EFFECT OF INTERACTION NETWORK STRUCTURE IN A RESPONSE THRESHOLD MODEL
Abstract:
The study investigates the impact of interaction network structure on the dynamics of a response threshold model. The response threshold model is a widely used framework in various fields, including sociology, epidemiology, and ecology, to study the spread of information, diseases, and behaviors. The model assumes that individuals adopt a particular state or behavior when the number of neighboring individuals exhibiting that state exceeds a predefined threshold.
While previous research has primarily focused on the impact of individual attributes and threshold values on the model’s dynamics, the role of the underlying interaction network structure has received relatively less attention. This study aims to bridge this gap by systematically exploring how different interaction network structures influence the spread and persistence of behaviors in the response threshold model.
To achieve this objective, various interaction network topologies, such as regular lattices, random networks, and scale-free networks, are considered. The response threshold model is simulated on each network, and the resulting dynamics are analyzed and compared. The simulations involve examining the emergence and evolution of behaviors over time, as well as the overall cascade size and speed of behavior adoption.
Preliminary findings suggest that interaction network structure plays a crucial role in shaping the behavior adoption patterns in the response threshold model. For instance, regular lattices tend to promote the formation of localized clusters of behavior adoption, while scale-free networks exhibit a more rapid and extensive spread of behaviors. Random networks, on the other hand, exhibit a balance between localized clusters and global behavior adoption.
Additionally, the study investigates the impact of network characteristics, such as average degree, clustering coefficient, and network diameter, on the model’s dynamics. By systematically varying these network properties, insights into the relationship between network structure and behavior adoption are gained.
Understanding the effect of interaction network structure on the response threshold model has significant implications for various domains. It can provide insights into the design of interventions and strategies to promote or hinder the spread of behaviors or information in different types of social networks. Furthermore, it can contribute to the development of more accurate models for predicting and managing the dynamics of diseases, social movements, and other complex phenomena.
Keywords: response threshold model, interaction network structure, behavior adoption, social networks, epidemic modeling, complex systems.