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.
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