Sikder, Orowa;
(2024)
Averaging dynamics on graphs: models, methods and applications.
Doctoral thesis (Ph.D), UCL (University College London).
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Abstract
We hypothesize that a unifying framework of models and methods in averaging dynamics on graphs can be created, that can be consistently applied to study a range of socioeconomic phenomenon. The thesis tests this theory by constructing this framework, based on a review of relevant literature. We show only a particular subclass of models possesses the properties we desire for socioeconomic modelling. We develop an analytic methodology to predict the steady state outcomes of such models, verified through extensive simulation. Finally, we apply this set of models and methods to analyze both a breadth and depth of real socioeconomic phenomenon. The major contributions of this thesis are: (1) A unifying framework for a broad class of “averaging dynamics” models and theorems that help characterize their behavior. (2) A novel analytic method for solving the equilibrium outcomes of averaging dynamics that can be easily adapted for new variations. Method published. (3) A novel analytic model of information flow in social networks under the effects of confirmation bias, which emergently demonstrates filter bubbles and information cascades. It makes specific predictions about mis- information flow, verified with a proof-of-concept experiment using data on climate change misinformation in the US. Results published. (4) A novel game-theoretic model of strategic control of information. The model predicts an optimal strategy for maximizing political polarisation, and we show this resembles available patterns of Russian cyberattacks on US and Catalonian elections. We show that the Nash equilibrium outcome of two-party campaign politics approximates exactly this outcome, and analyze whether this strategy shows up in real data on US Presidential campaign expenditures. The thesis concludes with a discussion of the overall implications from the framework and outlines future work. Major proofs used in the thesis are provided in the appendices.
| Type: | Thesis (Doctoral) |
|---|---|
| Qualification: | Ph.D |
| Title: | Averaging dynamics on graphs: models, methods and applications |
| Open access status: | An open access version is available from UCL Discovery |
| Language: | English |
| Additional information: | Copyright © The Author 2022. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) Licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request. |
| Keywords: | networks, graphs, multiagent, random-walk |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10191124 |
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