Introducing non-Markovian and empirical effects into social interaction models
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Authors
Doyle, Casey
Issue Date
2018-08
Type
Electronic thesis
Thesis
Thesis
Language
ENG
Keywords
Physics
Alternative Title
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
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY