Introducing non-Markovian and empirical effects into social interaction models
Authors
Doyle, Casey
ORCID
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Other Contributors
Korniss, Gyorgy
Szymanśki, Bolesław
Meunier, Vincent
Terrones, H. (Humberto)
Szymanśki, Bolesław
Meunier, Vincent
Terrones, H. (Humberto)
Issue Date
2018-08
Keywords
Physics
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
Stochastic models of opinion spread are a popular method for simulating and predicting the social behaviors of large populations. Though classical models in this field have proven to be accurate towards their intended purpose, often they fall short when applied to more specific scenarios. Many of the assumptions made in these base models have proven to be quite different from the natural behavioral patterns of real people, making further updates and extensions of the original models imperative to understand these shortcomings. This work presents two such model extensions, building off of the basic examples of the naming game and voter models to create more in depth systems and describe complex phenomenon.
Description
August 2018
School of Science
School of Science
Department
Dept. of Physics, Applied Physics, and Astronomy
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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