Computational Model Library

Extended Flache and Mas (2008) (1.0.0)

Building on the Lau and Murnighan’s theory of faultline strength, Flache and Mäs (2008) proposed a computational opinion dynamics model to explore the effect of demographic falutline strength on the cohesion of teams. We extend the Flache-Mäs model to incorporate the location and dyadic communication regime of the agents in the opinion formation process. More specifically, we make spatially proximate agents more likely to interact with each other in a pairwise communication regime. Our results show that when agents update their opinion after each pairwise encounter, the final opinion polarization is higher compared to when they update after interacting with all agents. However, if nearby agents are more likely to interact, we see less polarization in the final opinion space compared to the extended Flache-Mäs model. Policy implications of the results are discussed.

Release Notes

Associated Publications

Extended Flache and Mas (2008) 1.0.0

Building on the Lau and Murnighan’s theory of faultline strength, Flache and Mäs (2008) proposed a computational opinion dynamics model to explore the effect of demographic falutline strength on the cohesion of teams. We extend the Flache-Mäs model to incorporate the location and dyadic communication regime of the agents in the opinion formation process. More specifically, we make spatially proximate agents more likely to interact with each other in a pairwise communication regime. Our results show that when agents update their opinion after each pairwise encounter, the final opinion polarization is higher compared to when they update after interacting with all agents. However, if nearby agents are more likely to interact, we see less polarization in the final opinion space compared to the extended Flache-Mäs model. Policy implications of the results are discussed.

Version Submitter First published Last modified Status
1.0.0 Hadi Aliahmadi Mon Feb 26 20:03:46 2018 Mon Feb 26 20:03:46 2018 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