Optimal control of the freezing time in the Hegselmann–Krause dynamics

Kurz, Sascha

doi:10.1080/10236198.2015.1045890

Cited by 14 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many publications have used the model for investigating descriptive questions, most notably, questions concerning the conditions that lead a community of initially disagreeing agents to reach a consensus and those that lead to polarization (e.g., Lorenz, 2003Lorenz, , 2008. A major focus of studies lay, and (due to many open questions) still lies, on the time that it takes to reach a stable final pattern (Chazelle, 2011;Kurz & Rambau, 2011;Kurz, 2015;Hegarty & Wedin, 2016). Other work has recruited the model to shed light on a number of normative issues of interest mostly to philosophers, for instance, concerning the practice of assertion (Olsson, 2008), the resolution of disagreement amongst peers (Douven, 2010), and efficient truth approximation (Douven & Kelp, 2011).…”

Section: Bounded Confidence Updatingmentioning

confidence: 99%

Mis- and disinformation in a bounded confidence model

Douven

Hegselmann

2021

Artificial Intelligence

View full text Add to dashboard Cite

The bounded confidence model has been widely used to formally study groups of agents who are sharing opinions with those in their epistemic neighborhood. We revisit the model with an eye toward studying mis-and disinformation campaigns, which have been much in the news of late. To that end, we introduce typed agents into the model, specifically agents who can be irresponsible in different ways, most notably, by being deceitful, but also by being reluctant to try and obtain information from the world directly. We further add a mechanism of confidence dynamics to the model, which-among other things-allows agents to adapt the closeness threshold for counting others as being their epistemic neighbors. This will be used to study the effectiveness of possible defense mechanisms against mis-and disinformation efforts.

show abstract

Section: Bounded Confidence Updatingmentioning

confidence: 99%

Mis- and disinformation in a bounded confidence model

Douven

Hegselmann

2021

Artificial Intelligence

View full text Add to dashboard Cite

show abstract

Noisy Hegselmann-Krause Systems: Phase Transition and the 2R-Conjecture

Wang

Weinan

et al. 2017

J Stat Phys

View full text Add to dashboard Cite

The classic Hegselmann-Krause (HK ) model for opinion dynamics consists of a set of agents on the real line, each one instructed to move, at every time step, to the mass center of all the agents within a fixed distance R. In this work, we investigate the effects of noise in the continuous-time version of the model as described by its mean-field limiting Fokker-Planck equation. In the presence of a finite number of agents, the system exhibits a phase transition from order to disorder as the noise increases. The ordered phase features clusters whose width depends only on the noise level. We introduce an order parameter to track the phase transition and resolve the corresponding phase diagram. The system undergoes a phase transition for small R but none for larger R. Based on the stability analysis of the mean-field equation, we derive the existence of a forbidden zone for the disordered phase to emerge. We also provide a theoretical explanation for the well-known 2R conjecture, which states that, for a random initial distribution in a fixed interval, the final configuration consists of clusters separated by a distance of roughly 2R. Our theoretical analysis also confirms previous simulations and predicts properties of the noisy HK model in higher dimension.

show abstract