The Arab Spring: A simple compartmental model for the dynamics of a revolution

Lang, John; Sterck, Hans De

doi:10.1016/j.mathsocsci.2014.01.004

Cited by 35 publications

(56 citation statements)

References 20 publications

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“…The results gleaned from the empirical evidence support the idea that citizens perceived social media to be very important during the Arab Spring (Howard et al 2011;Lang and De Sterck 2014), including the Tunisian revolution (Marzouki et al 2012) or the protests in Egypt (Attia et al 2011;Azab 2012;Lim 2012;Tufekci and Wilson 2012). Hence, microblogging activity (Qin et al 2016) or the amount of tweets (Acemoglu et al 2014;Steinert-Threlkeld et al 2015) might have been related to the incidence and the intensity of protests.…”

Section: Introductionmentioning

confidence: 54%

“…Citizens are ex ante identical and then they receive noisy signals about the regime's strength from several media outlets, but not about the decisions of other citizens. Lang and De Sterck (2014) show that mobilization is successful only if protesters are enthusiastic enough and their action is visible.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the models without observing other citizens' decisions (De Mesquita 2010;Edmond 2013) observing correlated signals makes a revolt potentially successful. In the models where observing other individuals plays a role (Ellis and Fender 2011;Lang and De Sterck 2014) there is only one type of individuals and if enough of them decide to revolt, then the dictator is overthrown. Our model attempts to capture the uncertainty involved in mobilization, resorting to unwilling individuals whose choice is always the same.…”

Section: Literature Reviewmentioning

confidence: 99%

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Overthrowing the dictator: a game-theoretic approach to revolutions and media

2017

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A distinctive feature of recent revolutions was the key role of social media (e.g. Facebook, Twitter and YouTube). In this paper, we study its role in mobilization. We assume that social media allow potential participants to observe the individual participation decisions of others, while traditional mass media allow potential par- ticipants to see only the total number of people who participated before them. We show that when individuals' willingness to revolt is publicly known, then both sorts of media foster a successful revolution. However, when willingness to revolt is private information, only social media ensure that a revolt succeeds, with mass media multiple outcomes are possible, one of which has individuals not participating in the revolt. This suggests that social media enhance the likelihood that a revolution triumphs more than traditional mass media.

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Section: Introductionmentioning

confidence: 54%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Overthrowing the dictator: a game-theoretic approach to revolutions and media

2017

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“…Political philosophers expound on a variety of political ideologies, or ways of government in order to improve the organization of the societies [10][11][12][13][14][15][16]. The study of ideologies is also of great interest for sociologists [17][18][19][20][21], psychologists [22][23][24], biologists [25,26] and even computer scientists [27][28][29][30]. A definition of belief system is provided by [31].…”

Section: -Introductionmentioning

confidence: 99%

A Belief System's Organization Based on a Computational Model of the Dynamic Context: First Approximation

Pont

Mora

López

et al. 2015

Cybernetics and Systems

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In this paper we present a model of organization of a belief system based on a set of binary recursive functions that characterize the dynamic context that modifies the beliefs. The initial beliefs are modeled by a set of two bit words that grow, update and generate other beliefs as the different experiences of the dynamic context appear. Reason is presented as an emergent effect of the experience on the beliefs. The system presents a layered structure that allows a functional organization of the belief system. Our approach seems suitable to model different ways of thinking and to apply to different realistic scenarios such as ideologies.Key-words. Recursive functions, behavioral patterns, belief system, ideology 1-IntroductionIt is understood that our behavior is the result of both our beliefs and our capability to implement them by means of actions on the world around us. Belief systems are sets of norms that provide a coherent interpretation of the world and allow a viable interaction between humans and society [1,2]. Everybody may have a belief system which is shared totally, partially or not shared with others [3, 4] but belief systems don't deal only with individuals, they also develop social structured organizations that promote attitudes and values in order to provide stability and transmission [5]. The interaction between individuals and organizations and the changes in the world may trigger belief revision [6][7][8][9]. Ideology is our most closely held belief system, made of values, feelings and opinions which act as the filter through which we see everything and everybody. Ideologies have played a key role in history by shaping governments and political movements. Political philosophers expound on a variety of political ideologies, or ways of government in order to improve the organization of the societies [10][11][12][13][14][15][16]. The study of ideologies is also of great interest for sociologists [17][18][19][20][21], psychologists [22][23][24], biologists [25,26] and even computer scientists [27][28][29][30]. A definition of belief system is provided by [31]. The authors claim a belief system is a set of related ideas, learned and shared which has some permanence in time and space. They also affirm any belief system is formed by two essential levels denoted the abstract level and the material level, and operates as a cybernetic feedback process. The abstract level is formed by a set of substantive beliefs that are the axioms of the system and by a set of derived beliefs that constitute their theorems. Our proposed belief system considers that experience coming from the dynamic context applies to a few initial embryonic beliefs (substantive beliefs) that grow and increase in number (derived beliefs). The experience modifies the beliefs and next, the emergence of reason completes the updating. We claim reason needs previous beliefs and incoming experience to arise, but in our model, the relationship between cognition and experience

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“…Generally, belief systems have no need to be constructed upon reason and survive as long as they provide a satisfactory approach or explanation to events that are poorly understood. Many different disciplines such as mathematics [1,4]], biology [5][6][7][8], psychology [9][10][11], philosophy [12,13], sociology [14][15][16], politics [17][18][19][20] and more recently computational science [21][22][23][24][25][26][27][28][29] have supported important advances at different levels of analysis from the molecular/neurological, to the cognitive/psychological and finally to the social/institutional and motivate a great interest for the study of belief systems. In this paper we consider the belief systems under the scope of the communication of ideas which is crucial in society evolution.…”

mentioning

confidence: 99%

A Computational Model of the Belief System Under the Scope of Social Communication

et al. 2015

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This paper presents an approach to the belief system based on a computational framework in three levels: first, the logic level with the definition of binary local rules, second, the arithmetic level with the definition of recursive functions and finally the behavioural level with the definition of a recursive construction pattern. Social communication is achieved when different beliefs are expressed, modified, propagated and shared through social nets. This approach is useful to mimic the belief system because the defined functions provide different ways to process the same incoming information as well as a means to propagate it. Our model also provides a means to cross different beliefs so, any incoming information can be processed many times by the same or different functions as it occurs is social nets. Key-words. Binary local rules, recursive functions, behavioural patterns, belief system, social communication 1-IntroductionBeliefs are very important for individuals since they give sense to their actions when available information is incomplete or inconsistent. Beliefs have the capability to impact on our behavior and are a powerful engine to move and change the social environment. Reciprocally, societal changes may trigger belief revision [1,2]. Belief systems are sets of norms that provide an organized interpretation of the world to the human beings in order to allow a viable interaction human/society [3]. Everybody may have a belief system which is shared totally, partially or not shared with others. Generally, belief systems have no need to be constructed upon reason and survive as long as they provide a satisfactory approach or explanation to events that are poorly understood. Many different disciplines such as mathematics [1,4]], biology [5][6][7][8], psychology [9][10][11], philosophy [12,13], sociology [14][15][16], politics [17][18][19][20] and more recently computational science [21][22][23][24][25][26][27][28][29] have supported important advances at different levels of analysis from the molecular/neurological, to the cognitive/psychological and finally to the social/institutional and motivate a great interest for the study of belief systems. In this paper we consider the belief systems under the scope of the communication of ideas which is crucial in society evolution. Communication between humans allows a wide expansion of ideas grounded in beliefs that configure a time varying map of trends. Nowadays, social nets are responsible of the speedy and ubiquitous information propagation which implies the emergence of trending topics. Our approach deals with the keys of message modification and propagation in society as a case of complex system behaviour. We define a set of binary local rules that apply recursively and trigger functions with complex emergent behaviours. These provide a model for social communication based in beliefs. After introduction, Section 2 develops the computational model in three levels: first, the logic level with the definition of binary local rules, second, the ari...

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The Arab Spring: A simple compartmental model for the dynamics of a revolution

Cited by 35 publications

References 20 publications

Overthrowing the dictator: a game-theoretic approach to revolutions and media

Overthrowing the dictator: a game-theoretic approach to revolutions and media

A Belief System's Organization Based on a Computational Model of the Dynamic Context: First Approximation

A Computational Model of the Belief System Under the Scope of Social Communication

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