Crowd Dynamics: Modeling and Control of Multiagent Systems

Gong, Xiaoqian; Herty, Michaël; Piccoli, Benedetto; Visconti, Giuseppe

doi:10.1146/annurev-control-060822-123629

Cited by 4 publications

(1 citation statement)

References 120 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the one hand, different heuristics have been developed to determine which agents should be targeted by an external controller to maximize their influence [41,43,34]. On the other hand, in [2,31], it is studied how external controllers have to act to bring the opinion distribution of agents close to a specified target distribution, while, in [34], it is established how to control opinion distributions by (externally) interfering with the interaction network between individuals, and, in [1,25], dynamic external controls for mean-field opinion models are considered.…”

Section: Introductionmentioning

confidence: 99%

Modelling opinion dynamics under the impact of influencer and media strategies

Helfmann¹,

Conrad²,

Lorenz-Spreen³

et al. 2023

Preprint

View full text Add to dashboard Cite

Digital communication has made the public discourse considerably more complex, and new actors and strategies have emerged as a result of this seismic shift. Aside from the often-studied interactions among individuals during opinion formation, which have been facilitated on a large scale by social media platforms, the changing role of traditional media and the emerging role of "influencers" are not well understood, and the implications of their engagement strategies arising from the incentive structure of the attention economy even less so. Here we propose a novel opinion dynamics model that accounts for these different roles, namely that media and influencers change their own positions on slower time scales than individuals, while influencers dynamically gain and lose followers. Numerical simulations show the importance of their relative influence in creating qualitatively different opinion formation dynamics: with influencers, fragmented but shortlived clusters emerge, which are then counteracted by more stable media positions. Mean-field approximations by partial differential equations reproduce this dynamic. Based on the mean-field model, we study how strategies of influencers to gain more followers can influence the overall opinion distribution. We show that moving towards extreme positions can be a beneficial strategy for influencers to gain followers. Finally, we demonstrate that optimal control strategies allow other influencers or media to counteract such attempts and prevent further fragmentation of the opinion landscape. Our modelling framework contributes to better understanding the different roles and strategies in the increasingly complex information ecosystem and their impact on public opinion formation.

show abstract

Section: Introductionmentioning

confidence: 99%

Modelling opinion dynamics under the impact of influencer and media strategies

Helfmann¹,

Conrad²,

Lorenz-Spreen³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Modelling opinion dynamics under the impact of influencer and media strategies

Helfmann,

Djurdjevac Conrad,

Lorenz-Spreen

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Digital communication has made the public discourse considerably more complex, and new actors and strategies have emerged as a result of this seismic shift. Aside from the often-studied interactions among individuals during opinion formation, which have been facilitated on a large scale by social media platforms, the changing role of traditional media and the emerging role of “influencers” are not well understood, and the implications of their engagement strategies arising from the incentive structure of the attention economy even less so. Here we propose a novel framework for opinion dynamics that can accommodate various versions of opinion dynamics as well as account for different roles, namely that of individuals, media and influencers, who change their own opinion positions on different time scales. Numerical simulations of instances of this framework show the importance of their relative influence in creating qualitatively different opinion formation dynamics: with influencers, fragmented but short-lived clusters emerge, which are then counteracted by more stable media positions. The framework allows for mean-field approximations by partial differential equations, which reproduce those dynamics and allow for efficient large-scale simulations when the number of individuals is large. Based on the mean-field approximations, we can study how strategies of influencers to gain more followers can influence the overall opinion distribution. We show that moving towards extreme positions can be a beneficial strategy for influencers to gain followers. Finally, our framework allows us to demonstrate that optimal control strategies allow other influencers or media to counteract such attempts and prevent further fragmentation of the opinion landscape. Our modelling framework contributes to a more flexible modelling approach in opinion dynamics and a better understanding of the different roles and strategies in the increasingly complex information ecosystem.

show abstract

Biological modeling with nonlocal advection–diffusion equations

Painter,

Hillen,

Potts

2023

Math. Models Methods Appl. Sci.

View full text Add to dashboard Cite

The employment of nonlocal PDE models to describe biological aggregation and other phenomena has gained considerable traction in recent years. For cell populations, these methods grant a means of accommodating essential elements such as cell adhesion, critical to the development and structure of tissues. For animals, they can be used to describe how the nearby presence of conspecifics and/or heterospecifics influence movement behaviour. In this review, we will focus on classes of biological movement models in which the advective (or directed) component to motion is governed by an integral term that accounts for how the surrounding distribution(s) of the population(s) impact on a member’s movement. We recount the fundamental motivation for these models: the intrinsic capacity of cell populations to self-organise and spatially sort within tissues; the wide-ranging tendency of animals towards spatial structuring, from the formations of herds and swarms to territorial segregation. We examine the derivation of these models from an individual level, illustrating in the process methods that allow models to be connected to data. We explore a growing analytical literature, including methods of stability and bifurcation analysis, and existence results. We conclude with a short section that lays out some future challenges and connections to the modelling of sociological phenomena including opinion dynamics.

show abstract

Crowd Dynamics: Modeling and Control of Multiagent Systems

Cited by 4 publications

References 120 publications

Modelling opinion dynamics under the impact of influencer and media strategies

Modelling opinion dynamics under the impact of influencer and media strategies

Modelling opinion dynamics under the impact of influencer and media strategies

Biological modeling with nonlocal advection–diffusion equations

Contact Info

Product

Resources

About