Last summer, my granddaughter was born. From the moment she entered the world, she began receiving a childcare allowance. We jokingly said that she had become a “government employee” at birth, with the state treasury continuously paying her a “salary.” My daughter-in-law’s prenatal examinations, delivery, and postpartum rehabilitation expenses were all covered by maternity insurance, and the family could also claim a special additional deduction on individual income tax. From birth, my granddaughter has been fully supported by the caring policies of the state and society. Behind all this, one technology has quietly been playing a role—population system simulation.

More than thirty years ago, my son was born. In the early 1990s, China’s family-planning policy was being strictly enforced. Childbearing was not simply a family choice, but a policy boundary that could not be crossed. After my son was born, all we received was a Beijing Honorary Certificate for Parents of an Only Child and a monthly only-child allowance of five yuan until he turned eighteen. Beyond that, there was almost no additional support from the state or society.

Thirty Years: From “Control” to “Care”

More than three decades have passed, and my granddaughter is growing up in a policy environment entirely different from the one her father experienced. The country has shifted comprehensively from “controlling births” to “encouraging births.” The three-child policy has been implemented, social maintenance fees have been completely abolished, and a series of measures supporting childbirth have been introduced. The policy rationale has moved from “restricting population size” to “optimizing population structure and creating a more fertility-friendly society.”

The contrast between the “policy restrictions” experienced by my son and the “policy support” enjoyed by my granddaughter could hardly be sharper. At its core, this profound transformation reflects a shift in population governance—from experience-based decision-making to data-driven analysis and intelligent simulation.

Simulation-Driven Decision-Making:

The “Intelligent Brain” Behind Policy Transformation

Since the beginning of the twenty-first century, China’s population structure has faced mounting challenges, including accelerated ageing, a declining labor supply, and persistently low fertility rates. Adjustments to fertility policy therefore became increasingly urgent. Population development is a complex systems project, and policy changes affect the country’s long-term future; they cannot be made blindly. Against this background, population system simulation—an important area of social simulation research—has become an “intelligent brain” supporting policy formulation.

This technology draws on multiple sources of data, including population censuses, vital statistics, fertility-intention surveys, and socioeconomic indicators. More than one hundred variables—such as birth rates, death rates, population age structure, and fertility intentions—are incorporated into refined simulation models. Computer simulations are then used to forecast how the population may evolve under different policy scenarios, providing quantitative evidence for policy decisions.

Supported by data modeling and simulation analysis, China has gradually adjusted its fertility policy: from the selective two-child policy to the universal two-child policy, and then to the three-child policy and a continually improving support system. The focus has shifted from population control toward building a fertility-friendly society. Policy-making itself has also moved away from reliance on experience and judgment toward scientific decisions based on data simulation and scenario analysis.

Watching my granddaughter learn to speak while looking back on my son’s childhood, I see how the sharply different childbearing experiences of two generations reflect both the development of the times and the modernization of China’s population-governance capacity. As an important tool in this transformation, population system simulation helps make public policy more precise, robust, and forward-looking. It also aligns population governance more closely with long-term development patterns, allowing every new life to grow up in a more supportive institutional environment.

In the future, as artificial intelligence and big-data modeling continue to advance, this model of “simulation-driven decision-making” will play a role across a broader range of social-governance fields. Technology will become more deeply embedded in governance systems and contribute to higher-quality development decisions.

END

About the Author

Qingwu

Faculty Member, Beihang University