Andrea Garulli (Università di Siena)
Asymptotic behaviors of a class of threshold models for social networks
We study the asymptotic behaviors of threshold models used to describe the formation of collective actions in social networks. The model has been introduced to analyze the mechanisms underlying the formation of a collective action taking place during political unrest or social revolutions, but can be generalized to networks in which the agents make a choice between two possible actions, at every time instant. The decision of each agent is made on the basis of the actions chosen by the agent’s neighbors and the value of a dynamically updated threshold. The main novelty of the proposed model is the introduction of a parameter accounting for the level of self-confidence of the agents, which affects the dynamic evolution of the threshold and in turn the way the agents make their decision. Three different network topologies are considered and for each of them the possible limiting behaviors of the network are characterized in terms of the self-confidence parameter and of the initial threshold value.
We study the asymptotic behaviors of threshold models used to describe the formation of collective actions in social networks. The model has been introduced to analyze the mechanisms underlying the formation of a collective action taking place during political unrest or social revolutions, but can be generalized to networks in which the agents make a choice between two possible actions, at every time instant. The decision of each agent is made on the basis of the actions chosen by the agent’s neighbors and the value of a dynamically updated threshold. The main novelty of the proposed model is the introduction of a parameter accounting for the level of self-confidence of the agents, which affects the dynamic evolution of the threshold and in turn the way the agents make their decision. Three different network topologies are considered and for each of them the possible limiting behaviors of the network are characterized in terms of the self-confidence parameter and of the initial threshold value.