Econ Papers Daily / 期刊论文
将机器行为融入人类被试实验：一个用户友好型工具包及其在框架囚徒困境中的应用
Integrating machine behavior into human subject experiments: a user-friendly toolkit and an application to framed prisoner’s dilemmas
摘要
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.
中文摘要
大型语言模型（LLMs）具有深刻变革和丰富实验经济学研究的潜力。我们提出了一种新的软件框架"alter_ego"，该框架能够便捷地设计LLMs之间的实验，并将其整合到基于oTree平台、涉及人类被试的实验中。我们的工具包可在github.com/mrpg/ego免费获取。为进行说明，我们通过机器间交互以及人机交互的方式，运行了不同框架设定的囚徒困境实验。在纯机器处理组中，框架效应显著且与以往纯人类实验的预期结果相似；但当机器与人类被试交互时，框架效应则较弱且存在质性差异。