Scenario-Cognitive Modeling of Complex Systems Based on Event-Driven Identification of Factor Dynamics
- Autores: Chernov I.V1
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Afiliações:
- Trapeznikov Institute of Control Sciences, Russian Academy of Sciences
- Edição: Nº 3 (2023)
- Páginas: 65-76
- Seção: Control in Social and Economic Systems
- URL: https://ogarev-online.ru/1819-3161/article/view/286632
- DOI: https://doi.org/10.25728/pu.2023.3.5
- ID: 286632
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Resumo
This paper is devoted to methodological problems of increasing the effectiveness of scenario analysis and modeling of development processes in socio-economic systems. The corresponding results can be used in management decision support systems for proactive evaluation of their effectiveness. Several limitations of the traditional approach to scenario-cognitive modeling are considered; due to these limitations, the resulting scenario neglects key events directly affecting the assessment of the current situation and decision-making. A novel approach is proposed to identify and analyze the dynamics of factor values when studying the model as well as to form additional scenario-event relationships between the factors in order to increase the adequacy of the model to the situation. A computational algorithm is developed to analyze the dynamics of factor values of the model. This algorithm is implemented and tested within the program-analytical complex of scenario modeling. Finally, an example of using the algorithm is given.
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Sobre autores
I. Chernov
Trapeznikov Institute of Control Sciences, Russian Academy of Sciences
Email: ichernov@gmail.com
Moscow, Russia
Bibliografia
- Модели и методы анализа и синтеза сценариев развития социально-экономических систем: в 2-х кн. / Под ред. В.Л. Шульца и В.В. Кульбы. – М.: Наука, 2012. Кн. 1. – 304 с., кн. 2. – 358 с. [Modeli i metody analiza i sinteza scenariev razvitiya so-cial'no-ekonomicheskih sistem: v 2-h kn. / Pod red. V.L. Shul'ca i V.V. Kul'by. – M.: Nauka, 2012. Kn. 1. – 304 s., kn. 2. – 358 s. (In Russian)]
- Дранко О.И., Новиков Д.А., Райков А.Н., Чернов И.В. Управление развитием региона. Моделирование возможностей. – М.: URSS, ООО «ЛЕНАНД», 2023. – 432 с. [Dranko O.I., Novikov D.A., Rajkov A.N., Chernov I.V. Upravlenie razvitiem regiona. Modelirovanie vozmozhnostej. – M.: URSS, OOO «LENAND», 2023. – 432 s. (In Russian)]
- Авдеева З.К., Коврига С.В. О постановке задач управления ситуацией со многими активными субъектами с использо-ванием когнитивных карт // Управление большими системами. – 2017. – Вып. 68. – С. 74–99. [Avdeeva, Z.K., Kovriga S.V. On the Statement of Control Problems of the Situation with Many Active Stakeholders with Use of Cognitive Maps // Large-Scale Systems Control. – 2017. – Vol. 68. – P. 74–99. (In Russian)]
- Plonsky, O., Apel, R., Ert, E., et al. Predicting Human Decisions with Behavioral Theories and Machine Learning // arXiv. – 2019. – DOI: https://doi.org/10.48550/arXiv.1904.06866.
- Stulp, F., and Sigaud, O. Many Regression Algorithms, One Unified Model: A Review // Neural Networks. – 2015. – Vol. 69. – P. 60–79.
- Wooldridge, J.M. Introductory Econometrics: A Modern Approach // Boston: Cengage Learning, 2019.
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