Design and Development of a Collaborative Augmented Reality Environment for Systems Science

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dc.contributor.authorGiabbanelli, Philippe
dc.contributor.authorShrestha, Anish
dc.contributor.authorDemay, Loic
dc.date.accessioned2023-12-26T18:36:16Z
dc.date.available2023-12-26T18:36:16Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2023.071
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.otherc52bd1f4-74f5-4596-8821-d218fbbad824
dc.identifier.urihttps://hdl.handle.net/10125/106446
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDesign, Development, and Evaluation of Collaboration Technologies
dc.subjectcausal maps
dc.subjectextended reality
dc.subjectmultiple views
dc.subjectparticipatory modeling
dc.subjectsystems thinking
dc.titleDesign and Development of a Collaborative Augmented Reality Environment for Systems Science
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractParticipatory modeling seeks to support a group in reaching a shared understanding of a complex system by identifying commonalities between individual views and navigating their differences. Traditional workshops where participants and facilitators are physically present face several barriers: complex systems span multiple domains hence they may involve experts living in various locations or rarely available together, while other stakeholder groups may struggle to access a workshop for logistical reasons such as time commitment or transportation costs. Switching to a fully remote environment through desktop applications alleviates some of these concerns but loses a sense of rapport, which can impair the collective learning experience normally fostered by systems thinking workshops. To address these limitations, we present the design and code implementation of a new augmented reality environment that aims to support remote participants in collectively arriving at a shared causal map. Our collaborative modeling application leverages graph-drawing algorithms, multiple synchronized views, and custom network protocols.
dcterms.extent10 pages
prism.startingpage589

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Design, Development, and Evaluation of Collaboration Technologies