Computational Model Library

COMM-PDND: Communication-Based Model of Perceived Descriptive Norm Dynamics in Digital Networks (1.0.0)

The Communication-Based Model of Perceived Descriptive Norm Dynamics in Digital Networks (COMM-PDND) is an agent-based model specifically created to examine the dynamics of perceived descriptive norms in the context of digital network structures. The model, developed as part of a master’s thesis titled “The Dynamics of Perceived Descriptive Norms in Digital Network Publics: An Agent-Based Simulation,” emphasizes the critical role of communication processes in norm formation. It focuses on the role of communicative interactions in shaping perceived descriptive norms.

The COMM-PDND is tuned to explore the effects of normative deviance in digital social networks. It provides functionalities for manipulating agents according to their network position, and has a versatile set of customizable parameters, making it adaptable to a wide range of research contexts.

COMM-PDND interface.png

Release Notes

Click Download to get access to the ABM - along with the full documentation and simulation outputs (Tip: The NetLogo model is located in the “code” directory).

To run the ABM, NetLogo 6.3 can be downloaded from the following link: http://ccl.northwestern.edu/netlogo

Associated Publications

Deffuant, G., Amblard, F., Weisbuch, G., & Faure, T. (2002). How can extremism prevail? A study based on the relative agreement interaction model. Journal of artificial societies and social simulation, 5(4). https://www.jasss.org/5/4/1.html

Flache, A., Mäs, M., Feliciani, T., Chattoe-Brown, E., Deffuant, G., Huet, S., & Lorenz, J. (2017). Models of Social Influence: Towards the Next Frontiers. Journal of Artificial Societies and Social Simulation, 20(4), 2. https://doi.org/10.18564/jasss.3521

Friemel, T. N., & Neuberger, C. (2021). Öffentlichkeit als dynamisches Netzwerk. In M. Eisenegger, M. Prinzing, P. Ettinger, & R. Blum (Hrsg.), Digitaler Strukturwandel der Öffentlichkeit (S. 81–96). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-32133-8_5

Hegselmann, R. & Krause, U. (2002). Opinion Dynamics and Bounded Confidence, Models, Analysis and Simulation. Journal of Artificial Societies and Social Simulation, 5, 2. https://www.jasss.org/5/3/2.html

Lorenz, J. (2012). Continuous Opinion Dynamics under Bounded Confidence. NetLogo. http://ccl.northwestern.edu/netlogo/models/community/bc

Piedrahita, P., Borge-Holthoefer, J., Moreno, Y., & González-Bailón, S. (2018). The contagion effects of repeated activation in social networks. Social Networks, 54, 326–335. https://doi.org/10.1016/j.socnet.2017.11.001

COMM-PDND: Communication-Based Model of Perceived Descriptive Norm Dynamics in Digital Networks 1.0.0

The Communication-Based Model of Perceived Descriptive Norm Dynamics in Digital Networks (COMM-PDND) is an agent-based model specifically created to examine the dynamics of perceived descriptive norms in the context of digital network structures. The model, developed as part of a master’s thesis titled “The Dynamics of Perceived Descriptive Norms in Digital Network Publics: An Agent-Based Simulation,” emphasizes the critical role of communication processes in norm formation. It focuses on the role of communicative interactions in shaping perceived descriptive norms.

The COMM-PDND is tuned to explore the effects of normative deviance in digital social networks. It provides functionalities for manipulating agents according to their network position, and has a versatile set of customizable parameters, making it adaptable to a wide range of research contexts.

Release Notes

Click Download to get access to the ABM - along with the full documentation and simulation outputs (Tip: The NetLogo model is located in the “code” directory).

To run the ABM, NetLogo 6.3 can be downloaded from the following link: http://ccl.northwestern.edu/netlogo

Version Submitter First published Last modified Status
1.0.0 Lars Reinelt Fri Sep 8 15:36:02 2023 Fri Sep 8 15:36:03 2023 Published

Discussion

This website uses cookies and Google Analytics to help us track user engagement and improve our site. If you'd like to know more information about what data we collect and why, please see our data privacy policy. If you continue to use this site, you consent to our use of cookies.
Accept