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2023 | OriginalPaper | Chapter

Studying Simulated Mobs Using Monte Carlo Method and the Theory of Collective Action

Authors : Samer Al-khateeb, Nitin Agarwal

Published in: Modeling and Simulation of Social-Behavioral Phenomena in Creative Societies

Publisher: Springer Nature Switzerland

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Abstract

A mob is an event that is organized via social media, email, SMS, or other forms of digital communication technologies in which a group of people (who might have an agenda) get together online or offline to collectively conduct an act and then disperse (quickly or over a long period). These events are increasingly happening around the world in recent years due to the anonymity of the internet, affordability of social media, boredom, etc. Studying such a phenomenon is not an easy task due to a lack of data, theoretical underpinning, and resources. In this research, we use Monte Carlo Experiments and our previous research on Deviant Cyber Flash Mobs that leveraged the theory of Collective Action to implement a model that can simulate a mob, estimate its success rate, and the needed powerful actors (e.g., mob organizers) to succeed. This research is one step toward fully understanding mob formation and the motivations of its participants and organizers.

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next chapter Dynamic Approach to Modeling Hierarchical Conflict Situations in Small Groups
Footnotes
1
These are differential equations that are used to calculate the strength of the military and were invented during WWII by Frederick W. Lanchester, an English engineer [22].
 
2
Social capital, as stated by Pierre Bourdieu, is “the value that one gain from personal connections such as membership in a family, an ethnic association, elite clubs, or other solidarity groups” [25].
 
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Metadata
Title
Studying Simulated Mobs Using Monte Carlo Method and the Theory of Collective Action
Authors
Samer Al-khateeb
Nitin Agarwal
Copyright Year
2023
Book
Modeling and Simulation of Social-Behavioral Phenomena in Creative Societies

Print ISBN: 978-3-031-33727-7

Electronic ISBN: 978-3-031-33728-4

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-33728-4_1

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