Humans interact with a variety of forms of AI (Large Language Models (LLMs), algorithms, etc.) across many domains, and the frequency of these interactions is expected to increase. Their impact on human behavior and markets crucially depends on how humans perceive algorithms and interact with them. Evidence is needed to evaluate these impacts and understand the fundamental mechanisms that drive human decision-making when interacting with machines. While there is growing evidence of how individuals interact with algorithms (e.g. Burton et al., 2020; Chugonova and Sele, 2022; Bayer and Renou, 2024; Dargnies et al., 2024), many open questions remain, and these questions are changing as LLMs are becoming more broadly used (Capraro et al., 2024).

In the field, the parameters determining the ‘behavior of AI’ are often hidden. Experiments are an ideal tool for controlling these parameters and environments and an important part of the evidence needed to evaluate the impact of algorithms and LLMs on human behavior.

This special issue of Experimental Economics aims to bring together new evidence on how humans interact with algorithms, LLMs and related technologies, and how these interactions can help us better understand fundamental features of human behavior. We invite manuscripts studying all aspects of human-AI interactions. These manuscripts can include new evidence, based on laboratory, online, or (artefactual) field experiments, or be meta-analyses and surveys of this rapidly growing literature.

Important Dates:

Submission Opening: July 15, 2025.

Submission Deadline: January 15, 2026.

Guest Editors

Yan Chen (University of Michigan)

Daniel Martin (University of California, Santa Barbara)

Qiazhou Mei (University of Michigan)

Juanjuan Meng (Peking University)

Hans-Theo Normann (University of Dusseldorf)

References

Bayer, R.-C. and Renou, L. (2024). Interacting with Man or Machine: When Do Humans Reason Better. Management Science, forthcoming.

Burton, J. W., Stein, M. K., and Jensen, T. B. (2020). A systematic review of algorithm aversion in augmented decision making. Journal of behavioral decision making, 33(2), 220-239.

Capraro, V., Lentsch, A., Acemoglu, D., Akgun, S., Akhmedova, A., Bilancini, E., Bonnefon, J.F., Brañas-Garza, P., Butera, L., Douglas, K.M. and Everett, J.A., (2024). The impact of generative artificial intelligence on socioeconomic inequalities and policy making. PNAS nexus, 3(6).

Chugunova, M. and Sele, D. (2022). We and It: An interdisciplinary review of the experimental evidence on how humans interact with machines. Journal of Behavioral and Experimental Economics 99, 101897.

Dargnies, M.-P., Hakimov, R., and Kuebler, D. (2024), Aversion to Hiring Algorithms: Transparency, Gender Profiling, and Self-Confidence. Management Science (articles in advance)