  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.
