Results 1  10
of
19
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 ..."
Abstract

Cited by 24 (3 self)
 Add to MetaCart
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.
Scale Invariance in Biology: Coincidence Or Footprint of a Universal Mechanism?
, 2001
"... In this article, we present a selfcontained review of recent work on complex biological systems which exhibit no characteristic scale. This property can manifest itself with fractals (spatial scale invariance), flicker noise or 1}fnoise where f denotes the frequency of a signal (temporal scale i ..."
Abstract

Cited by 23 (1 self)
 Add to MetaCart
In this article, we present a selfcontained review of recent work on complex biological systems which exhibit no characteristic scale. This property can manifest itself with fractals (spatial scale invariance), flicker noise or 1}fnoise where f denotes the frequency of a signal (temporal scale invariance) and power laws (scale invariance in the size and duration of events in the dynamics of the system). A hypothesis recently put forward to explain these scalefree phenomomena is criticality, a notion introduced by physicists while studying phase transitions in materials, where systems spontaneously arrange themselves in an unstable manner similar, for instance, to a row of dominoes. Here, we review in a critical manner work which investigates to what extent this idea can be generalized to biology. More precisely, we start with a brief introduction to the concepts of absence of characteristic scale (powerlaw distributions, fractals and 1}fnoise) and of critical phenomena. We then review typical mathematical models exhibiting such properties : edge of chaos, cellular automata and selforganized critical models. These notions are then brought together to see to what extent they can account for the scale invariance observed in ecology, evolution of species, type III epidemics and some aspects of the central nervous system. This article also discusses how the notion of scale invariance can give important insights into the workings of biological systems.
SelfOrganization in PeertoPeer Systems
 In Proceedings of the 2002 SIGOPS European Workshop, St. Emilion
, 2002
"... This paper addresses the problem of forming groups in peertopeer (P2P) systems and examines what dependability means in decentralized distributed systems. Much of the literature in this field assumes that the participants form a local picture of global state, yet little research has been done disc ..."
Abstract

Cited by 23 (2 self)
 Add to MetaCart
This paper addresses the problem of forming groups in peertopeer (P2P) systems and examines what dependability means in decentralized distributed systems. Much of the literature in this field assumes that the participants form a local picture of global state, yet little research has been done discussing how this state remains stable as nodes enter and leave the system. We assume that nodes remain in the system long enough to benefit from retaining state, but not sufficiently long that the dynamic nature of the problem can be ignored. We look at the components that describe a system's dependability and argue that nextgeneration decentralized systems must explicitly delineate the information dispersal mechanisms (e.g., probe, eventdriven, broadcast), the capabilities assumed about constituent nodes (bandwidth, uptime, reentry distributions), and distribution of information demands (needles in a haystack vs. hay in a haystack [13]). We evaluate two systems based on these criteria: Chord [22] and a heterogeneousnode hierarchical grouping scheme [11]. The former gives a failed request rate under normal P2P conditions and a prototype of the latter a similar rate under more strenuous conditions with an order of magnitude more organizational messages. This analysis suggests several methods to greatly improve the prototype.
Blackout mitigation assessment in power transmission systems
 36th Hawaii International Conference on System Sciences
, 2003
"... Electric power transmission systems are a key infrastructure and blackouts of these systems have major direct and indirect consequences on the economy and national security. Analysis of North American Electrical Reliability Council blackout data suggests the existence of blackout size distributions ..."
Abstract

Cited by 18 (9 self)
 Add to MetaCart
Electric power transmission systems are a key infrastructure and blackouts of these systems have major direct and indirect consequences on the economy and national security. Analysis of North American Electrical Reliability Council blackout data suggests the existence of blackout size distributions with power tails. This is an indication that blackout dynamics behave as a complex dynamical system. Here, we investigate how these complex system dynamics impact the assessment and mitigation of blackout risk. The mitigation of failures in complex systems needs to be approached with care. The mitigation efforts can move the system to a new dynamic equilibrium while remaining near criticality and preserving the power tails. Thus, while the absolute frequency of disruptions of all sizes may be reduced, the underlying forces can still cause the relative frequency of large disruptions to small disruptions to remain the same. Moreover, in some cases, efforts to mitigate small disruptions can even increase the frequency of large disruptions. This occurs because the large and small disruptions are not independent but are strongly coupled by the dynamics. 1.
An inverse cascade model for selforganized complexity and natural hazards
 J. Int
, 2004
"... natural hazards ..."
Evolution in Complex Systems
, 2004
"... What features characterize complex system dynamics? Power laws and scale invariance of fluctuations are often taken as the hallmarks of complexity, drawing on analogies with equilibrium critical phenomena. Here we argue that slow, directed dynamics, during which the system’s properties change signif ..."
Abstract

Cited by 2 (2 self)
 Add to MetaCart
What features characterize complex system dynamics? Power laws and scale invariance of fluctuations are often taken as the hallmarks of complexity, drawing on analogies with equilibrium critical phenomena. Here we argue that slow, directed dynamics, during which the system’s properties change significantly, is fundamental. The underlying dynamics is related to a slow, decelerating but spasmodic release of an intrinsic strain or tension. Time series of a number of appropriate observables can be analyzed to confirm this effect. The strain arises from local frustration. As the strain is released through “quakes, ” some system variable undergoes record statistics with accompanying logPoisson statistics for the quake event times. We demonstrate these phenomena via two very different systems: a model of magnetic relaxation in type II superconductors and the Tangled Nature model of evolutionary ecology and show how quantitative indications of aging can be found. © 2004 Wiley Periodicals, Inc. Complexity 10: 49–56, 2004 Key Words: complex dynamics; nonstationary measures; evolution Many macroscopic systems evolve through periods of relative quiescence separated by brief outbursts of hectic activity. We describe the prototype complex dynamics using two specific systems from physics and biology: the magnetic behavior of type II superconductors and biological macroevolution. Each system is metastable when observed on short time scales, whereas at long time scales, each evolves towards greater stability. The models were
Mean field frozen percolation
 Journal of Statistical Physics 137
"... We define a modification of the ErdősRényi random graph process which can be regarded as the mean field frozen percolation process. We describe the behavior of the process using differential equations and investigate their solutions in order to show the selforganized critical and extremum properti ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
We define a modification of the ErdősRényi random graph process which can be regarded as the mean field frozen percolation process. We describe the behavior of the process using differential equations and investigate their solutions in order to show the selforganized critical and extremum properties of the critical frozen percolation model. We prove two limit theorems about the distribution of the size of the component of a typical frozen vertex. 1
AN INITIAL COMPLEX SYSTEMS ANALYSIS OF THE RISKS OF BLACKOUTS IN POWER TRANSMISSION SYSTEMS
 POWER SYSTEMS AND COMMUNICATIONS INFRASTRUCTURES FOR THE FUTURE, BEIJING
, 2002
"... ..."
Combining global and multiscale features in a description of the solar windmagnetosphere coupling
, 2003
"... ..."
ANALYSIS OF EXTREMES IN MANAGEMENT STUDIES
"... The potential advantage of extreme value theory in modeling management phenomena is the central theme of this paper. The statistics of extremes have played only a very limited role in management studies despite the disproportionate emphasis on unusual events in the world of managers. An overview of ..."
Abstract
 Add to MetaCart
The potential advantage of extreme value theory in modeling management phenomena is the central theme of this paper. The statistics of extremes have played only a very limited role in management studies despite the disproportionate emphasis on unusual events in the world of managers. An overview of this theory and related statistical models is presented, and illustrative empirical examples provided. As I am sure almost every geophysicist knows, distributions of actual errors and fluctuations have much more straggling extreme values than would correspond to the magic bellshaped distribution of Gauss and Laplace.