We explore the use of aggregative crowdsourced forecasting (ACF) as a mechanism to help operationalize ``collective intelligence'' of human-machine teams for coordinated actions. We adopt the definition for Collective Intelligence as: ``A property of groups that emerges from synergies among data-information-knowledge, software-hardware, and individuals (those with new insights as well as recognized authorities) that enables just-in-time knowledge for better decisions than these three elements acting alone.'' Collective Intelligence emerges from new ways of connecting humans and AI to enable decision-advantage, in part by creating and leveraging additional sources of information that might otherwise not be included. Aggregative crowdsourced forecasting (ACF) is a recent key advancement towards Collective Intelligence wherein predictions (X\% probability that Y will happen) and rationales (why I believe it is this probability that X will happen) are elicited independently from a diverse crowd, aggregated, and then used to inform higher-level decision-making. This research asks whether ACF, as a key way to enable Operational Collective Intelligence, could be brought to bear on operational scenarios (i.e., sequences of events with defined agents, components, and interactions) and decision-making, and considers whether such a capability could provide novel operational capabilities to enable new forms of decision-advantage.

翻译：我们探索将聚合众包预测（ACF）作为一种机制，以帮助实现人机团队协调行动的"集体智能"操作化。我们采用集体智能的定义："一种群体属性，源于数据-信息-知识、软件-硬件以及个体（包括具有新见解者和公认权威）之间的协同作用，能够提供即时知识，从而比这三个要素单独作用时做出更优决策。"集体智能通过连接人类与人工智能的新方式产生决策优势，部分途径是创建并利用原本可能被遗漏的额外信息源。聚合众包预测（ACF）是近期迈向集体智能的关键进展，该方法独立地从多元化群体中收集预测（Y事件发生概率为X%）及理由（为何我认为X%概率会发生），经聚合后用于支撑更高层次的决策。本研究探究ACF作为实现操作集体智能的关键途径，能否应用于操作场景（即包含特定主体、组件及交互的事件序列）与决策过程，并思考此类能力是否可提供新型操作能力以实现新型决策优势。