arXiv:2505.14752v3 Announce Type: replace Abstract: Macro-aligned micro-records are crucial for credible simulations in social science and urban studies. For example, epidemic models are only reliable when individual-level mobility and contacts mirror real behavior, while aggregates match real-world statistics like case counts or travel flows. However, collecting such fine-grained data at scale is impractical, leaving researchers with only macro-level data. LLMSynthor addresses this by turning a pretrained LLM into a macro-aware simulator that generates realistic micro-records consistent with
The increasing sophistication of large language models makes them capable of synthesizing complex, nuanced data, an advancement not previously possible at this scale.
This development enables more credible and detailed simulations in social science and urban studies, critical for policy-making and understanding complex systems without relying on impractical data collection.
Researchers can now generate realistic micro-records from macro-level data, improving the reliability of models across various fields where fine-grained data was previously unattainable.
- · Social Science Researchers
- · Urban Planners
- · AI Model Developers
- · Policy Makers
- · Traditional survey methods reliant on extensive data collection
- · Simulation methodologies limited by data scarcity
Improved accuracy and resolution of social and urban simulations, leading to better predictive models.
New insights into complex societal behaviors and urban dynamics, informing more effective interventions and policies.
Potential for ethical debates around synthetic data generation, privacy, and the influence of simulated realities on public discourse.
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