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008 240313s2022 enk|||||o|||| 00| 0 eng d
020 _a9781009239158
040 _aSPU
049 _amain
050 4 _aQA 9.58
_bM37G 2022
100 _aMasuda, Naoki,
_d1976-
_9256942
245 1 0 _aGillespie algorithms for stochastic multiagent dynamics in populations and networks /
_cNaoki Masuda, Christian L. Vestergaard
260 _aCambridge :
_bCambridge University Press,
_c2022
300 _aonline resource
449 _a140504
490 1 _aCambridge elements. Elements in the structure and dynamics of complex networks
505 _aIntroduction -- Preliminaries -- Classic Gillespie Algorithms -- Computational Complexity and Efficient Implementations -- Gillespie Algorithms for Temporal Networks and Non-Poissonian Jump Processes
506 _aAvailable to OhioLINK libraries
520 _aMany 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
650 0 _aALGORITHMS
_938987
650 0 _aSTOCHASTIC PROCESSES
_938474
650 0 _aMULTIAGENT SYSTEMS
_9256938
650 0 _aSYSTEM ANALYSIS
_935579
700 _aVestergaard, Christian L.
_9256941
830 _aCambridge elements. Elements in the structure and dynamics of complex networks
_9256937
850 _aSPU
856 _uhttps://doi.org/10.1017/9781009239158
910 _aLibrary
_bCambridge University Press
_c120324
_pEB000460
942 _2lcc
_cEBK
998 _aniparat 0324
999 _c213743