Abstract
The paper presents a new method of timeline balancing for monitoring and simulation of the possible scenarios of a social system development based on cross-correlation analysis of non-even time series. The proposed approach allows using the simulation results to improve the adequacy and accuracy of the forecast and determine the necessary and sufficient number of considered situations. The situational model is introduced to solve the problem of describing several generated options of the pace of developments in a complex social system. Possible generated options include positive, negative and indifferent scenarios as well as those considered as a possible feedback to specific incoming events. The main idea is to split the current situation to several options and generate separate scenarios for its development in time. The method allows evaluating the closeness of neutral expected scenario to its positive and negative options, which leads to automatically generated recommendations of collecting additional input data or making efforts to mitigate the risks or avoid negative effect. The proposed method implemented by the monitoring and simulation software platform was probated and tested to analyze the dynamics of the accumulated statistics on the key parameter of the growth of morbidity. Initial data includes the incidence statistics of Samara region for 18 months starting from March 2020, taken from the open sources online. Timeline balancing method is recommended as a component for data management and visualization in the analytical systems and specialized software for situational management and decision-making support.
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Ivaschenko, A., Dodonova, E., Dubinina, I., Sitnikov, P., Golovnin, O. (2022). Timeline Branching Method for Social Systems Monitoring and Simulation. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 506. Springer, Cham. https://doi.org/10.1007/978-3-031-10461-9_6
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DOI: https://doi.org/10.1007/978-3-031-10461-9_6
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