Collaborative intelligence principles are derived from study of how novelty originates, e.g. in the origin and evolution of life and the emergence of intelligence. Beyond arguments that “collaboration is ethically desirable,” collaborative intelligence is a set of principles that characterise how evolution advances toward increased functional effeciveness. If we, the human species, aim to continue the constructive acceleration of evolution, we must understand these principles in order to survive.

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. Collaborative intelligence offers a method to transform next generation social networks into problem-solving systems. 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.

Harnessing the collaborative intelligence of diverse participants requires better systems for semantic analysis, with capacity to cluster and link related concepts, visualize work-in-progress, tag user profiles, and credit individual contributions on the critical path of the problem-solving process. A knowledge processing system that enables users to share information and opinions can process qualitative input.

Principles of collaborative intelligence revealed by living systems overturn traditional consensus-seeking and goal-setting models of problem-solving, comprising a systematic method to guide multi-agent problem-solving systems (humans and their extensions) toward coherent outcomes when goals cannot be stated in advance, as in probems requiring innovation.

Collaborative intelligence characterizes the attributes of cross-disciplinary problem-solving teams — a distributed group mind at peak performance in solving creative problems, 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. A method to guide processes that require collaborative intelligence can benefit from tools, such as evolvable templates, problem-maps, online process tracking, improved search, visualization, and decision support.

Collaborative autonomy supplies the critical link between the A-PR Hypothesis and collaborative intelligence. The A-PR Hypothesis asserts two key capacities of life (Autonomy and Pattern Recognition) together make evolution toward increased functionality possible. 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. Collaborative intelligence relies on the principle of collaborative autonomy to overcome “the consensus barrier” to succeed where other methods have failed.

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.

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