population dynamics

This paper examines intergenerational transmission associated with population dynamics from sociological approach. The discussion is based on the analysis of observations in a country that has experienced substantial changes in family formation resulting in one of the world's most spectacular falls in women's birth rate ever experienced in human history: Iran. Facing fundamental historical experiences and substantial socio-cultural changes over the past decades, the context of this study acts as a unique ‘social laboratory’ to survey the intergenerational comparisons. The results of this analysis show substantial intergenerational transmission, which provide new evidence to support Sauvey's (1978) and Weeks's (1994) socio-demographic investigations in some other developing countries

Iran has undergone unique demographic changes in the recent decades. This paper aims to project the natural population growth rate -NPG over the next decade (2024–2034), which would offer a comprehensive perspective into the future of Iran's population dynamics. In this regard, to accomplish the above task, this work deals with the projection of most important demographic measures that characterize the population process, namely the Crude Birth Rate -CBR, the Crude Death Rate -CDR, and the Population Doubling Time- PDT. To this end, a deep neural network modeling approach was developed and applied. Forecasting with deep neural networks is one of the most important and influential techniques used in machine learning and artificial intelligence. The data-driven model, based on data obtained from the Statistical Center of Iran, was subsequently implemented for model development in MATLAB.Results from the paper indicate that the CBR drops from 11.3 per thousand in 2024 to 9.3 per thousand in 2034. On the other hand, the CDR increases from 5.2 per thousand in 2025 to 6.1 per thousand in 2034. With this effect, the NPG decreases from 6.1 per thousand in 2025 to 3.2 per thousand in 2034. Lastly, PDT for the population is forecasted to rise from 114 years in 2025 to 218 years in 2034.This study presents a deep neural network model for describing and forecasting the complex dynamics of population changes in Iran. Constructing this model helps policy-makers and planners use the forecasted population dynamics to design and implement programs and policies with greater precision.