Will Scaling Improve Social Simulation with LLMs?
Large Language Model (LLM) social simulations are a promising research method, but they are not yet faithful enough to be adopted widely. In this work, we investigate whether the current scaling paradigm in language modeling is likely to close these gaps, or whether simulation fidelity is orthogonal to general capabilities and therefore deserving of more research attention. We use scaling laws to study the relationship between LLMs' compute scale, general capability benchmarks, and the fidelity of social simulation in three representative sub-domains: opinion modeling, behavioral simulation, and longitudinal forecasting. Surprisingly, we discover strong compute scaling in all three settin...
| Authors | Caleb Ziems, William Held, Su Doga Karaca et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 July 2026 |
| Last updated at source | 29 July 2026 |
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