Abstract

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 …

Metadata

publication

International Conference on Human-Computer Interaction, 296-308, 2024

year

2024

publication date

2024/6/1

authors

Nikolos Gurney, Fred Morstatter, David V Pynadath, Adam Russell, Gleb Satyukov

link

https://link.springer.com/chapter/10.1007/978-3-031-60125-5_20

resource_link

https://arxiv.org/pdf/2402.13273

book

International Conference on Human-Computer Interaction

pages

296-308

publisher

Springer Nature Switzerland