Economic choice prediction is an essential challenging task, often
constrained by the difficulties in acquiring human choice data. Indeed,
experimental economics studies had focused mostly on simple choice settings.
The AI community has recently contributed to that effort in two ways:
considering whether LLMs can substitute for humans in the above-mentioned
simple choice prediction settings, and the study through ML lens of more
elaborated but still rigorous experimental economics settings, employing
incomplete information, repetitive play, and natural language communication,
notably language-based persuasion games. This leaves us with a major
inspiration: can LLMs be used to fully simulate the economic environment and
generate data for efficient human choice prediction, substituting for the
elaborated economic lab studies? We pioneer the study of this subject,
demonstrating its feasibility. In particular, we show that a model trained
solely on LLM-generated data can effectively predict human behavior in a
language-based persuasion game, and can even outperform models trained on
actual human data.

使用语言模型生成的数据能够有效地预测基于语言的说服游戏中的人类行为，并且在某些情况下超过基于实际人类数据训练的模型。