Teaming Up with Artificial Agents in Non-routine Analytical Tasks

Lorenzo Cominelli, Federico Galatolo, Caterina Giannetti, Cristiano Ciaccio, Felice Dell’Orletta, Philipp Chaposkvi and Giulia Venturi

Discussion Papers from Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy

Abstract: Using a real-life escape room scenario, we investigate how different levels of embodiment in artificial agents influence team performance and conversational dynamics in non-routine analytical tasks. Teams composed of either three humans or two humans and an artificial agent (a Box, an Avatar, and a hyper-realistic Humanoid) worked together to escape the room within a time limit. Our findings reveal that while human-only teams tend to complete all tasks more frequently, they also tend to be slower and make more errors. Additionally, we observe a non-linear relationship between the degree of agent embodiment and team performance, with a significant effect on conversational dynamics. Teams with agents exhibiting higher levels of embodiment display conversational patterns more similar to those occurring among humans. These results highlight the complex role that embodied AI plays in human-agent interactions, offering new insights into how artificial agents can be designed to support team collaboration in problem-solving environments.

Keywords: complex tasks; artificial agents; teamwork (search for similar items in EconPapers)
JEL-codes: C92 (search for similar items in EconPapers)
Date: 2024-11-01
New Economics Papers: this item is included in nep-ain and nep-hrm
Note: ISSN 2039-1854
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Persistent link: https://EconPapers.repec.org/RePEc:pie:dsedps:2024/314

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