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53
An introduction to collective intelligence
 Handbook of Agent technology. AAAI
, 1999
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Nonequilibrium critical phenomena and phase transitions into absorbing states
 ADVANCES IN PHYSICS
, 2000
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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.
Comparative evaluation of microscopic carfollowing behavior
 IEEE Transactions on Intelligent Transportation Systems
, 2005
"... Abstract—Microscopic trafficsimulation tools are increasingly being applied to evaluate the impacts of a wide variety of intelligent transport systems (ITS) applications and other dynamic problems that are difficult to solve using traditional analytical models. The accuracy of a trafficsimulation ..."
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Abstract—Microscopic trafficsimulation tools are increasingly being applied to evaluate the impacts of a wide variety of intelligent transport systems (ITS) applications and other dynamic problems that are difficult to solve using traditional analytical models. The accuracy of a trafficsimulation system depends highly on the quality of the trafficflow model at its core, with the two main critical components being the carfollowing and lanechanging models. This paper presents findings from a comparative evaluation of carfollowing behavior in a number of traffic simulators [advanced interactive microscopic simulator for urban and nonurban networks (AIMSUN), parallel microscopic simulation (PARAMICS), and Verkehr in Stadten—simulation (VISSIM)]. The carfollowing algorithms used in these simulators have been developed from a variety of theoretical backgrounds and are reported to have been calibrated on a number of different data sets. Very few independent studies have attempted to evaluate the performance of the underlying algorithms based on the same data set. The results reported in this study are based on a carfollowing experiment that used instrumented vehicles to record the speed and relative distance between follower and leader vehicles on a onelane road. The experiment was replicated in each tool and the simulated carfollowing behavior was compared to the field data using a number of error tests. The results showed lower error values for the Gippsbased models implemented in AIMSUN and similar error values for the psychophysical spacing models used in VISSIM and PARAMICS. A qualitative “drift and goalseeking behavior ” test, which essentially shows how the distance headway between leader and follower vehicles should oscillate around a stable distance, also confirmed the findings. Index Terms—Carfollowing models, microscopic traffic simulation. I.
A survey of collectives
 IN COLLECTIVES AND THE DESIGN OF COMPLEX SYSTEMS
, 2004
"... Due to the increasing sophistication and miniaturization of computational components, complex, distributed systems of interacting agents are becoming ubiquitous. Such systems, where each agent aims to optimize its own performance, but where there is a welldefined set of systemlevel performance cr ..."
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Cited by 28 (12 self)
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Due to the increasing sophistication and miniaturization of computational components, complex, distributed systems of interacting agents are becoming ubiquitous. Such systems, where each agent aims to optimize its own performance, but where there is a welldefined set of systemlevel performance criteria, are called collectives. The fundamental problem in analyzing/designing such systems is in determining how the combined actions of a large number of agents leads to “coordinated ” behavior on the global scale. Examples of artificial systems which exhibit such behavior include packet routing across a data network, control of an array of communication satellites, coordination of multiple rovers, and dynamic job scheduling across a distributed computer grid. Examples of natural systems include ecosystems, economies, and the organelles within a living cell. No current scientific discipline provides a thorough understanding of the relation between the structure of collectives and how well they meet their overall performance criteria. Although still very young, research on collectives has resulted in successes both in understanding and designing such systems. It is expected that as it matures and draws upon other disciplines related to collectives, this field will greatly expand the range of computationally addressable tasks. Moreover, in addition to drawing on them, such a fully developed field of collective intelligence may provide insight into already established scientific fields, such as mechanism design, economics, game theory, and population biology. This chapter provides a survey to the emerging science of collectives.
Modeling and Simulation of Large Biological, Information and SocioTechnical Systems: An Interaction Based Approach
 Interactive Computation: The New
, 2005
"... Summary We describe an interaction based approach for computer modeling and simulation of large integrated biological, information, social and technical (BIST) systems 1 Examples of such systems are urban regional transportation systems, the national electrical power markets and grids, gene regulato ..."
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Cited by 27 (12 self)
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Summary We describe an interaction based approach for computer modeling and simulation of large integrated biological, information, social and technical (BIST) systems 1 Examples of such systems are urban regional transportation systems, the national electrical power markets and grids, gene regulatory networks, the worldwide Internet, infectious diseases, vaccine design and deployment, theater war, etc. These systems are composed of large numbers of interacting human, physical, informational and technological components. These components adapt and learn, exhibit perception, interpretation, reasoning, deception, cooperation and noncooperation, and have economic motives as well as the usual physical properties of interaction. The theoretical foundation of our approach consists of two parts: (i) mathematics of complex interdependent dynamic networks, and (ii) mathematical and computational theory of a class of finite discrete dynamical systems called Sequential Dynamical Systems (SDSs). We then consider engineering principles based on such a theory. As with the theoretical foundation, they consist of two basic parts: (i) Efficient data manipulation, including synthesis, integration, storage and regeneration and (ii) high performance computing oriented system design, development and implementation. The engineering methods allow us to specify, design, and analyze simulations of extremely large systems and implement them on massively parallel architectures. As an illustration of our approach, an interaction based computer modeling and simulation framework to study very large interdependent societal infrastructures is described. 1
The asymmetric exclusion process: Comparison of update procedures
 J. Stat. Phys
, 1998
"... Abstract The asymmetric exclusion process (ASEP) has attracted a lot of interest not only because its many applications, e.g. in the context of the kinetics of biopolymerization and traffic flow theory, but also because it is a paradigmatic model for nonequilibrium systems. Here we study the ASEP fo ..."
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Cited by 26 (1 self)
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Abstract The asymmetric exclusion process (ASEP) has attracted a lot of interest not only because its many applications, e.g. in the context of the kinetics of biopolymerization and traffic flow theory, but also because it is a paradigmatic model for nonequilibrium systems. Here we study the ASEP for different types of updates, namely randomsequential, sequential, sublatticeparallel and parallel. In order to compare the effects of the different update procedures on the properties of the stationary state, we use largescale Monte Carlo simulations and analytical methods, especially the socalled matrixproduct Ansatz (MPA). We present in detail the exact solution for the model with sublatticeparallel and sequential updates using the MPA. For the case of parallel update, which is important for applications like traffic flow theory, we determine the phase diagram, the current, and density profiles based on Monte Carlo simulations. We furthermore suggest a MPA for that case and derive the corresponding matrix algebra. Key Words: Asymmetric exclusion process; boundaryinduced phase transitions; steady state; matrix product Ansatz; discretetime updates
A pedestrian’s view on interacting particle systems, KPZ universality, and random matrices
, 2010
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The ergodic theory of traffic jams
 Journal of Statistical Physics
, 2001
"... We introduce and analyze a simple probabilistic cellular automaton which emulates the flow of cars along a highway. Our Traffic CA captures the essential features of several more complicated algorithms, studied numerically by ..."
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Cited by 20 (0 self)
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We introduce and analyze a simple probabilistic cellular automaton which emulates the flow of cars along a highway. Our Traffic CA captures the essential features of several more complicated algorithms, studied numerically by
Cellular Automata for Traffic Flow Modeling
"... In this paper, we explore the usefulness of cellular automata to traffic flow modeling. We extend some of the existing CA models to capture characteristics of traffic flow that have not been possible to model using either conventional analytical models or existing simulation techniques. In particula ..."
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In this paper, we explore the usefulness of cellular automata to traffic flow modeling. We extend some of the existing CA models to capture characteristics of traffic flow that have not been possible to model using either conventional analytical models or existing simulation techniques. In particular, we examine higher moments of traffic flow and evaluate their effect on overall traffic performance. The behavior of these higher moments is found to be surprising, somewhat counterintuitive, and to have important important implications for design and control of traffic systems. For example, we show that the density of maximum throughput is near the density of maximum speed variance. Contrary to current practice, traffic should, therefore, be steered away from this density region. For deterministic systems, we found traffic flow to possess a finite period which is highly sensitive to density in a nonmonotonic fashion. We show that knowledge of this periodic behavior to be very useful in ...