Modelling the stochastic dynamics of transitions between states in social systems incorporating self-organization and memory
Dmitry Zhukov,
Tatiana Khvatova (),
Carla Millar and
Anastasia Zaltcman
Additional contact information
Dmitry Zhukov: EM - EMLyon Business School
Post-Print from HAL
Abstract:
This conceptual research presents a new stochastic model of the dynamics of state-to-state transitions in social systems, the Zhukov–Khvatova model. Employing a mathematical approach based on percolation theory the model caters for random changes, system memory and self-organisation. Curves representing the approach of the system to the percolation threshold differ significantly from the smooth S-shaped curves predicted by existing models, showing oscillations, steps and abrupt steep gradients. 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.
Keywords: Algorithms for monitoring network and social system states; Non-Markov; S-curve; Self-organization; Semi-random processes with memory; Social system; Stochastic dynamics (search for similar items in EconPapers)
Date: 2020-09-01
References: Add references at CitEc
Citations: View citations in EconPapers (4)
Published in Technological Forecasting and Social Change, 2020, 158
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03188186
Access Statistics for this paper
More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().