including [16,29,40,41] describe emergence (one of the self-organizing system's properties) as the system order's changes and use entropy for emergence measurement. ...
Nowadays, the study of self-organizing systems has attracted much attention. However, since these systems are run in dynamic, changing, and evolving environments, it is possible that undesirable behaviors that are contrary to the system goals occur. Therefore, it is necessary to provide mechanisms to guide the self-organizing system. However, several approaches were proposed to guide self-organizing systems, more effective approaches are required due to the variation of the contexts in which they are deployed and their complexity. This paper aims to use the self-advising property to provide guidelines about the context of self-organizing systems. The agents of these systems are guided implicitly by using the guidelines provided. In the proposed approach, contextual data is made by an advisor agent that produces them based on the agents’ behavioral entropy. The proposed approach is evaluated using a case study based on the NASA ANTS mission. According to experiments, the proposed approach causes adaptation activities’ costs to decrease at all radio ranges. Besides, in some radio ranges, i.e., 110 and 120 GHz, the guiding state’s adaptive time is less than the no-guiding state’s adaptive time. The evaluations also show that the ruler agents’ mean entropy in the guiding state is less than the no-guiding state in 75%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$75\%$$\end{document} of radio ranges. This approach’s success in reducing the agents’ entropy indicates its ability to guide self-organizing systems.
... The identification of emergence is central to this paper, therefore we need a measure expressing how strongly emergence occurs. Entropy is a metric to represent disorder in a complex dynamic system [12] [13]. There are several types of entropies proposed in various domains such as thermodynamic entropy, Boltzmann's entropy, social entropy, information entropy, generalized entropy, and so on. ...
... The micro-to-macro derivable tracks of weak emergences should be non-trivial, which can be amenable to computer simulations [33]. Tang and Mao [34] proposed a metrics based on information entropy to measure the emergences in an artificial society model (the zombie-city), which overcomes the diversity of artificial societies including the emergences of attribute, behavior and structure. Three metrics were proposed to denote the three kinds of emergences (attribution/behavior/structure) in artificial societies, and to quantitatively measure emergences. ...
The general idea of information entropy provided by C.E. Shannon "hangs over everything we do" and can be applied to a great variety of problems once the connection between a distribution and the quantities of interest is found. The Shannon information entropy essentially quantify the information of a quantity with its specific distribution, for which the information entropy based methods have been deeply developed in many scientific areas including physics. The dynamical properties of heavy-ion collisions (HICs) process make it difficult and complex to study the nuclear matter and its evolution, for which Shannon information entropy theory can provide new methods and observables to understand the physical phenomena both theoretically and experimentally. To better understand the processes of HICs, the main characteristics of typical models, including the quantum molecular dynamics models, thermodynamics models, and statistical models, etc., are briefly introduced. The typical applications of Shannon information theory in HICs are collected, which cover the chaotic behavior in branching process of hadron collisions, the liquid-gas phase transition in HICs, and the isobaric difference scaling phenomenon for intermediate mass fragments produced in HICs of neutron-rich systems. Even though the present applications in heavy-ion collision physics are still relatively simple, it would shed light on key questions we are seeking for. It is suggested to further develop the information entropy methods in nuclear reactions models, as well as to develop new analysis methods to study the properties of nuclear matters in HICs, especially the evolution of dynamics system.
... 1 For example, Tang studied the disease transmission by flocking birds in a study 2 and utilized information entropy-based metrics to measure the emergence of a flock of birds in Tang and Mao. 3 In 1986, Reynolds 4 created the first computer animation of flocking with three flocking rules, namely, cohesion, separation, and alignment. Under these rules, an agent in the flock attempts to stay close to nearby agents and avoid collision while matching velocity with nearby agents. ...
In this paper, we propose a distributed control approach for flocking and group maneuvering of nonholonomic agents, with constrained kinematic properties commonly found in practical systems, such as fixed-wing unmanned aerial vehicles. Flocking of agents with differential drive kinematics is addressed by introducing a virtual leader-follower mechanism into the Olfati-Saber's algorithm, which is originally proposed for holonomic agents with double integrator kinematics. Then, group maneuverability of the flock is achieved by superimposing a group motion onto each agent's flocking motion. Moreover, it is proven that speed limits are intrinsically guaranteed by the approach, which renders it more applicable in practical systems. Experimental results in MATLAB and Gazebo, a popular robotic simulator, are presented to evaluate the performance and demonstrate the effectiveness of the proposed approach.
... I. Giagkiozis et al. proposed a new method that can took advantage of the knowledge of cross entropy to solve the problem of multi-object programming [19]. Tang M and Mao X researched information entropy-based metrics for measuring emergences in artificial societies [20]. ...
Information entropy and its extension, which are important generalization of entropy, have been applied in many research domains today. In this paper, a novel generalized relative entropy is constructed to avoid some defects of traditional relative entropy. We presented the structure of generalized relative entropy after the discussion of defects in relative entropy. Moreover, some properties of the provided generalized relative entropy is presented and proved. The provided generalized relative entropy is proved to have a finite range and is a finite distance metric.
Emergence and causality are two fundamental concepts for understanding complex systems. They are interconnected. On one hand, emergence refers to the phenomenon where macroscopic properties cannot be solely attributed to the cause of individual properties. On the other hand, causality can exhibit emergence, meaning that new causal laws may arise as we increase the level of abstraction. Causal emergence (CE) theory aims to bridge these two concepts and even employs measures of causality to quantify emergence. This paper provides a comprehensive review of recent advancements in quantitative theories and applications of CE. It focuses on two primary challenges: quantifying CE and identifying it from data. The latter task requires the integration of machine learning and neural network techniques, establishing a significant link between causal emergence and machine learning. We highlight two problem categories: CE with machine learning and CE for machine learning, both of which emphasize the crucial role of effective information (EI) as a measure of causal emergence. The final section of this review explores potential applications and provides insights into future perspectives.
Information entropy can be used for investigating the critical fluctuation phenomenon by describing multiplicity fluctuations in heavy-ion collisions. In this study, a multiphase transport (AMPT) model in the default version (AMPT-DEF) and with the string melting (AMPT-SM) were used as event generators to simulate the Au+Au reaction at different incident energies. Owing to the influence of the distribution bin number in extracting its information entropy, two methods, i.e., data binning and weighting factors, were proposed to correct the information entropy analysis. The comparison between information entropy and kurtosis also reflects the necessity to correct the information entropy analysis. The corrected information entropy of net-proton multiplicity was analyzed in central Au+Au collisions at sNN= 4.7, 6.4, 7.7, 8.8, 11.5, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 140, 170, and 200 GeV with the impact parameter b=3 fm. The results indicated that the fluctuations of net-proton multiplicity were essentially identical at collision energies ranging from sNN= 4.7 GeV to sNN= 200 GeV in the same model. No critical fluctuation phenomenon was found in the central Au+Au collisions simulated using the AMPT models, similar to the case of the AMPT model, which does not have. Comparing the information entropy of AMPT-SM to AMPT-DEF, the effect of partonic phase leads to a bigger fluctuation, indicating that the corrected information entropy can be a useful probe to explore the phase transition. The mid-rapidity net-proton multiplicity information entropy was also calculated, and the results led to the same conclusion. The corrected information entropy may be useful for investigating critical fluctuations, which is important for exploring critical endpoint.
