外文翻译---复杂网络理论和基于代理的社会网络：一个梗概

外文翻译---复杂网络理论和基于代理的社会网络：一个梗概内容摘要：

ions, etc.) which is in most cases is localized, but leads to the emergence of patterns that would be hard to predict given the simple nature of the rulesofinteraction. Complexity, plex systems and all sorts of relevant terms have appeared extensively in the contemporary scientific and philosophical literature, eg Edmonds (2020) define plexity which can be used in the context of the 39。 level of difficulty39。 involved in modeling the behavior of a natural or artificial system. Definition: Complexity as defined by Edmonds（ 2020） “ Complexity is that property of a model which makes it difficult to formulate its overall behaviour in a given language, even when given reasonably plete information about its atomic ponents and their interrelations. ” Complex Networks Networks have been studied formally at least as early as from the 18 th century, where Euler pioneered the Graph Theory in his attempt to solve the famous K246。 nigsberg bridge problem. Graph theoretic concepts have been applied ever since in various disciplines where there are a set of entities linked by means of some relationship. Investigating the structural properties of such works has led to a whole range of analytic measures that form the basis of the modern graph theory. Application of such analysis tools in social sciences can date back to the start of the 20 th century (Scott, 1991), in which ever since, the discipline of social work analysis (SNA) has stemmed as a result of the research of works involving actors (individuals, anizations, etc.) and development of a metrics focusing on different aspects of social positions and interactions. We will discuss SNA in the Section 2. Typical Complex Networks and Characteristics We present only a brief overview of the typical plex work characteristics and their salient features. An exhaustive literature is available, especially ( Barab225。 si, 2020。 Watts， 2020）， provide a prehensive account of the works. Random Graph Theory One of the earliest attempts to study the behavior of these socalled 39。 plex works39。 dates back to the seminal work on random graph theory by Paul Erd246。 s and Alfr233。 d R233。 nyi, (the socalled ER model) in the 1950s. The basic ER model requires connecting N nodes through n edges chosen randomly such that the resulting work is from a space of graphs, each equally likely. Several nodes can have the same degree in a random 11 graph (large enough) which can be calculated. Given the probability of wiring p is not small, the diameter of random graphs is usually small and the diameter increases logarithmically, as a random graph evolves. Pareto Distribution and SelfOrganized Criticality The underlying assumption in using the statistical methods, in many situations, is that the mean and standard deviation of the distribution of the data are known and are stable. In many cases, it was found that the simulation results in generation of data that has a fattail and thinpeak。 a characteristic known as leptokurtosis . One of the most studied characteristics of the plex works is the appearance of the powerlaw distribution in many areas. Informally, it means that the most connected nodes in the work are relatively very few as pared to the lesser connected nodes (or vice versa).The socalled powerlaw(s) stem from the Pareto Distribution , a specialization of the Pareto Principle , named after Vifredo Pareto (Wikipedia, 2020). The probability density function of the distribution is defined as: The distribution is parameterized by two parameters: x m and k. As Barth233。 l233。 my (2020) discusses, if the shape (peakedness) parameter k  (0,2] has value k ≤ 1 , the mean is infinite。 while for k ≤ 2 the variance is finite. Figure 1 shows the thin peakedness, which is the characteristic of the distribution. Our purpose is here is about the implications and not presenting a discussion on the analytical properties of the distribution, which can be found in any standard text on stochastic distributions. Such characteristics provide cues for further investigation of the underlying model and the phenomenon it addresses. We will e back to this issue later in this article. Another implication identified by (ibid) was that “ the specification of utility maximising agents will not support an analysis of the properties of large, distributed systems if those systems are to mimic market systems as described by equilibrium economic theory ”. This further encourages in looking for statistical signatures in validating the relevant theories. The fact that higher, thinner peak of the frequency distribution with respect to the corresponding normal distribution, ie leptokurtosis, are observed in the series of data from quite a few social works (Moss, 2020。 Moss and Edmonds, 2020), provides analysis of such behavior a highly prospective candidate for a thorough investigation. Therefore, it is interesting for both policy designers and modelers to not only identify the causes of volatility and clustering of data, but to look for means to be able to predict (if possible) the phase transition, which may be regarded as policy change, change of political views, etc. SelfOrganized Criticality (SOC) maybe interpreted as the response of slowlydriven system such that the oute of the system39。 s behavior is limited by the magnitude of its size。 thus, leading to the scalefree property (discussed in the subsequent section). Following Jensen (1998), one may explain SOC as the development of emergent patterns due to the interactions among metastable agents, such that at some critical state, the result of interactions affects the entire system such that 39。 all members of the system influence each other39。 . Several properties of the systems exhibiting SOC have been reported. Moss and Edmonds (2020) 12 present these properties in the context of a number of cases involving a。