Modeling the dynamics of social contagion processes has recently attracted a substantial amount of interest from researchers due to its wide applicability in network science, multi-agent systems, information science, and marketing. Unlike in biological spreading, the existence of a reinforcement effect in social contagion necessitates considering the complexity of individuals in the systems. Although many studies acknowledged the heterogeneity of the individuals in their adoption of information (or behavior), there are no studies that take into account the individuals' uncertainty during their decision-making despite its theoretical and experimental evidence in behavioral economics, decision science, cognitive science, or multi-agent systems. This resulted in less than optimal modeling of social contagion dynamics in the existence of phase transition in the final adoption size versus transmission probability. We believe that it is mainly because traditional approaches do not consider the uncertainty stemming from agent interactions through an information exchange that can influence individuals' emotions, change subconscious feelings, and trigger subjective biases. To address this problem, we propose quantum-like generalization of social contagion analysis for the analysis of co-evolutionary dynamics of social contagion. For this purpose, we employed Inverse Born Problem (IBP) to represent probabilistic entities as complex probability amplitudes in edge-based compartmental theory and demonstrated that our novel approach performs better in the prediction of social contagion dynamics through extensive simulations on random regular networks.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2261 |
| Date | 01 January 2022 |
| Creators | Mutlu, Ece |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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