RuEn

Journal section "Theoretical and methodological issues"

Forecasting the Dissemination of Norms and Values in Russia with the Use of an Agent-Based Approach

Mashkova A.L.

Volume 15, Issue 1, 2022

Mashkova A.L. (2022). Forecasting the dissemination of norms and values in Russia with the use of an agentbased approach. Economic and Social Changes: Facts, Trends, Forecast, 15(1), 90–109. DOI: 10.15838/esc.2022.1.79.5

DOI: 10.15838/esc.2022.1.79.5

Abstract   |   Authors   |   References
  1. Adams J., White G., Araujo R. (2021). The role of mistrust in the modelling of opinion adoption. Journal of Artificial Societies and Social Simulation, 24(4), 4. Available at: http://jasss.soc.surrey.ac.uk/24/4/4.html. DOI: 10.18564/jasss.4624
  2. Atkinson K., Bench-Capon T. (2016). Value based reasoning and the actions of others. Frontiers in Artificial Intelligence and Applications, 285, 680–688. DOI: 10.3233/978-1-61499-672-9-680
  3. Ceragioli F., Frasca P. (2018). Consensus and disagreement: The role of quantized behaviors in opinion dynamics. SIAM Journal on Control and Optimization, 56, 1058–1080. DOI: 10.1137/16M1083402
  4. Chen Z., Lan H. (2021). Dynamics of public opinion: Diverse media and audiences’ choices. Journal of Artificial Societies and Social Simulation, 24(2), 8, Available at: http://jasss.soc.surrey.ac.uk/24/2/8.html. DOI: 10.18564/jasss.4552
  5. Cranefield S., Winikoff M., Dignum V., Dignum F. (2017). No pizza for you: Value-based plan selection in BDI agents. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence Main Track. DOI: 10.24963/ijcai.2017/26
  6. Davydov S.G. (2021). Digital compitencies of Russians and work on self-isolation during the COVID-19 pandemic. Monitoring obshchestvennogo mneniya: ekonomicheskie i sotsial'nye peremeny=Monitoring of Public Opinion: Economic and Social Changes, 2, 403–422. DOI: 10.14515/monitoring.2021.2.1913 (in Russian).
  7. Deffuant G., Neau D., Amblard F., Weisbuch G. (2000). Mixing beliefs among interacting agents. Advances in Complex Systems, 3, 87–98.
  8. Dong Y., Ding Z., Chiclana F., Herrera-Viedma E. (2021). Dynamics of public opinions in an online and offline social network. IEEE Transactions on Big Data, 7(4), 610–618. DOI: 10.1109/TBDATA.2017.2676810
  9. Fishbein M., Azjen I. (2011). Predicting and Changing Behavior: The Reasoned Action Approach. New York: Psychology Press.
  10. Hegselmann R., Krause U. (2002). Opinion dynamics and bounded confidence models, analysis, and simulation. Journal of Artificial Societies and Social Simulation, 5(3).
  11. Hu H., Zhu J.J. (2017). Social networks, mass media and public opinions. Journal of Economic Interaction and Coordination, 12(2), 393–411. DOI: 10.1007/s11403-015-0170-8
  12. Iliycheva L.E., Kondrashov A.O., Lapin A.V. (2021). Trust as a bridge over the uncertainty gap between the government and society. Monitoring obshchestvennogo mneniya: ekonomicheskie i sotsial'nye peremeny=Monitoring of Public Opinion: Economic and Social Changes, 2, 162–185. DOI: 10.14515/monitoring.2021.2.1917 (in Russian).
  13. Jiao Y., Li Y. (2021). An active opinion dynamics model: The gap between the voting result and group opinion. Information Fusion, 65, 128–146. DOI: 10.1016/j.inffus.2020.08.009
  14. Lapin N.I. (2010). Functional-oriented clusters of basic values of the population of Russia and its regions. Sotsiologicheskie issledovaniya=Sociological Studies, 1, 28–36 (in Russian).
  15. Makarov V.L., Bakhtizin A.R. (2013). Sotsial’noe modelirovanie – novyi komp’yuternyi proryv (agent-orientirovannye modeli) [Social Modeling – a New Computer Breakthrough (Agent-Oriented Models)]. Moscow: Ekonomika.
  16. Mareeva S.V. (2013). Dynamics of norms and values of Russians. Sotsiologicheskie issledovaniya=Sociological Studies, 7, 120–130 (in Russian).
  17. Mareeva S.V. (2015). Values in modern Russian society. Monitoring obshchestvennogo mneniya: ekonomicheskie i sotsial’nye peremeny=Monitoring of Public Opinion: Economic and Social Changes, 4, 50–65 (in Russian).
  18. Martins A.C.R. (2013). Trust in the CODA model: Opinion dynamics and the reliability of other agents. Physics Letters A, 377(37), 2333–2339. DOI: 10.1016/j.physleta.2013.07.007
  19. Mashkova A.L., Dembovskii I.A. Novikova E.V. (2019). Formation of the consumer strategy of households in the agent model of sectoral development of the Russian economy. Iskusstvennye obshchestva=Artificial Societies, 14(3) (in Russian).
  20. Mashkova A.L., Nevolin I.V., Savina O.A. et al. (2020). Generating social environment for agent-based models of computational economy. In: Chugunov A., Khodachek I., Misnikov Y., Trutnev D. (Eds). Electronic Governance and Open Society: Challenges in Eurasia. EGOSE 2020, 1349, 291–305. DOI: 10.1007/978-3-030-67238-6_21
  21. Mashkova A.L., Novikova E.V., Savina O.A. et al. (2020). Simulating budget system in the agent model of the Russian Federation spatial development. In: Chugunov A., Khodachek I., Misnikov Y., Trutnev D. (Eds). Electronic Governance and Open Society: Challenges in Eurasia. EGOSE 2019, 1135, 17–31. DOI: 10.1007/978-3-030-39296-3_2
  22. Mercuur R., Dignum V., Jonker C. (2019). The value of values and norms in social simulation. Journal of Artificial Societies and Social Simulation, 22(1), 9, Available at: http://jasss.soc.surrey.ac.uk/22/1/9.html. DOI: 10.18564/jasss.3929
  23. Novikova T.S., Tsyplakov A.A. (2020). Social policy in a multi-regional agent-based model. Ekonomicheskie i sotsial’nye peremeny: fakty, tendentsii, prognoz=Economic and Social Changes: Facts, Trends, Forecast, 13(3), 129–142. DOI: 10.15838/esc.2020.3.69.9 (in Russian).
  24. Pineda M., Buendía G. (2015). Mass media and heterogeneous bounds of confidence in continuous opinion dynamics. Physica A: Statistical Mechanics and Its Applications, 420, 73–84. DOI: 10.1016/j.physa.2014.10.089
  25. Poel I.V., Royakkers L.M. (2011). Ethics, Technology, and Engineering: An Introduction. Hoboken, NJ: John Wiley & Sons.
  26. Quattrociocchi W., Conte R., Lodi E. (2011). Opinions manipulation: Media, power and gossip. Advances in Complex Systems, 14(04), 567–586. DOI: 10.1142/S0219525911003165
  27. Rogers E.M. (2003). Diffusion of Innovations, 5th Edition. New York, NY: Free Press.
  28. Stauffer D. (2002). Sociophysics: The Sznajd model and its applications. Computer Physics Communications, 46(1), 93–98.
  29. Suslov V.I., Domozhirov D.A., Ibragimov N.M. et al. (2016). Agent-based multiregional input-output model of the Russian economy. Ekonomika i matematicheskie metody=Economics and Mathematical Methods, 52(1), 112–131 (in Russian).
  30. Sznajd-Weron K., Sznajd J. (2000). Opinion evolution in closed community. International Journal of Modern Physics C, 11(6), 1157–1165.
  31. Waigeng Y., Morev M.V., Ukhanova Yu.V., Kosygina K.E. (2021). The effectiveness of the authorities’ activities at the local level in the COVID-19 pandemic (the experience of Russia and China). Ekonomicheskie i sotsial’nye peremeny: fakty, tendentsii, prognoz=Economic and Social Changes: Facts, Trends, Forecast, 14(4), 231–250. DOI: 10.15838/esc.2021.4.76.14 (in Russian).
  32. Weisbuch G., Deffuant G., Amblard F., Nadal J.-P. (2002). Meet, discuss, and segregate! Complexity, 7(3), 55–63. DOI:10.1002/cplx.10031
  33. Zhang A., Zheng M., Pang B. (2018). Structural diversity effect on hashtag adoption in Twitter. Physica A: Statistical Mechanics and Its Applications, 493, 267–275. DOI: 10.1016/j.physa.2017.09.075
  34. Zino L., Ye M., Cao M. (2020). A two-layer model for coevolving opinion dynamics and collective decision-making in complex social systems. Chaos: An Interdisciplinary Journal of Nonlinear Science, 30(8), 083107. DOI: 30.083107.10.1063/5.0004787

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