Socio-Cultural Strategy

Socio-Cultural Strategy

Innovative Population Policy Tools: Simulating the Impact of Fertility Preference Changes on Total Fertility Rate Using Computational Social Science Approaches

Document Type : Original Article

Authors
1 Assistant Professor of Demography, University of Tehran, Tehran, Iran.
2 Associate Professor of Demography, Faculty of Social Sciences, Tehran university, Tehran, Iran (Corresponding author).
Abstract
In this study, using the approach of computational social science, the impact of fertility preferences as a key factor on the fertility behavior is examined. Understanding the relationship between preferences and fertility behavior can play a significant role in formulating effective policies to increase fertility rates. The main objective of the research is to analyze and predict the relationship between changes in women's desired number of children and their fertility behavior in Tehran Province. In this regard, the agent-based modeling- ABM method is employed as a key technique in data analysis within the field of computational social science, allowing data from a ten-year period (period (2020–2029) in Tehran province to be analyzed and forecasted. Different scenarios were designed using the AnyLogic software, and their effects on the Total Fertility Rate (TFR) were systematically evaluated. In the first scenario, reducing women's fertility preferences from 2.5 children to 2 children, the TFR is projected to decrease from 1.3 in 2021 to 1.04 in 2028. In the second scenario, increasing the desired number of children to 4 leads to a slight rise in the TFR, reaching approximately 1.08 children by 2028.  The simulation results indicate a significant impact of changes in fertility preferences on the overall fertility trend. In computational social science, the purpose of simulating human and behavioral systems is to understand and extract the trends in behavioral-social patterns, rather than providing precise numerical predictions. This approach can be effective in forecasting demographic changes and evaluating the impact of population policies.
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  • Receive Date 09 December 2024
  • Revise Date 11 May 2025
  • Accept Date 14 May 2025