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Recruiting Collective Intelligence to Level
the Contemporary Art World’s Stratified
Distribution of Prestige and Value
How Online Collections Could Potentially Impact the Actual Art System
Dr. Stéphanie Bertrand
This presentation is part of a wider research project has received funding from
the European Union’s Horizon 2020 research and innovation programme under
the Marie Skłodowska-Curie grant agreement No 893454
Content warning
This presentation contains a Phillip Guston work
depicting hooded figures referencing the Ku Klux
Klan (Slide 19).
If you do not wish to view this content, please
look away for 20 seconds following Slide 18.
2
There are no objective criteria to
assess the value of any artwork
or the performance of its maker
3
4
Where there are no objective
criteria to evaluate performance,
networks drive success
5
ART WORLD CENTER
6
7
Art’s value depends on the
judgment of a small # of art world
experts who do not possess
unlimited capacity for discovery
8
Changing the makeup of the
network by recruiting the public’s
collective intelligence to make the
artworld more equal and diverse
9
10
11
12
13
Social influence has a strong
impact on artistic appreciation &
different reward systems on
crowdsourcing platforms
14
Social feedback can lead to 2
major issues: runaway inequality
& artificial manipulation
15
How can you minimize outside
influence to truly capture users’
unique perspective?
16
17
18
19
20
Images
Slide 4: Teaser of photos by Stefan Draschan from his People
Matching Artworks Series
Slide 6: (left) The Art Network (with presenter indication) and (right)
The Art Network, zoomed in on the central cluster with labels, both
Barabasi Lab Art, 2018
Slide 7: (from top left to bottom right) views of the facades of The
Whitney Museum of American Art, Centre Pompidou, Pace Gallery
New York, Solomon R. Guggenheim Museum, MoMA and Gagosian
Gallery London
Slide 10: (from top left to bottom center) Rijksstudio, Europeana, The
MONA’s The O App, The Baden State Museum’s Ping! App and
Smartify App
Slide 11: (left) Image of a Sotheby’s auction in May 2021 (right) Image
of Paris+ art fair 2022 (both sourced from the NYTimes)
Slide 12: (from top left to bottom right) Videoclip stills of The Carter’s
Apeshit, Katy Perry’s This Is How We Do, Rhianna’s Rude Boy and
Drake’s Hotline Bling
Slide 17: (left) Vivian Maier, Macy’s Parade, 1955 (right) Salvador Dali,
Le Sommeil (Sleep), 1937
Slide 18: (left) Pablo Picasso, Guernica, 1937 (right) Salvador Dali, Le
Sommeil (Sleep), 1937
Slide 19: (left) Philip Guston, Riding Around, 1969 (top right) Work by
Angela Deane from her Ghost Photograph series (center right)
Elizabeth Catlett, Black Unity, 1968 (bottom right) Barbara Kruger,
Untitled (You’ve got money to burn), 1988
References
Barabási, A.-L.. The Formula: The Universal Laws of Success. New
York: Little, Brown and Company, 2018.
Berger, J. Ways of Seeing. London: Penguin Books, 2008.
Bertrand, Stéphanie. “Curating Online Collections: Towards an
Authentically Digital, Mediation Protocol for Art Digitizations”. Journal
of Curatorial Studies 11, no. 1 (2022).
De Duve, Thierry. Kant After Duchamp, Cambridge MA: MIT Press,
1996.
Fraiberger S. P. et al. “Quantifying Reputation and Success in Art”.
Science 362, no. 6416. November 8 (2018), 825-829.
Franceschet, Massimo and Giovanni Colavizza. “Art Metrics”. ArXiv
preprint. 2019. Accessed June 9, 2022. https://www.arxiv-
vanity.com/papers/1907.07758/
Mitali, Banerjee and Paul L. Ingram. “Fame as an Illusion of Creativity:
Evidence from the Pioneers of Abstract Art”. HEC Paris Research
Paper, no. SPE-2018-1305. Columbia Business School Research
Paper, no. 18-74 (2018). Accessed June 9, 2022.
http://dx.doi.org/10.2139/ssrn.3258318
Sacco, Pier Luigi (2021) “Cultural Participation, Digital Technologies
and Transformational Social Change”. Europeana 2021. Online
Conference. November 12, 2021.
Salganik M. J. et al. “Experimental Study of Inequality and
Unpredictability in an Artificial Cultural Market”. Science 311, no. 5762.
February 10 (2006): 854-856.
Van de Rijt, A. et al. “Field experiments of success-breeds-success
dynamics”. Proceedings of the National Academy of Sciences (PNAS)
111, no. 19. April 28 (2014): 6934–6939.
21
Thank you!
https://uvimca.ics.forth.gr/
@sbrtrandcurator
uvimca
This presentation is part of a wider research project has received funding from
the European Union’s Horizon 2020 research and innovation programme under
the Marie Skłodowska-Curie grant agreement No 893454

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how online collections could potentially impact the actual art system

  • 1. Recruiting Collective Intelligence to Level the Contemporary Art World’s Stratified Distribution of Prestige and Value How Online Collections Could Potentially Impact the Actual Art System Dr. Stéphanie Bertrand This presentation is part of a wider research project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 893454
  • 2. Content warning This presentation contains a Phillip Guston work depicting hooded figures referencing the Ku Klux Klan (Slide 19). If you do not wish to view this content, please look away for 20 seconds following Slide 18. 2
  • 3. There are no objective criteria to assess the value of any artwork or the performance of its maker 3
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  • 5. Where there are no objective criteria to evaluate performance, networks drive success 5
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  • 8. Art’s value depends on the judgment of a small # of art world experts who do not possess unlimited capacity for discovery 8
  • 9. Changing the makeup of the network by recruiting the public’s collective intelligence to make the artworld more equal and diverse 9
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  • 14. Social influence has a strong impact on artistic appreciation & different reward systems on crowdsourcing platforms 14
  • 15. Social feedback can lead to 2 major issues: runaway inequality & artificial manipulation 15
  • 16. How can you minimize outside influence to truly capture users’ unique perspective? 16
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  • 21. Images Slide 4: Teaser of photos by Stefan Draschan from his People Matching Artworks Series Slide 6: (left) The Art Network (with presenter indication) and (right) The Art Network, zoomed in on the central cluster with labels, both Barabasi Lab Art, 2018 Slide 7: (from top left to bottom right) views of the facades of The Whitney Museum of American Art, Centre Pompidou, Pace Gallery New York, Solomon R. Guggenheim Museum, MoMA and Gagosian Gallery London Slide 10: (from top left to bottom center) Rijksstudio, Europeana, The MONA’s The O App, The Baden State Museum’s Ping! App and Smartify App Slide 11: (left) Image of a Sotheby’s auction in May 2021 (right) Image of Paris+ art fair 2022 (both sourced from the NYTimes) Slide 12: (from top left to bottom right) Videoclip stills of The Carter’s Apeshit, Katy Perry’s This Is How We Do, Rhianna’s Rude Boy and Drake’s Hotline Bling Slide 17: (left) Vivian Maier, Macy’s Parade, 1955 (right) Salvador Dali, Le Sommeil (Sleep), 1937 Slide 18: (left) Pablo Picasso, Guernica, 1937 (right) Salvador Dali, Le Sommeil (Sleep), 1937 Slide 19: (left) Philip Guston, Riding Around, 1969 (top right) Work by Angela Deane from her Ghost Photograph series (center right) Elizabeth Catlett, Black Unity, 1968 (bottom right) Barbara Kruger, Untitled (You’ve got money to burn), 1988 References Barabási, A.-L.. The Formula: The Universal Laws of Success. New York: Little, Brown and Company, 2018. Berger, J. Ways of Seeing. London: Penguin Books, 2008. Bertrand, Stéphanie. “Curating Online Collections: Towards an Authentically Digital, Mediation Protocol for Art Digitizations”. Journal of Curatorial Studies 11, no. 1 (2022). De Duve, Thierry. Kant After Duchamp, Cambridge MA: MIT Press, 1996. Fraiberger S. P. et al. “Quantifying Reputation and Success in Art”. Science 362, no. 6416. November 8 (2018), 825-829. Franceschet, Massimo and Giovanni Colavizza. “Art Metrics”. ArXiv preprint. 2019. Accessed June 9, 2022. https://www.arxiv- vanity.com/papers/1907.07758/ Mitali, Banerjee and Paul L. Ingram. “Fame as an Illusion of Creativity: Evidence from the Pioneers of Abstract Art”. HEC Paris Research Paper, no. SPE-2018-1305. Columbia Business School Research Paper, no. 18-74 (2018). Accessed June 9, 2022. http://dx.doi.org/10.2139/ssrn.3258318 Sacco, Pier Luigi (2021) “Cultural Participation, Digital Technologies and Transformational Social Change”. Europeana 2021. Online Conference. November 12, 2021. Salganik M. J. et al. “Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market”. Science 311, no. 5762. February 10 (2006): 854-856. Van de Rijt, A. et al. “Field experiments of success-breeds-success dynamics”. Proceedings of the National Academy of Sciences (PNAS) 111, no. 19. April 28 (2014): 6934–6939. 21
  • 22. Thank you! https://uvimca.ics.forth.gr/ @sbrtrandcurator uvimca This presentation is part of a wider research project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 893454