In one implementation of the Tripod Learning Model, each student is asked to choose three thinkers and to imagine what these thinkers might have produced if they had collaborated. In contrast to traditional rote learning, where students memorize concepts of thought leaders to regurgitate at examination, this exercise asks them to choose thinkers who inspire them, and whom they believe could have produced significant work if they had collaborated.

In order to imagine what their chosen thinkers might have said or produced if they had collaborated, students must know the work of these thinkers in more depth than if they simply memorised their work or analysed their writings. Some students will identify three thinkers whose juxtaposition produces significant innovation; others will be less successful. But the task alone, independent of its outcome, will teach all students that synthesis can be as systematic and fruitful as analysis.

For example, the Tripod Learning Model for collaborative intelligence can be illustrated by examining the complementarity of the work of Irving Janis, James Lovelock, and Buckminster Fuller, and the significant recent domains of research that have grown from, or relate to, their work. At the intersection of the three disciplines that these three thinkers represent lie concepts that underpin collaborative intelligence: Cognitive Science was the field of Irving Janis (1918 – 1990), whose research on “groupthink” characterised how committees degenerate to lowest-common-denominator results. Earth Systems Science is a field inspired by James Lovelock (1919 – based in Devon, UK), who proposed “The Gaia Hypothesis.” Design Science is a new field defined by Buckminster Fuller (1895 – 1983), who proposed the concept for “World Game” in 1961 before technology existed to realize his concept.

These three thinkers were not collaborators and did not explore the intersecting ideas in their work. All three had detractors who argued against their work, as well as advocates who argued that their work has not been recognised to the degree that it deserves. The critique and resistance each encountered is instructive, not only for critical analysis, but also to understand cultural resistance to innovation. Janis, Lovelock, and Fuller proposed concepts so big that receiving less credit than they deserved was almost to be expected.

• What makes collaboration succeed or fail? (Janis)
• How did Earth and its life co-evolve? (Lovelock)
• How can humanity harness its collaborative brainpower to address sustainability challenges to survive on “Spaceship Earth”? (Fuller)

These three thinkers seeded new threads of inquiry that intersect to define collaborative intelligence. That intersection, implied in the complementary ideas of these three thinkers, is shown in the diagram.

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.

References
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