Perceived Teammate Identity and Cooperative Behavior in Hybrid Human–Artificial Agent Teams

We study how perceived teammate identity shapes effort allocation in hybrid human–artificial agent teams in collaborative environments. In a real-effort online experiment and under different incentive schemes, fixed payment and team competition, participants work in a team where each team member is responsible for their own segment but can observe and intervene in teammates’ segments. Teammates are algorithmically controlled agents, framed as either artificial or human-like, with their actual behavior held constant across treatments. Participants exert approximately one-third less compensatory effort when teammates are presented with human-like cues than when they are transparently labeled as artificial, and this difference is robust across both incentive schemes. The pattern is consistent with a model in which perceived human identity reduces the non-monetary benefit of helping. The result highlights that in hybrid teams, the labeling and presentation of artificial collaborators have economically meaningful consequences for cooperative effort and the organization of team production.