esball国际平台客户端复杂系统科学中心(C3S2)

esball国际平台客户端C3S2

在短暂的, complex systems science involves the study of how many elements develop behaviors that are beyond those behaviors possible by considering the individual elements alone. While the behavior of each individual component of a system in isolation may support intricate dynamics, together the individual components interact to support group behaviors and system dynamics well beyond those possible from individual components alone.

Complex systems science is a rapidly growing and emerging field that is inherently interdisciplinary. It can be applied to a wide variety of fields including biology; medicine and cognitive science; mechanical, 化学, electrical and civil engineering; physics and astronomy; economics and social sciences. The future of research in these fields lies in understanding not just the isolated components of a given system, but the manner in which the individual components interact to produce emergent group behavior.

与数据挖掘或大数据相比, in which a primary focus is to understand hidden patterns or structure in large data sets, complex systems science attempts to identify causality and uncover the universality that exists in large-scale systems. 因果关系和普遍性是由于同伴和等级的相互作用, 单个系统组件的模式和缩放. Universality has been observed across a wide range of fields such as brain science, 昆虫聚集, 社会科学与流体动力学.

Key to the advancement of complex systems science is the development and use of mathematical tools designed to understand the resultant outcome of group behaviors that are not evident when studying the behavior individual elements alone. Mathematical tools for complex systems science are drawn from the following fields:

• Information dynamics — the study of interaction of elements and the information flow between elements. Of particular interest is the minimum information needed to produce an outcome of important behaviors. 
• Algorithmic complexity — In contrast to information dynamics and entropy of evolving systems is the concept of algorithmic complexity, Komolgorov复杂性和最小描述的概念, as a contrast that intricate behavior is often opposite to simplicity of design.
• Structure and dynamics on networks — a large number of interacting parts can give rise to behaviors that emerge from the group interactions and are not implicit in any one element. 考虑蚁群的集体行为和能力, 从零件的行为来看，这显然是无法理解的. 考虑网络引入了图论的数学, but well beyond this when understanding dynamics on networks comes complexity theory.
• 随机网络的临界性和尺度建模, the implications of critical phenomena to complexity and the recent approaches to evolutionary dynamics are all part of this field. 像这样, understanding interactions from food webs to economies all have a universality that can be understood in terms of the science that includes hierarchical interactions. It is the characterization of such universalities that lead to complex systems as a unifying field across such disciplines.
• Technical details and the tool-sets — includes areas of dynamical systems and chaos theory, 网络理论和图论, 信息理论, 热力学和统计力学, 元胞自动机, 信息理论, 包括玻璃在内的活化过程, 分形, 缩放和重整化.