The Science Fiction Science Method

Iyad Rahwan (), Azim Shariff and Jean-François Bonnefon ()
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Iyad Rahwan: Max Planck Institute for Human Development - Max-Planck-Gesellschaft
Azim Shariff: UBC - University of British Columbia [Canada]
Jean-François Bonnefon: TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - Comue de Toulouse - Communauté d'universités et établissements de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement

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Abstract: Predicting the social and behavioral impact of future technologies, before they are achieved, would allow us to guide their development and regulation before these im-pacts get entrenched. Traditionally, this prediction has relied on qualitative, narrative methods. Here we describe a method which uses experimental methods to simulate future technologies, and collect quantitative measures of the attitudes and behaviors of participants assigned to controlled variations of the future. We call this method ‘sci-ence fiction science'. We suggest that the reason why this method has not been fully embraced yet, despite its potential benefits, is that experimental scientists may be re-luctant to engage in work facing such serious validity threats as science fiction science. To address these threats, we consider possible constraints on the kind of technology that science fiction science may study, as well as the unconventional, immersive meth-ods that science fiction science may require. We seek to provide perspective on the reasons why this method has been marginalized for so long, what benefits it would bring if it could be built on strong yet unusual methods, and how we can normalize these methods to help the diverse community of science fiction scientists to engage in a virtuous cycle of validity improvement.

Date: 2025-08-07
Note: View the original document on HAL open archive server: https://hal.science/hal-05273736v1
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Published in Nature, 2025, 644, pp.51-58. ⟨10.1038/s41586-025-09194-6⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05273736

DOI: 10.1038/s41586-025-09194-6

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