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Agent Based Modeling, Large Scale Simulations

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Computational Complexity

Article Outline

Glossary

Definition of the Subject

Introduction

Large Scale Agent Based Models: Guidelines for Development

Parallel Computing

Example

Future Directions

Acknowledgments

Bibliography

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Abbreviations

Agent:

A popular definition of an agent, particularly in AI research, is that of Wooldridge [45], pp. 29: “an agent is a computer system that is situated in some environment, and that is capable of autonomous action in this environment in order to meet its design objectives”. In particular, it is the autonomy, flexibility, inter-agent communication, reactivity and proactiveness of the agents that distinguishes the paradigm and gives power to agent-based models and multi-agent simulation [15,21]. Multi-agent systems (MAS) comprise of numerous agents, which are given rules by which they act and interact with one another to achieve a set of goals.

Block mapping:

A method of partitioning an array of elements between nodes of a distributed system, where the array elements are partitioned as evenly as possible into blocks of consecutive elements and assigned to processors. The size of the blocks approximates to the number of array elements divided by the number of processors.

Complexity:

Complexity and complex systems pertain to ideas of randomness and irregularity in a system, where individual-scale interactions may result in either very complex or surprisingly simple patterns of behavior at the larger scale. Complex agent-based systems are therefore usually made up of agents interacting in a non-linear fashion. The agents are capable of generating emergent behavioral patterns, of deciding between rules and of relying upon data across a variety of scales. The concept allows for studies of interaction between hierarchical levels rather than fixed levels of analysis.

Cyclic mapping:

A method of partitioning an array of elements between nodes of a distributed system, where the array elements are partitioned by cycling through each node and assigning individual elements of the array to each node in turn.

Grid:

Computer ‘Grids’ are comprised of a large number of disparate computers (often desktop PCs) that are treated as a virtual cluster when linked to one another via a distributed communication infrastructure (such as the internet or an intranet). Grids facilitate sharing of computing, application, data and storage resources. Grid computing crosses geographic and institutional boundaries, lacks central control, and is dynamic as nodes are added or removed in an unco-ordinated manner. BOINC computing is a form of distributed computing is where idle time on CPUs may be used to process information (http://boinc.berkeley.edu/).

Ising-type model:

Ising-type models have been primarily used in the physical sciences. They simulate behavior in which individual elements (e. g., atoms, animals, social behavior, etc.)modify their behavior so as to conform to the behavior of other individuals in their vicinity.Conway's Game of Life is a Ising-type model, where cells are in one of two states: dead or alive.In biology, the technique is used to model neural networks and flocking birds, for example.

Message passing (MP):

Message passing (MP) is the principle way by which parallel clusters of machines are programmed. It is a widely-used, powerful and general method of enabling distribution and creating efficient programs [30]. Key advantages of using MP architectures are an ability to scale to many processors, flexibility, ‘future-proofing’ of programs and portability [29].

Message passing interface (MPI):

A computing standard that is used for programming parallel systems. It is implemented as a library of code that may be used to enable message passing in a parallel computing system. Such libraries have largely been developed in C and Fortran, but are also used with other languages such as Java (MPIJava http://www.hpjava.org). It enables developers of parallel software to write parallel programs that are both portable and efficient.

Multiple instruction multiple data (MIMD):

Parallelization where different algorithms are applied to different data items on different processors.

Parallel computer architecture:

A parallel computer architecture consists of a number of identical units that contain CPUs (Central Processing Units) which function as ordinary serial computers. These units, called nodes, are connected to one another (Fig. 1). They may transfer information and data between one another (e. g. via MPI) and simultaneously perform calculations on different data.

Single instruction multiple data (SIMD):

SIMD techniques exploit data level parallelism: when a large mass of data of a uniform type needs the same instruction performed on it. An example is a vector or array processor. An application that may take advantage of SIMD is one where the same value is being added (or subtracted) to a large number of data points.

Vector computer/vector processor:

Vector computers contain a CPU designed to run mathematical operations on multiple data elements simultaneously (rather than sequentially). This form of processing is essentially a SIMD approach.The Cray Y-MP and the Convex C3880 are two examples of vector processors used for supercomputing in the 1980s and 1990s. Today, most recent commodity CPU designs include some vector processing instructions.

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Acknowledgments

Many thanks to Andrew Evans (Multi-Agent Systems and Simulation Research Group, University of Leeds, UK) and Phil Northing (Central ScienceLaboratory, UK) for their advice on this chapter.

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  1. Central Science Laboratory, York, UK

    Hazel R. Parry

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  1. RAMTECH LIMITED, 122 Escalle Lane, Larkspur, CA, 94939, USA

    Robert A. Meyers Ph. D. (Editor-in-Chief) (Editor-in-Chief)

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Parry, H.R. (2012). Agent Based Modeling, Large Scale Simulations. In: Meyers, R. (eds) Computational Complexity. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1800-9_5

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