Modeling and analyzing knowledge transmission process considering free-riding behavior of knowledge acquisition: A waterborne disease approach

Shi-Gen Liao and Shu-Ping Yi

Physica A: Statistical Mechanics and its Applications, 2021, vol. 569, issue C

Abstract: Free-riding is a common phenomenon in the knowledge transmission process. Compared with acquiring knowledge from individual interactions, acquiring knowledge from a knowledge base can be regarded as a free-riding behavior that costs less and benefits from team contributions. To consider this approach in knowledge infection modeling, in this study, a heterogeneous stochastic susceptible–infected-recovered (SSIR)-knowledge base (B) knowledge transmission model is established, based on the similarity between water-borne diseases and knowledge transmission considering free-riding. Specifically, a “water pool” (i.e., a knowledge base) is associated with the individual interactions, to describe how knowledge can travel from groups to free riders. The model includes two knowledge infection processes: human–human infection and human–knowledge base–human infection. As a difference from the waterborne disease model, these two processes act on active and free riders, respectively. A theoretical basic regeneration number is given. Furthermore, the global stabilities of the knowledge-free equilibrium and knowledge-endemic equilibrium are analyzed. The theoretical analysis implies that when R0<1, the knowledge in the system will eventually die out. Conversely, when R0>1, knowledge transmission will remain active. Numerical simulations are used to illustrate the theoretical analysis results, and can be used to further investigate the effects of parameters on the knowledge propagation process. The results suggest that our model can provide a good mechanism for two different understandings about free-riding.

Keywords: Knowledge transmission; Waterborne disease model; Free-riding; Global stability; Complex networks (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:569:y:2021:i:c:s0378437121000418

DOI: 10.1016/j.physa.2021.125769

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