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205
Complex networks: Structure and dynamics
, 2006
"... Coupled biological and chemical systems, neural networks, social interacting species, the Internet and the World Wide Web, are only a few examples of systems composed by a large number of highly interconnected dynamical units. The first approach to capture the global properties of such systems is t ..."
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Cited by 431 (9 self)
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Coupled biological and chemical systems, neural networks, social interacting species, the Internet and the World Wide Web, are only a few examples of systems composed by a large number of highly interconnected dynamical units. The first approach to capture the global properties of such systems is to model them as graphs whose nodes represent the dynamical units, and whose links stand for the interactions between them. On the one hand, scientists have to cope with structural issues, such as characterizing the topology of a complex wiring architecture, revealing the unifying principles that are at the basis of real networks, and developing models to mimic the growth of a network and reproduce its structural properties. On the other hand, many relevant questions arise when studying complex networks ’ dynamics, such as learning how a large ensemble of dynamical systems that interact through a complex wiring topology can behave collectively. We review the major concepts and results recently achieved in the study of the structure and dynamics of complex networks, and summarize the relevant applications of these ideas in many different disciplines,
Traffic and related selfdriven manyparticle systems
, 2000
"... Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ‘‘phantom traffic jams’ ’ even though drivers all like to drive fast? ..."
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Cited by 336 (38 self)
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Since the subject of traffic dynamics has captured the interest of physicists, many surprising effects have been revealed and explained. Some of the questions now understood are the following: Why are vehicles sometimes stopped by ‘‘phantom traffic jams’ ’ even though drivers all like to drive fast? What are the mechanisms behind stopandgo traffic? Why are there several different kinds of congestion, and how are they related? Why do most traffic jams occur considerably before the road capacity is reached? Can a temporary reduction in the volume of traffic cause a lasting traffic jam? Under which conditions can speed limits speed up traffic? Why do pedestrians moving in opposite directions normally organize into lanes, while similar systems ‘‘freeze by heating’’? All of these questions have been answered by applying and extending methods from statistical physics and nonlinear dynamics to selfdriven manyparticle systems. This article considers the empirical data and then reviews the main approaches to modeling pedestrian and vehicle traffic. These include microscopic (particlebased), mesoscopic (gaskinetic), and macroscopic (fluiddynamic) models. Attention is also paid to the formulation of a micromacro link, to aspects of universality, and to other unifying concepts, such as a general modeling framework for selfdriven manyparticle systems, including spin systems. While the primary focus is upon vehicle and pedestrian traffic, applications to biological or socioeconomic systems such as bacterial colonies, flocks of birds, panics, and stock market dynamics are touched upon as well.
Algorithms for Generic Role Assignment in Wireless Sensor Networks
 in SenSys ’05: Proceedings of the 3rd international conference on Embedded networked sensor systems
, 2005
"... We consider configuration of wireless sensor networks, where certain functions must be automatically assigned to sensor nodes, such that the properties of a sensor node (e.g., remaining energy, network neighbors) match the requirements of the assigned function. Essentially, sensor nodes take on cert ..."
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Cited by 55 (4 self)
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We consider configuration of wireless sensor networks, where certain functions must be automatically assigned to sensor nodes, such that the properties of a sensor node (e.g., remaining energy, network neighbors) match the requirements of the assigned function. Essentially, sensor nodes take on certain roles in the network as a result of configuration. To help developers with such configuration tasks for a variety of applications, we propose generic role assignment as a programming abstraction, where roles and rules for their assignment can be easily specified using a configuration language. We present such a role specification language and distributed algorithms for role assignment according to such specifications. We evaluate our approach and show that efficient and robust generic role assignment is practically feasible for wireless sensor networks.
Cellular Automata and Lattice Boltzmann Techniques: An Approach to Model and Simulate Complex Systems
 ADVANCES IN PHYSICS, SUBMITTED
, 1998
"... We discuss the cellular automata approach and its extensions, the lattice Boltzmann and multiparticle methods. The potential of these techniques is demonstrated in the case of modeling complex systems. In particular, we consider applications taken from various fields of physics, such as reactiondi ..."
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Cited by 42 (3 self)
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We discuss the cellular automata approach and its extensions, the lattice Boltzmann and multiparticle methods. The potential of these techniques is demonstrated in the case of modeling complex systems. In particular, we consider applications taken from various fields of physics, such as reactiondiffusion systems, pattern formation phenomena, fluid flows, fracture processes and road traffic models.
Hierarchical Learning with Procedural Abstraction Mechanisms
, 1997
"... Evolutionary computation (EC) consists of the design and analysis of probabilistic algorithms inspired by the principles of natural selection and variation. Genetic Programming (GP) is one subfield of EC that emphasizes desirable features such as the use of procedural representations, the capability ..."
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Cited by 37 (2 self)
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Evolutionary computation (EC) consists of the design and analysis of probabilistic algorithms inspired by the principles of natural selection and variation. Genetic Programming (GP) is one subfield of EC that emphasizes desirable features such as the use of procedural representations, the capability to discover and exploit intrinsic characteristics of the application domain, and the flexibility to adapt the shape and complexity of learned models. Approaches that learn monolithic representations are considerably less likely to be effective for complex problems, and standard GP is no exception. The main goal of this dissertation is to extend GP capabilities with automatic mechanisms to cope with problems of increasing complexity. Humans succeed here by skillfully using hierarchical decomposition and abstraction mechanisms. The translation of such mechanisms into a general computer implementation is a tremendous challenge, which requires a firm understanding of the interplay between repr...
The world as a process: Simulations in the natural and social sciences
 Simulation and Modeling in the Social Sciences from the Philosophy of Science Point of View, Theory and Decision Library
, 1996
"... Simulation techniques, especially those implemented on a computer, are frequently employed in natural as well as in social sciences with considerable success. There is mounting evidence that the “modelbuilding era ” (J. Niehans) that dominated the theoretical activities of the sciences for a long t ..."
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Cited by 36 (3 self)
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Simulation techniques, especially those implemented on a computer, are frequently employed in natural as well as in social sciences with considerable success. There is mounting evidence that the “modelbuilding era ” (J. Niehans) that dominated the theoretical activities of the sciences for a long time is about to be succeeded or at least lastingly supplemented by the “simulation era”. But what exactly are models? What is a simulation and what is the difference and the relation between a model and a simulation? These are some of the questions addressed in this article. I maintain that the most significant feature of a simulation is that it allows scientists to imitate one process by another process. “Process ” here refers solely to a temporal sequence of states of a system. Given the observation that processes are dealt with by all sorts of scientists, it is apparent that simulations prove to be a powerful interdisciplinarily acknowledged tool. Accordingly, simulations are best suited to investigate the various research strategies in different sciences more carefully. To this end, I focus on the function of simulations in the research process. Finally, a somewhat detailed casestudy from nuclear physics is presented which, in my view, illustrates elements of a typical simulation in physics. ∗I wish to thank H. Carteret, P. Humphreys, F. Rohrlich, K. Troitzsch and M. Weber for valuable comments on a draft of this paper and M. Stöckler for many helpful discussions and support. S. Wolfram provided me with his most recent writings on cellular automata. Thanks! A slightly revised version of this
CRYSTALLINE COMPUTATION
 CHAPTER 18 OF FEYNMAN AND COMPUTATION (A. HEY, ED.)
, 1999
"... Discrete lattice systems have had a long and productive history in physics. Examples range from exact theoretical models studied in statistical mechanics to approximate numerical treatments of continuum models. There has, however, been relatively little attention paid to exact lattice models which o ..."
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Cited by 36 (8 self)
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Discrete lattice systems have had a long and productive history in physics. Examples range from exact theoretical models studied in statistical mechanics to approximate numerical treatments of continuum models. There has, however, been relatively little attention paid to exact lattice models which obey an invertible dynamics: from any state of the dynamical system you can infer the previous state. This kind of microscopic reversibility is an important property of all microscopic physical dynamics. Invertible lattice systems become even more physically realistic if we impose locality of interaction and exact conservation laws. In fact, some invertible and momentum conserving lattice dynamics—in which discrete particles hop between neighboring lattice sites at discrete times—accurately reproduce hydrodynamics in the macroscopic limit. These kinds of discrete systems not only provide an intriguing informationdynamics approach to modeling macroscopic physics, but they may also be supremely practical. Exactly the same properties that make these models physically realistic also make them efficiently realizable. Algorithms that incorporate constraints such as locality of interaction and invertibility can be run on microscopic physical hardware that shares these constraints. Such hardware can, in principle, achieve a higher density and rate of computation than any other kind of computer. Thus it is interesting to construct discrete lattice dynamics which are more physicslike both in order to capture more of the richness of physical dynamics in informational models, and in order to improve our ability to harness physics for computation. In this chapter, we discuss techniques for bringing discrete lattice dynamics closer to physics, and some of the interesting consequences of doing so.
A Survey on Cellular Automata
, 2003
"... A cellular automaton is a decentralized computing model providing an excellent platform for performing complex computation with the help of only local information. Researchers, scientists and practitioners from different fields have exploited the CA paradigm of local information, decentralized contr ..."
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Cited by 35 (0 self)
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A cellular automaton is a decentralized computing model providing an excellent platform for performing complex computation with the help of only local information. Researchers, scientists and practitioners from different fields have exploited the CA paradigm of local information, decentralized control and universal computation for modeling different applications. This article provides a survey of available literature of some of the methodologies employed by researchers to utilize cellular automata for modeling purposes. The survey introduces the different types of cellular automata being used for modeling and the analytical methods used to predict its global behavior from its local configurations. It further gives a detailed sketch of the efforts undertaken to configure the local settings of CA from a given global situation; the problem which has been traditionally termed as the inverse problem. Finally, it presents the different fields in which CA have been applied. The extensive bibliography provided with the article will be of help to the new entrant as well as researchers working in this field.
Generic Role Assignment for Wireless Sensor Networks
 In Proceedings of the 11th ACM SIGOPS European Workshop
, 2004
"... Wireless ad hoc networks of sensor nodes are envisioned to be deployed in the physical environment to monitor a wide variety of realworld phenomena. Almost any sensor network application requires some form of selfconfiguration, where sensor nodes take on specific functions or roles in the network w ..."
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Cited by 34 (8 self)
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Wireless ad hoc networks of sensor nodes are envisioned to be deployed in the physical environment to monitor a wide variety of realworld phenomena. Almost any sensor network application requires some form of selfconfiguration, where sensor nodes take on specific functions or roles in the network without manual intervention. These roles may be based on varying sensor node properties (e.g., available sensors, location, network neighbors) and may be used to support applications requiring heterogeneous node functionality (e.g., clustering, data aggregation). In this paper we argue that the assignment of userdefined roles is a fundamental part of a wide range of sensor network applications. Consequently, a framework for assignment of roles to sensor nodes in an applicationspecific manner could significantly ease sensor network programming. We outline the general structure of such a framework and present a first approach to its realization. We demonstrate its utility and feasibility using a number of concrete examples.
A General Methodology for Mathematical Analysis of MultiAgent Systems
 USC Information Sciences
, 2001
"... We propose a general mathematical methodology for studying the dynamics of multiagent systems in which complex collective behavior arises out of local interactions between many simple agents. The mathematical model is composed of a system of coupled differential equations describing the macroscop ..."
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Cited by 34 (4 self)
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We propose a general mathematical methodology for studying the dynamics of multiagent systems in which complex collective behavior arises out of local interactions between many simple agents. The mathematical model is composed of a system of coupled differential equations describing the macroscopic, or collective, dynamics of an agentbased system. We illustrate our approach by applying it to analyze several agentbased systems, including coalition formation in an electronic marketplace, and foraging and collaboration in a group of robots. 1.