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dc.contributor.authorBaltzersen, Rolf Kristian
dc.date.accessioned2022-09-25T21:52:52Z
dc.date.available2022-09-25T21:52:52Z
dc.date.issued2022
dc.identifier.urihttps://hdl.handle.net/11250/3021108
dc.descriptionChapter 2 in the book Cultural-historical perspectives on collective intelligence. In the era of digital communication, collective problem solving is increasingly important. Large groups can now resolve issues together in completely different ways, which has transformed the arts, sciences, business, education, technology, and medicine. Collective intelligence is something we share with animals and is different from machine learning and artificial intelligence. To design and utilize human collective intelligence, we must understand how its problem-solving mechanisms work. From democracy in ancient Athens, through the invention of the printing press, to COVID-19, this book analyzes how humans developed the ability to find solutions together. This wide-ranging, thought-provoking book is a game-changer for those working strategically with collective problem solving within organizations and using a variety of innovative methods. It sheds light on how humans work effectively alongside machines to confront challenges that are more urgent than what humanity has faced before. This title is also available as Open Access on Cambridge Core.en_US
dc.description.abstractChapter 2 describes crowdsourcing, a process where problems are sent outside an organization to a large group of people—a crowd—who can help provide solutions. Online citizen science and online innovation contests are of particular interest because of their societal value. Within innovation, the two selected examples are from IdeaConnection and Climate Co-lab, two innovation intermediaries who host different types of online innovation contests. One of these contests, the IdeaRalley, represents an interesting new crowdsourcing method that allows hundreds of experts to participate in a one-week long intensive idea building process. In online citizen science, Zooniverse (e.g. Galaxy Zoo) and Foldit, are selected as two prominent, but contrasting examples. The online protein folding game Foldit stands out as a particularly successful project that show what amateur gamers can achieve. The game design combines human visual skills with computer power in solving protein-structure prediction problems by constructing three-dimensional structures. Most successful solutions are team performances or achievements made by the entire Foldit gaming community. All the examples in this chapter illustrate successful case stories, and the detailed analysis identify basic problem-solving mechanisms in crowdsourcing.en_US
dc.language.isoengen_US
dc.publisherCambridge University Pressen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.no*
dc.subjectCitizen scienceen_US
dc.subjectOnline innovation contestsen_US
dc.subjectCreative crowd communityen_US
dc.subjectCrowd competitionen_US
dc.subjectLarge group problem solvingen_US
dc.subjectTransparencyen_US
dc.subjectCrowdsourcing skillsen_US
dc.subjectCitizen science gamesen_US
dc.subjectZooniverseen_US
dc.subjectFolditen_US
dc.subjectIdeaRallyen_US
dc.subjectClimate CoLaben_US
dc.titleCrowdsourcingen_US
dc.typeChapteren_US
dc.description.versionpublishedVersionen_US
dc.rights.holderRolf K. Baltzersenen_US
dc.source.pagenumberpp 27-49en_US
dc.identifier.doi10.1017/9781108981361.002


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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
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