Collaborative intelligence IS
founded in the study of how novelty originates. Through study of principles manifest in the origin and evolution of life, and emergence of intelligence, principles of collaborative intelligence are revealed that overturn traditional consensus-seeking and goal-setting models for problem-solving, offering a systematic method to guide multi-agent problem-solving systems toward coherent outcomes when goals cannot be stated in advance (as in innovation). Application of collaborative intelligence principles can make cross-disciplinary design teams more effective.

Collective intelligence processing methods maintain the traditional anonymity of survey responders, collecting and aggregating input of many anonymous discrete responders to specific, generally quantitative, questions. After homogenizing input from anonymous participants, that input is processed to generate a better-than-average prediction (generally quantitative).

Collaborative intelligence shifts from the anonymity of collective intelligence to acknowledged identity, as when individuals participate in social networks. Harnessing the collaborative intelligence of diverse participants requires better systems for semantic analysis, with capacity to cluster and link related concepts, visualise work-in-progress, tag user profiles, and credit individual contributions. A knowledge processing system that enables users to share information and opinions can process qualitative input. Diverse, generally non-anonymous, credited, time-stamped input into an interactive system is tagged, preserving a database of the unique knowledge, expertise, and priorities of participants, while offering diverse methods of clustering, searching, and accessing their input.

This website surveys theoretical work relevant to developing a theory of collaborative intelligence and coherent body of knowledge on this subject, research spanning cognitive science, and computing; Earth systems science and evolutionary theory; and design science and game theory. To define the principles of collaborative intelligence requires identifying intersections with theory in cognate fields.

A method to guide processes that require collaborative intelligence is described, together with tools, such as evolvable templates, problem-maps, and online process tracking to support that method. Collaborative intelligence characterises the attributes of a distributed group mind at peak performance in solving creative problems, the dynamics that occur when people from different disciplines and institutions with diverse skills, agendas and priorities produce outcomes that a majority of participants and stakeholders in the process view as more effective than what independent individuals, or single discipline groups, could have produced alone.

Collaborative autonomy is the principle underpinning collaborative intelligence through which individual contributors maintain their roles and priorities as they apply their unique skills and leadership autonomy in a problem-solving process. Individuals are not homogenized, as in consensus-driven processes, nor equalized through quantitative data processing, as in collective intelligence. Consensus is not required. Problem resolution is achieved through systematic convergence toward coherent results.

Innovation (or Knowledge) Networks link participants, while maintaining their uniqueness and collaborative autonomy such that knowledge can evolve as networks grow, with potential for emergent, unpredictable patterns and innovative outcomes.

Problem mapping a priori, in contrast to information visualisation after-the-fact, generates visual frameworks, or “empty constructs” to structure the process of knowledge-gathering. Problem maps can evolve into navigable user interfaces. These open frameworks (partial patterns) tap the pattern recognition capabilities of users, serving as vehicles to order incoming information in process, and for use by participants during the problem-solving process. A classic example of a problem map is Dmitri Mendeleev’s Periodic Table of Elements, which prompted chemists to look for elements that appeared logically likely to exist, based upon the pattern of the Table.

Situation architecture addresses the key factor of contextualization. Meaning is interpreted in context and may differ, not only with different interpreters, but in different contexts.

Synergy is whole system behavior that cannot be predicted from the behavior of its parts.
Synergetics is the dynamics through which synergies are produced; both synergy and synergetics were key principles in the work of Buckminster Fuller.