Integrating machine behavior into human subject experiments: a user-friendly toolkit and an application to framed prisoner’s dilemmas
提出一个名为alter_ego的软件框架，方便设计大型语言模型间的实验，并整合到基于oTree的人类被试实验中。通过框架囚徒困境实验展示其应用，发现机器间的框架效应与人类实验相似，但人机交互时效应减弱且性质不同。
Abstract Large Language Models (LLMs) have the potential to profoundly transform and enrich experimental economic research. We propose a new software framework, “alter_ego”, which makes it easy to design experiments between LLMs and to integrate LLMs into oTree-based experiments with human subjects. Our toolkit is freely available at github.com/mrpg/ego . To illustrate, we run differently framed prisoner’s dilemmas with interacting machines as well as with human-machine interaction. Framing effects in machine-only treatments are strong and similar to those expected from previous human-only experiments, yet less pronounced and qualitatively different if machines interact with human participants.