Modelling the stochastic dynamics of transitions between states in social systems incorporating self-organization and memory
Zhukov, Dmitry; Khvatova, Tatiana; Millar, Carla; Zaltcman, Anastasia (2020-06-16)
Sisältö avataan julkiseksi: 17.06.2022
Post-print / Final draft
Technological Forecasting and Social Change
School of Business and Management
The modelling approach is new, working with system level parameters, avoiding reference to node-level changes and modelling a non-Markov process by including self-organisation and the effects (memory) of previous system states over a configurable number of time intervals. Computational modelling is used to demonstrate how the percolation threshold (i.e. the share of nodes which allows information to spread freely within the network) is reached.
Possible applications of the model discussed include modelling the dynamics of viewpoints in society during social unrest and elections, changing attitudes in social networks and forecasting the outcome of promotions or uptake of campaigns. The easy availability of system level data (network connectivity, evolving system penetration) makes the model a particularly valuable addition to the toolkit for social sciences, politics, and potentially marketing.
Zhukov, D., Khvatova, T., Millar, C., Zaltcman, A. (2020). Modelling the stochastic dynamics of transitions between states in social systems incorporating self-organization and memory. Technological Forecasting and Social Change, vol. 158. DOI: 10.1016/j.techfore.2020.120134
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