Gillespie algorithms for stochastic multiagent dynamics in populations and network

Abstract

Many multiagent dynamics, including various collective dynamics occurring on networks, 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. In the first main part of this volume, we provide a tutorial on the Gillespie algorithms focusing on simulation of social multiagent dynamics occurring in populations and networks. We do not assume advanced knowledge of mathematics (or computer science or physics). We clarify why one should use the continuous-time models and the Gillespie algorithms in many cases, instead of easier-to-understand discrete-time models. In the remainder of this volume, we review 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.

25 figures. Elements in the Structure and Dynamics of Complex Networks. Cambridge: Cambridge University Press (2023)

References in corpus (9)

• Statistical physics of social dynamics
• Prediction and predictability of global epidemics: the role of the airline transportation network
• Reaction-diffusion processes and metapopulation models in heterogeneous networks
• Adaptive Coevolutionary Networks: A Review
• Small But Slow World: How Network Topology and Burstiness Slow Down Spreading
• Close encounters in a pediatric ward: measuring face-to-face proximity and mixing patterns with wearable sensors
• Scaling in the Inter-Event Time of Random and Seasonal Systems
• Multilayer Network Science: from Cells to Societies
• Gillespie algorithms for stochastic multiagent dynamics in populations and network

Cited by in corpus (8)

• Descriptive vs. inferential community detection in networks: pitfalls, myths, and half-truths
• Gillespie algorithms for stochastic multiagent dynamics in populations and network
• A Parrondo paradox in susceptible-infectious-susceptible dynamics over periodic temporal networks
• A unified perspective on exponential tilt and bridge algorithms for rare trajectories of discrete Markov processes
• Biased versus unbiased numerical methods for stochastic simulations
• Spline tie-decay temporal networks
• Effects of concurrency on epidemic spreading in Markovian temporal networks
• Target-density formation in swarms with stochastic sensing and dynamics