Can ABMs model pension policy impacts?
An Agent-based Model Simulation Approach to Demonstrate Effects of Aging Population and Social Service Policies on Pensions Fund and Its Long-term Socio-economic Consequences
This research uses a multi-agent model to simulate the impacts of aging populations and social service policies on Iran's pension fund solvency. It explores the interaction between individual agents (citizens) with varying attributes (vision, metabolism, lifespan) and their environment (sugar resource, representing wealth and income).
Key points relevant to LLM-based multi-agent systems include: heterogeneity of agents (diverse characteristics and behaviors), autonomous decision-making based on local information (gradient search algorithm), and the emergence of macro-level patterns (pension fund solvency, GDP, Gini coefficient) from micro-level interactions. The model's flexibility in incorporating various parameters (social services, productivity decay, genetic distributions) highlights the potential of multi-agent systems for simulating complex scenarios and exploring policy impacts in dynamic environments. The observed cyclical patterns in some key indicators resonate with the concept of secular cycles in socioeconomics, offering potential avenues for LLM-based prediction and analysis.