Collaborative intelligence

Part of a series on
Collective intelligence
Concepts
Collaborative intelligence Collective wisdom Common sense Intelligence assessment
Background concepts
Collective
v t e

Collaborative intelligence is distinguished from collective intelligence in three key ways: First, in collective intelligence there is a central controller who poses the question, collects responses from a crowd of anonymous responders, and uses an algorithm to process those responses to achieve a (typically) "better than average" consensus result, whereas collaborative intelligence focuses on gathering, and valuing, diverse input. Second, in collective intelligence the responders are anonymous, whereas in collaborative intelligence, as in social networks, participants are not anonymous. Third, in collective intelligence, as in the standard model of problem-solving, there is a beginning, when the central controller broadcasts the question, and an end, when the central controller announces the "consensus" result. In collaborative intelligence there is no central controller because the process is modeled on evolution. Distributed, autonomous agents contribute and share control, as in evolution and as manifested in the generation of Wikipedia articles.

Collaborative intelligence characterizes multi-agent, distributed systems where each agent, human or machine, is autonomously contributing to a problem solving network. Collaborative autonomy of organisms in their ecosystems makes evolution possible. Natural ecosystems, where each organism's unique signature is derived from its genetics, circumstances, behavior and position in its ecosystem, offer principles for design of next generation social networks to support collaborative intelligence, crowdsourcing individual expertise, preferences, and unique contributions in a problem solving process.^[1]

Four related terms are complementary:

• Collective intelligence processes input from a large number of anonymous responders to quantitative questions to produce better-than-average predictions.
• Crowdsourcing distributes microtasks to a large number of anonymous task performers.
• Human Computation engages the pattern-recognizing capacities of anonymous human microtask workers to improve on machine capabilities and enable machine learning.
• Collaborative intelligence complements the three methods defined above, but here task performers are not anonymous. Task performers have different skills, motivations and may perform different tasks. These non-anonymous devices and human contributors, from tagged sensors to geo-located devices to identified unique human contributors, drive collaborative problem-solving in next generation social networks.