The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Game-Theoretic Analysis of the Hegselmann-Krause Model for Opinion Dynamics in Finite Dimensions
Open accessAbstract
We consider the Hegselmann-Krause model for opinion dynamics and study the evolution of the system under various settings. We first analyze the termination time of the synchronous Hegselmann-Krause dynamics in arbitrary finite dimensions and show that the termination time in general only depends on the number of agents involved in the dynamics. To the best of our knowledge, that is the sharpest bound for the termination time of such dynamics that removes dependency of the termination time from the dimension of the ambient space, and connects the convergence speed of the dynamics to the eigenvalues of the adjacency matrix of the connectivity graph in the Hegselmann-Krause dynamics. This answers an open question in the paper by Bhattacharyya et al. on how to obtain a tighter upper bound for the termination time. Furthermore, we study the asynchronous Hegselmann-Krause model from a novel game-theoretic approach and show that the evolution of an asynchronous Hegselmann-Krause model is equivalent to a sequence of best response updates in a well-designed potential game. We then provide a polynomial upper bound for the expected time and expected number of switching topologies until the dynamic reaches an arbitrarily small neighborhood of its equilibrium points, provided that the agents update uniformly at random. This is a step toward analysis of heterogeneous Hegselmann-Krause dynamics. Finally, we consider the heterogeneous Hegselmann-Krause dynamics and provide a necessary condition for the finite termination time of such dynamics. In particular, we sketch some future directions toward more detailed analysis of the heterogeneous Hegselmann-Krause model.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Consensus Problems in Networks of Agents With Switching Topology and Time-Delays
Olfati-Saber, R., & Murray, R. M. (2004) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2004.834113 · Cited by 12,788 · cited 1×
“In fact, our analysis for an asynchronous homogeneous HegselmannKrause model here is a step toward more detailed analysis of the heterogeneous model using an appropriate potential function over directed graphs [30], [4].”
MethodCoordination of groups of mobile autonomous agents using nearest neighbor rules
Jadbabaie, A., Lin, J., & Morse, A. S. (2003) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2003.812781 · Cited by 8,339
Horn, R. A., & Johnson, C. R. (2012) · doi:10.1017/cbo9781139020411 · Cited by 4,642
Cited by
A tutorial on modeling and analysis of dynamic social networks. Part I
Proskurnikov, A. V., & Tempo, R. (2017) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2017.03.002 · Cited by 553
A tutorial on modeling and analysis of dynamic social networks. Part II
Proskurnikov, A. V., & Tempo, R. (2018) · Annual Reviews in Control · doi:10.1016/j.arcontrol.2018.03.005 · Cited by 294
Metadata
- Authors
- Etesami, S. R., & Basar, T.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 2015-07
- Volume
- 60
- Issue
- 7
- Pages
- 1886-1897
- ISSN
- 0018-9286 (ISSN-L)
- 1558-2523
- OpenAlex
- W2020433780
- Cited by
- 186
- References
- 3
- FWCI
- 13.47
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Game-Theoretic Analysis of the Hegselmann-Krause Model for Opinion Dynamics in Finite Dimensions. https://app.cochat.ai/papers/10.1109/tac.2015.2394954
- The paper
- Etesami, S. R., & Basar, T. (2015). Game-Theoretic Analysis of the Hegselmann-Krause Model for Opinion Dynamics in Finite Dimensions. IEEE Transactions on Automatic Control. https://doi.org/10.1109/tac.2015.2394954
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.