Stimulating Self-Organization in Human-Machine Collective Intelligence Environment

Abstract

Decision-making in complex situations often requires significant human involvement and remains an activity that is performed jointly by humans and various computational (and AI) tools. This paper develops a concept of human-machine collective intelligence environment for decision support, leveraging self-organization of human-machine teams in decision-support scenarios. Specifically, the paper proposes a set of methods and mechanisms to stimulate self-organization in the human-machine collective intelligence environment: 1) a team-forming method, 2) a method of ensuring the development of the participants, contributing to the fulfillment of the condition of self-organization in the long term. Although the proposed methods and mechanisms are designed, first of all, for the environment being developed, we believe that they can be adapted and therefore are useful for a wide range of applications, supporting collaborative work on complex problems.