Gillespie Algorithms for Stochastic Multiagent Dynamics in Populations and Networks

The Gillespie algorithms are popular for simulating stochastic multiagent dynamics, which are modeled as a stochastic process. They are used to simulate social multiagent dynamics in populations and networks, and recent extensions aim to add more reality or speed up simulations.

Format: Paperback / softback
Publication date: 05 January 2023
Publisher: Cambridge University Press

Numerous multi-agent dynamics can be approximated as stochastic processes, wherein the agents within the system undergo state transitions over time through interactions with one another. The Gillespie algorithms are widely recognized for their ability to precisely simulate such stochastic multi-agent dynamics, particularly when each state change is triggered by a discrete event, the dynamics are defined in continuous time, and the occurrence of stochastic events is governed by independent Poisson processes.

The first major section of this volume serves as a tutorial, specifically focusing on the Gillespie algorithms, with an emphasis on simulating social multi-agent dynamics occurring within populations and networks. The authors elucidate the rationale behind employing continuous-time models and the Gillespie algorithms in numerous scenarios, surpassing the simplicity of discrete-time models. The remaining sections of the Element explore recent advancements in the Gillespie algorithms, aimed at enhancing the realism of the models (including non-Poissonian cases) or accelerating the simulation process.

This title is also accessible as open access on Cambridge Core.

Weight: 166g
ISBN-13: 9781009239141