Automated Social Science: Language Models as Scientist and Subjects (2404.11794v2)

Abstract: We present an approach for automatically generating and testing, in silico, social scientific hypotheses. This automation is made possible by recent advances in LLMs (LLM), but the key feature of the approach is the use of structural causal models. Structural causal models provide a language to state hypotheses, a blueprint for constructing LLM-based agents, an experimental design, and a plan for data analysis. The fitted structural causal model becomes an object available for prediction or the planning of follow-on experiments. We demonstrate the approach with several scenarios: a negotiation, a bail hearing, a job interview, and an auction. In each case, causal relationships are both proposed and tested by the system, finding evidence for some and not others. We provide evidence that the insights from these simulations of social interactions are not available to the LLM purely through direct elicitation. When given its proposed structural causal model for each scenario, the LLM is good at predicting the signs of estimated effects, but it cannot reliably predict the magnitudes of those estimates. In the auction experiment, the in silico simulation results closely match the predictions of auction theory, but elicited predictions of the clearing prices from the LLM are inaccurate. However, the LLM's predictions are dramatically improved if the model can condition on the fitted structural causal model. In short, the LLM knows more than it can (immediately) tell.

• The paper introduces a framework using language models and structural causal models to automate social science research, enabling hypothesis generation and testing through simulations.
• The methodology involves using LLMs to generate hypotheses, construct simulated agents, design experiments, execute simulations, and analyze data to fit structural causal models.
• This automated approach has the potential to accelerate social science discovery and improve reproducibility by providing a scalable and easily replicable experimental framework.

Automated Social Science: LLMs as Scientist and Subjects

The paper by Benjamin S. Manning, Kehang Zhu, and John J. Horton explores an innovative approach to leveraging the potential of LLMs in automating social science research. By integrating these models with structural causal models (SCMs), the authors offer a framework that not only generates hypotheses but also tests them through simulated experiments. This automation is significant for advancing our ability to conduct rapid, scalable research in social sciences.

The central premise relies on using structural causal models as a backbone to organize and automate the process of hypothesis generation and testing. SCMs offer a mathematically precise way to define causal relationships, thus facilitating the construction of experimental designs that can be efficiently simulated with LLM-based agents.

Methodology

The methodological innovation of this research lies in structuring the simulations as a sequence of steps that mirror traditional social science research:

1. Hypothesis Generation: The system uses LLMs to generate potential causes and outcomes within a given domain, effectively building an SCM for the hypothesis.
2. Agent Construction: Agents are designed with relevant attributes based on the SCM, enabling them to simulate realistic roles in social scenarios.
3. Experimental Design: The attributes of these agents are varied systematically to mimic the effect of different treatments in traditional experiments.
4. Simulation Execution: The simulated interactions are governed by pre-defined protocols for conversational turn-taking, which are intelligently selected based on the scenario.
5. Data Collection and Analysis: Post-experimental surveys measure the outcomes, and the data gathered are used to fit the SCM, allowing for rigorous analysis of the causal paths.

The authors demonstrate this approach through several scenarios, such as bargaining, bail hearings, job interviews, and auctions, thereby highlighting the versatility and robustness of their method.

Insights and Results

The research presents compelling results from the experiments. For instance, the auction scenario validated theoretical predictions well, pointing to the potential of LLM simulations to capture human-like decision-making processes. The approach also revealed that while LLMs could predict the direction of effects, they struggled with estimating effect magnitudes without fitted SCMs. This gap accentuates the importance of structured experimentation to harness the latent knowledge within LLMs effectively.

In terms of the pathways to causal inference, the paper underscores the need for precise causal models, as reliance on observational data or unrestricted simulations can lead to misidentification of causal effects. The SCM-based framework mitigates this by enforcing clarity and structure in experimental design.

Implications and Future Directions

The implications of this research are manifold. Practically, it points to a future where automated systems could conduct hypothesis generation and testing at scale, greatly accelerating the pace of discovery in social sciences. Theoretically, it raises questions about the extent to which LLM-driven simulations can replace traditional human-based experiments, especially in capturing complex social behaviors.

The system's flexibility to incorporate human intervention at any stage of the process ensures that it can serve as a powerful tool for exploratory research while maintaining rigor. The ability to export and replicate these simulations with ease addresses ongoing challenges of reproducibility in social science research.

Future directions could entail optimizing the robustness of agent construction and interaction protocols, as well as integrating more complex causal frameworks within the SCMs. Furthermore, enhancing the system’s capacity to identify novel causal variables could lead to richer and more nuanced insights from automated experiments.

Overall, this paper represents a significant contribution to the field of computational social science, providing a sophisticated framework for automated hypothesis testing with profound potential to transform how social science research is conducted.