Integrating machine behavior into human subject experiments: a user-friendly toolkit and an application to framed prisoner’s dilemmas

Christoph Engel et al.

Experimental Economics2026https://doi.org/10.1017/eec.2025.10038article
AJG 3ABDC A*
Weight
0.50

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.

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1017/eec.2025.10038

Or copy a formatted citation

@article{christoph2026,
  title        = {{Integrating machine behavior into human subject experiments: a user-friendly toolkit and an application to framed prisoner’s dilemmas}},
  author       = {Christoph Engel et al.},
  journal      = {Experimental Economics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1017/eec.2025.10038},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Integrating machine behavior into human subject experiments: a user-friendly toolkit and an application to framed prisoner’s dilemmas

Flags are reviewed by the Arbiter methodology team within 5 business days.


Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.