Sharing electricity over money: Third-person perspectives on human-robot dictator game outcomes
Andreea Elena Potinteu, Nadia Said, Gerrit Anders, Markus Huff
- 发表年份
- 2025
- 引用次数
- 2
摘要
With the increasing integration of robots into various facets of human life, understanding prosociality towards robots emerges as a crucial research endeavor. Across two studies ( N = 117 and N = 310), we adopt a third-person perspective to investigate different outcomes of the dictator game paradigm. Participants were tasked with evaluating the shared allocation of distinct resources (Study 1: money, access to electricity; Study 2: money, access to electricity, food, and electronic tools). Results reveal that only ∼24% of responses, as averaged for the two studies, indicated a preference for non-sharing across all resource types and shared amounts. For Study 1 and 2, we find that people tend to agree to the sharing of access to electricity over money. Additionally, for Study 2 findings suggest a tendency towards greater sharing of electronic tools and lesser sharing of food, compared to access to electricity. Moreover, perceptions of robot trustworthiness corresponded to a preference for sharing higher resource quantities. This relationship was mediated by participant gender as well as robot related risk and opportunity perception. These findings provide valuable insights into the socio-cognitive mechanisms underlying human perceptions of sharing behaviors with robots. Our studies indicate an emerging societal expectation regarding the types and extent of resources to be shared with robots, thus contributing to the delineation of evolving social norms towards robots. Furthermore, the results shed light on the nuanced dynamics of human-robot interaction, enriching our comprehension of how humans navigate social exchanges with artificial agents. • Sharing with robots is influenced by the type of resource to be shared. • Some resources are potentially seen as useful for a robot and elicit higher sharing. • Sharing is mediated gender, robot risk perception, and trustworthiness.
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