Operational Collective Intelligence of Humans and Machines

TL;DR

This paper investigates how aggregative crowdsourced forecasting (ACF) can operationalize collective intelligence (CI) in human–machine teams to support coordinated actions. It defines Operational Collective Intelligence (OCI) as deploying ACF within defined operational scenarios—sequences of events with specific agents and interactions—and analyzes determinants of practicality, human factors, and machine intelligence factors. The authors outline a research program with questions on practicality determinants, human factors, machine factors, and potential OCI impacts, and discuss examples such as SAGE and MATRICS to illustrate possible performance gains. They argue that OCI could yield faster, more accurate decisions in adversarial contexts, but success depends on high-quality data, robust modeling, and well-calibrated trust in machine input.

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 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.