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.
- ^ Gill, Zann (2012). "User-driven collaborative intelligence". CHI '12 Extended Abstracts on Human Factors in Computing Systems. pp. 161–170. doi:10.1145/2212776.2212794. ISBN 9781450310161. S2CID 15027953.