Gillespie algorithms for stochastic multiagent dynamics in populations and networks / Naoki Masuda, Christian L. Vestergaard.

Format:

Book

Author/Creator:

Masuda, Naoki, 1976- author.

Vestergaard, Christian L., author.

Series:

Cambridge elements. Elements in the structure and dynamics of complex networks,

Language:

English

Subjects (All):

Algorithms.

Stochastic processes.

Multiagent systems.

System analysis.

Physical Description:

1 online resource (96 pages) : digital, PDF file(s).

Edition:

First edition.

Place of Publication:

Cambridge : Cambridge University Press, 2022.

Summary:

Many multiagent dynamics can be modeled as a stochastic process in which the agents in the system change their state over time in interaction with each other. The Gillespie algorithms are popular algorithms that exactly simulate such stochastic multiagent dynamics when each state change is driven by a discrete event, the dynamics is defined in continuous time, and the stochastic law of event occurrence is governed by independent Poisson processes. The first main part of this volume provides a tutorial on the Gillespie algorithms focusing on simulation of social multiagent dynamics occurring in populations and networks. The authors clarify why one should use the continuous-time models and the Gillespie algorithms in many cases, instead of easier-to-understand discrete-time models. The remainder of the Element reviews recent extensions of the Gillespie algorithms aiming to add more reality to the model (i.e., non-Poissonian cases) or to speed up the simulations. This title is also available as open access on Cambridge Core.

Contents:

1. Introduction; 2. Preliminaries; 3. Classic Gillespie Algorithms; 4. Computational Complexity and Efficient Implementations; 5. Gillespie Algorithms for Temporal Networks and Non-Poissonian Jump Processes; 6. Conclusions; References.

Notes:

Title from publisher's bibliographic system (viewed on 19 Dec 2022).

Includes bibliographical references.

ISBN:

9781009239134

1009239139

9781009239127

1009239120

9781009239158

1009239155

OCLC:

1492998264