Publication:
Gillespie Algorithms for Stochastic Multiagent Dynamics in Populations and Networks

dc.contributor.author Naoki Masuda, Christian L. Vestergaard
dc.date.accessioned 2024-01-22T03:02:25Z
dc.date.available 2024-01-22T03:02:25Z
dc.date.issued 2022
dc.description Publisher: Cambridge University Press ; License: CC BY-NC-ND ; Source: https://doi.org/10.1017/9781009239158 ; pages
dc.description.abstract 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.
dc.identifier.isbn 9781009239158
dc.identifier.uri http://repository.vlu.edu.vn:443/handle/123456789/12327
dc.language.iso en_US
dc.subject numerical simulations
dc.subject jump processes
dc.subject Poisson processes
dc.subject renewal processes
dc.subject complex systems
dc.title Gillespie Algorithms for Stochastic Multiagent Dynamics in Populations and Networks
dc.type Resource Types::text::book
dspace.entity.type Publication
oairecerif.author.affiliation #PLACEHOLDER_PARENT_METADATA_VALUE#
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