Editing Collaborative intelligence (section)

{{distinguish|collective intelligence}}
{{Group intelligence}}
'''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 computing|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.<ref>{{cite book | chapter-url=https://dl.acm.org/doi/10.1145/2212776.2212794 | doi=10.1145/2212776.2212794 | chapter=User-driven collaborative intelligence | title=CHI '12 Extended Abstracts on Human Factors in Computing Systems | date=2012 | last1=Gill | first1=Zann | pages=161–170 | isbn=9781450310161 | s2cid=15027953 }}</ref>

Four related terms are complementary:
* [[Collective intelligence]] processes input from a large number of anonymous responders to quantitative questions to produce better-than-average [[prediction]]s.
* [[Crowdsourcing]] distributes [[microtask]]s to a large number of anonymous task performers.
* [[Human computation|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 [[sensor]]s to geo-located devices to identified unique human contributors, drive collaborative problem-solving in next generation [[social network]]s.