在人-机器人和人-人网络中以声誉为基础的互惠
Ashley Harrell1, Margaret L Traeger2
1Department of Sociology, Duke University, Durham, NC 27705, USA.
PNAS nexus
|May 19, 2025
概括
基于声誉的互惠 (RBR) 在促进人机混合系统中的慷慨不那么有效. 当人们意识到机器人时,人们不那么慷慨,改变了帮助的社会规范.
科学领域:
- 社会心理学 社会心理学
- 人与计算机的交互
- 行为经济学是一种行为经济学.
背景情况:
- 基于声誉的互惠 (RBR) 激励人类网络中的帮助行为.
- 混合系统整合了人类和人工智能 (AI),创造了新的社会动态.
- 了解RBR在人机交互中的有效性对于设计这些系统至关重要.
研究的目的:
- 调查RBR是否在人机网络中促进慷慨,而不是仅限于人机网络.
- 检查机器人的存在如何影响RBR系统中的人类亲社会行为和规范感知.
主要方法:
- 进行了两项实验:一项是间接互惠网络和已知/未知的机器人存在,另一项是一次性双向的人机交互.
- 行为观测和慷慨的评估记录在网络和双向设置中.
- 分析了参与者的帮助意愿和他们的惩罚/奖励行为.
主要成果:
- 在混合人机器人系统中,RBR在促进慷慨方面效果明显较差.
- 当参与者意识到网络中的机器人时,他们表现出较低的慷慨,这归因于改变了帮助规范.
- 在二元互动中,人们不太愿意帮助机器人,即使有声誉激励,并且更严厉地判断那些帮助机器人的人.
结论:
- 机器人的存在降低了RBR在促进社会行为方面的有效性.
- 人类对帮助机器人的规范并不像对其他人类的规范一样.
- 混合系统需要仔细考虑社会规范的适应,以保持亲社会性.
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