A Summary of MIT & Harvard's 'Automated Social Science: Language Models as Scientist and Subjects' Available at: https://arxiv.org/abs/2404.11794 This summary is AI generated, however the creators of the AI that produces this summary have made every effort to ensure that it is of high quality. As AI systems can be prone to hallucinations we always recommend readers seek out and read the original source material. Our intention is to help listeners save time and stay on top of trends and new discoveries. You can find the introductory section of this recording provided below... This is a summary of "Automated Social Science: Language Models as Scientist and Subjects" published on April 19, 2024, by authors Benjamin S. Manning, Kehang Zhu, and John J. Horton from MIT and Harvard, with Horton also associated with NBER. In this paper, the authors explore the methodology for generating and testing social science hypotheses through automation, leveraging the advancements in large language models (LLMs) and structuring the process around structural causal models. The crux of this research lies in applying structural causal models not merely as theoretical frameworks but as actionable guides for creating LLM-based agents, designing experiments, and analyzing data. This application facilitates the automation of hypothesis generation and the testing thereof in a controlled, simulated environment. Through this innovative use of LLMs, the research team has attempted to expand the capacity for empirical investigation in the social sciences, enabling a more efficient and diverse examination of hypothesized causal relationships. The paper details experiments across various social scenarios including negotiations, bail hearings, job interviews, and auctions, to test hypotheses generated by the system. The results from these simulations demonstrate the system's potential in capturing and analyzing complex causal relationships, with many outcomes aligning with existing theories or empirical observations. An intriguing finding across the experiments was the significant improvement in the LLM's predictive ability when it could access the fitted structural causal model, suggesting that LLMs contain a wealth of latent information about social processes that can be harnessed more effectively with the right methodological approach. The authors argue that the use of LLMs, coupled with structural causal models, opens up new avenues for the automated exploration of social science, offering insights that may not be readily accessible through traditional hypothesis testing or direct elicitation from LLMs. Despite the results aligning with known theories and observations, the authors posit that the automation of hypothesis testing and the empirical verification of the results underscore the value of their approach in enhancing our understanding of social dynamics. Manning, Zhu, and Horton's work contributes to the broader discourse on the potential of LLMs in scientific research, presenting a compelling case for the integration of these technologies in hypothesis-driven investigations. Their framework for automated social science research not only underscores the evolving role of machine learning in empirical inquiry but also highlights the ongoing need for innovative methods in harnessing the capabilities of advanced computational models for social science research.