Tag Archives | Quantity Flows

Generalized Markovian Quantity Distribution Systems: Social Science Applications

Noah E. Friedkin, Anton V. Proskurnikov

Sociological Science October 8, 2020
10.15195/v7.a20


We propose a model of Markovian quantity flows on connected networks that relaxes several properties of the standard compartmental Markov process. The motivation of our generalization are social science applications of the standard model that do not comport with its steady state predictions. The proposed generalization relaxes the predictions that every node belonging to the same nontrivial strong component of a network must acquire the same fraction of its members’ initial quantities and that the sink component(s) of the network must absorb all of the system’s available initial quantity. For example, when applied to refugee flows from a nation in chaos to other nations on a network with one or more sink nations, the standard model predicts that all the refugees will be eventually located in the sink(s) of the network and none that will permanently locate themselves in the nations along the paths to the sink(s). We illustrate this and several other social science applications to which our proposed model is applicable.
Creative Commons LicenseThis work is licensed under a Creative Commons Attribution 4.0 International License.

Noah E. Friedkin: Department of Sociology and the Center for Control, Dynamical Systems, and Computation, University of California, Santa Barbara
E-mail: friedkin@soc.ucsb.edu

Anton V. Proskurnikov: Politecnico di Torino, Turin, Italy, and the Institute for Problems of Mechanical Engineering of the Russian Academy of Sciences, St. Petersburg, Russia
E-mail: anton.p.1982@ieee.org

  • Citation: Friedkin, Noah E., and Anton V. Proskurnikov. 2020. “Generalized Markovian Quantity Distribution Systems: Social Science Applications.” Sociological Science 7: 487-503.
  • Received: September 3, 2020
  • Accepted: September 10, 2020
  • Editors: Jesper Sørensen, Olav Sorenson
  • DOI: 10.15195/v7.a20


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