Pairit：一个用于人机协作实时实验的平台
Pairit: A Platform for Live Experiments on Human-AI Collaboration
- Carey Business School, Johns Hopkins University（约翰斯·霍普金斯大学凯瑞商学院）
- Sloan School of Management, Massachusetts Institute of Technology（麻省理工学院斯隆管理学院）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
Pairit是一个在线平台，通过YAML配置声明可执行实验图，支持实时人机协作实验的设计、部署与共享，并已通过多项同行评审研究验证其可行性。
中文摘要 AI 辅助
在人工智能时代，组织设计需要能够测试人机群体如何协调、委派和做出决策的实验方法。可编程平台能够协调实时的人与人会话或实时人机聊天，但研究人员难以在单一可审计配置中声明既包含AI参与者通信又包含其对共享工作采取行动的实验协议。在此，我们介绍Pairit，一个在线平台，旨在促进测试人机组织设计和干预措施的实验的设计、测试和部署。通过单个YAML配置文件，研究人员声明一个可执行的实验图（页面、路由、随机化、匹配、聊天、共享工作区、服务器托管的智能体、调查、计时器和自定义HTML组件），并在实时会话中组合任意数量的人类和AI智能体。我们通过多次实时部署验证了该平台的可行性，包括同行评审的已发表研究，捕获了实时人机二元组中通信、谈判和协作工作的高分辨率过程痕迹。通过将复杂的交互协议表示为标准化、可审计的配置文件，Pairit为指定、部署和共享实时人机组织实验提供了可复用的基础设施。
英文摘要
Organizational design in the era of artificial intelligence requires experimental methods that can test how human-AI groups coordinate, delegate, and make decisions. Programmable platforms coordinate live human-to-human sessions or real-time human-AI chat, but researchers cannot easily declare experiment protocols in which AI participants both communicate and act on shared work within one auditable configuration. Here we introduce Pairit, an online platform that facilitates the design, testing, and deployment of experiments that test human-AI organizational designs and interventions. Through a single YAML configuration file, researchers declare an executable experiment graph (pages, routing, randomization, matchmaking, chat, shared workspaces, server-hosted agents, surveys, timers, and custom HTML components) and combine any number of humans and AI agents in live sessions. We have validated the feasibility of the platform through multiple live deployments, including peer-reviewed published studies, capturing high-resolution process traces of communication, negotiation, and collaborative work in live human-AI dyads. By representing complex interactive protocols as standardized, auditable configuration files, Pairit provides reusable infrastructure for specifying, deploying, and sharing live human-AI organizational experiments.
