Hi there 👋
Thanks for dropping by! I’m Arul Murugan, currently a masters student at UC Berkeley.
Published open access in Strategic Management Journal, this paper introduces a framework for using LLM-based AI agents as synthetic subjects in strategy research.
We estimate how labor markets in 141 countries are exposed to frontier AI, showing large cross-country variation, gender gaps, adoption correlations, and remittance-linked indirect exposure.
We compare prompting, sparse autoencoders, and linear probes for interpreting and controlling LLM agent behavior in social science simulations. SAE-based steering outperforms prompting, offering fine-grained, predictable control over preferences and capabilities.