Wednesday, August 6, 2025
No menu items!
HomeNatureThe science fiction science method

The science fiction science method

  • Braghieri, L., Levy, R. & Makarin, A. Social media and mental health. Am. Econ. Rev. 112, 3660–3693 (2022).


    Google Scholar
     

  • Van Bavel, J. J., Robertson, C. E., Del Rosario, K., Rasmussen, J. & Rathje, S. Social media and morality. Annu. Rev. Psychol. 75, 311–340 (2024).

    PubMed 

    Google Scholar
     

  • Bail, C. Social-media reform is flying blind. Nature 603, 766 (2022).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Frewer, L. et al. Societal aspects of genetically modified foods. Food Chem. Toxicol. 42, 1181–1193 (2004).

    CAS 
    PubMed 

    Google Scholar
     

  • Siegrist, M. & Hartmann, C. Consumer acceptance of novel food technologies. Nat. Food 1, 343–350 (2020).

    PubMed 

    Google Scholar
     

  • Scott, S. E., Inbar, Y. & Rozin, P. Evidence for absolute moral opposition to genetically modified food in the United States. Perspect. Psychol. Sci. 11, 315–324 (2016).

    PubMed 

    Google Scholar
     

  • Fernbach, P. M., Light, N., Scott, S. E., Inbar, Y. & Rozin, P. Extreme opponents of genetically modified foods know the least but think they know the most. Nat. Hum. Behav. 3, 251–256 (2019).

    PubMed 

    Google Scholar
     

  • Cobb, M. D. & Macoubrie, J. Public perceptions about nanotechnology: risks, benefits and trust. J. Nanopart. Res. 6, 395–405 (2004).

    ADS 

    Google Scholar
     

  • Lee, C.-J., Scheufele, D. A. & Lewenstein, B. V. Public attitudes toward emerging technologies: examining the interactive effects of cognitions and affect on public attitudes toward nanotechnology. Sci. Commun. 27, 240–267 (2005).


    Google Scholar
     

  • Danaher, J. & Sætra, H. S. Mechanisms of techno-moral change: a taxonomy and overview. Ethical Theory Moral Pract. 26, 763–784 (2023). This article offers a precise and generative taxonomy of how technology reshapes moral life, providing a conceptual foundation for designing sci-fi-sci scenarios with mechanistic clarity.


    Google Scholar
     

  • Goldin, C. & Katz, L. F. The power of the pill: oral contraceptives and women’s career and marriage decisions. J. Pol. Econ. 110, 730–770 (2002).


    Google Scholar
     

  • Bailey, M. J. More power to the pill: the impact of contraceptive freedom on women’s life cycle labor supply. Q. J. Econ. 121, 289–320 (2006).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Baker, R. Before Bioethics (Oxford Univ. Press, 2013).

  • Shariff, A., Green, J. & Jettinghoff, W. The privacy mismatch: evolved intuitions in a digital world. Curr. Dir. Psychol. Sci. 30, 159–166 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zuboff, S.The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power (PublicAffairs, 2019).

  • Collingridge, D. The Social Control of Technology (Pinter, 1980). This foundational work diagnoses the dilemma of control in technological development, which sci-fi-sci attempts to tackle by generating early empirical insights before lock-in occurs.

  • Fergnani, A. Mapping futures studies scholarship from 1968 to present: a bibliometric review of thematic clusters, research trends, and research gaps. Futures 105, 104–123 (2019).


    Google Scholar
     

  • Kuosa, T. Evolution of futures studies. Futures 43, 327–336 (2011).


    Google Scholar
     

  • Danaher, J. & Sætra, H. S. Technology and moral change: the transformation of truth and trust. Ethics Inf. Technol. 24, 35 (2022).


    Google Scholar
     

  • Hopster, J. K. & Maas, M. M. The technology triad: disruptive AI, regulatory gaps and value change. AI Ethics 4, 1051–1069 (2024).


    Google Scholar
     

  • Brey, P. Ethics of Emerging Technology 175–191 (Rowman & Littlefield, 2017).

  • Pohl, F. The great new inventions. Galaxy 27, 6 (1968).


    Google Scholar
     

  • Bonnefon, J.-F., Shariff, A. & Rahwan, I. The social dilemma of autonomous vehicles. Science 352, 1573–1576 (2016).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Shariff, A., Bonnefon, J.-F. & Rahwan, I. Psychological roadblocks to the adoption of self-driving vehicles. Nat. Hum. Behav. 1, 694–696 (2017).

    PubMed 

    Google Scholar
     

  • Awad, E. et al. The Moral Machine experiment. Nature 563, 59–64 (2018). This study is a classic example of sci-fi-sci, experimentally probing public moral preferences for a speculative technology (fully autonomous vehicles) through a massive global dataset of 40 million decisions.

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Bonnefon, J.-F. et al. Ethics of connected and automated vehicles: recommendations on road safety, privacy, fairness, explainability and responsibility (European Commission, 2020).

  • Luetge, C. The German ethics code for automated and connected driving. Philos. Technol. 30, 547–558 (2017).


    Google Scholar
     

  • Santoni de Sio, F. The European Commission report on ethics of connected and automated vehicles and the future of ethics of transportation. Ethics Inf. Technol. 23, 713–726 (2021).


    Google Scholar
     

  • Adnan, N. Exploring the future: a meta-analysis of autonomous vehicle adoption and its impact on urban life and the healthcare sector. Transp. Res. Interdiscip. Persp. 26, 101110 (2024).


    Google Scholar
     

  • Tussyadiah, I. A review of research into automation in tourism: launching the annals of tourism research curated collection on artificial intelligence and robotics in tourism. Ann. Tour. Res. 81, 102883 (2020).


    Google Scholar
     

  • Zeng, Y., Liu, X., Zhang, X. & Li, Z. Retrospective of interdisciplinary research on robot services (1954–2023): from parasitism to symbiosis. Technol. Soc. 78, 102636 (2024).


    Google Scholar
     

  • Benkler, Y. Don’t let industry write the rules for AI. Nature 569, 161–162 (2019).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Cave, S. & Dihal, K. Hopes and fears for intelligent machines in fiction and reality. Nat. Mach. Intell. 1, 74–78 (2019).


    Google Scholar
     

  • Lazer, D. et al. Computational social science: obstacles and opportunities. Science 369, 1060–1062 (2020).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Lazer, D. et al. Meaningful measures of human society in the twenty-first century. Nature 595, 189–196 (2021).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Munger, K. The limited value of non-replicable field experiments in contexts with low temporal validity. Soc. Media Soc. 5, 2056305119859294 (2019).


    Google Scholar
     

  • Munger, K. Temporal validity as meta-science. Res. Politics 10, 20531680231187271 (2023). This article unpacks the concept of temporal validity, an essential concern for sci-fi-sci, as it exposes the limits of applying present-day empirical knowledge to future contexts.


    Google Scholar
     

  • Wilson, T. D. & Gilbert, D. T. Affective forecasting. Adv. Exp. Soc. Psychol. 35, 345–411 (2003).


    Google Scholar
     

  • Schönmann, M., Bodenschatz, A., Uhl, M. & Walkowitz, G. Contagious humans: a pandemic’s positive effect on attitudes towards care robots. Technol. Soc. 76, 102464 (2024).


    Google Scholar
     

  • Inhorn, M. C. & Birenbaum-Carmeli, D. Assisted reproductive technologies and culture change. Annu. Rev. Anthropol. 37, 177–196 (2008).


    Google Scholar
     

  • Dinh, C. T., Humphries, S. & Chatterjee, A. Public opinion on cognitive enhancement varies across different situations. Am. J. Bioethics 11, 224–237 (2020).


    Google Scholar
     

  • Mihailov, E., López, B. R., Cova, F. & Hannikainen, I. R. How pills undermine skills: moralization of cognitive enhancement and causal selection. Conscious. Cogn. 91, 103120 (2021).

    PubMed 

    Google Scholar
     

  • Sattler, S. et al. Neuroenhancements in the military: a mixed-method pilot study on attitudes of staff officers to ethics and rules. Neuroethics 15, 11 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lucas, S., Douglas, T. & Faber, N. S. How moral bioenhancement affects perceived praiseworthiness. Bioethics 38, 129–137 (2024).

    PubMed 

    Google Scholar
     

  • Laakasuo, M. et al. What makes people approve or condemn mind upload technology? untangling the effects of sexual disgust, purity and science fiction familiarity. Palgrave Commun. 4, 84 (2018).


    Google Scholar
     

  • Salganik, M. J., Dodds, P. S. & Watts, D. J. Experimental study of inequality and unpredictability in an artificial cultural market. Science 311, 854–856 (2006).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Longoni, C. & Cian, L. Artificial intelligence in utilitarian vs. hedonic contexts: the ’word-of-machine’ effect. J. Mark. 86, 91–108 (2022).


    Google Scholar
     

  • Köbis, N. et al. Artificial intelligence can facilitate selfish decisions by altering the appearance of interaction partners. Preprint at https://doi.org/10.48550/arXiv.2306.04484 (2023).

  • Benvegnù, G., Pluchino, P. & Garnberini, L. Virtual morality: using virtual reality to study moral behavior in extreme accident situations. In 2021 IEEE Virtual Reality and 3D User Interfaces 316–325 (IEEE, 2021).

  • Sütfeld, L. R., Ehinger, B. V., König, P. & Pipa, G. How does the method change what we measure? comparing virtual reality and text-based surveys for the assessment of moral decisions in traffic dilemmas. PLoS ONE 14, e0223108 (2019).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Faulhaber, A. K. et al. Human decisions in moral dilemmas are largely described by utilitarianism: Virtual car driving study provides guidelines for autonomous driving vehicles. Sci. Eng. Ethics 25, 399–418 (2019).

    PubMed 

    Google Scholar
     

  • Riek, L. D. Wizard of Oz studies in HRI: a systematic review and new reporting guidelines. J. Hum. Robot Interact. 1, 119–136 (2012).


    Google Scholar
     

  • Aroyo, A. M. et al. Will people morally crack under the authority of a famous wicked robot? In 2018 27th IEEE International Symposium on Robot and Human Interactive Communication 35–42 (IEEE, 2018).

  • Bishop, S. L., Kobrick, R., Battler, M. & Binsted, K. Fmars 2007: stress and coping in an Arctic Mars simulation. Acta Astronautica 66, 1353–1367 (2010).

    ADS 

    Google Scholar
     

  • Alfano, C. A., Bower, J. L., Cowie, J., Lau, S. & Simpson, R. J. Long-duration space exploration and emotional health: recommendations for conceptualizing and evaluating risk. Acta Astronautica 142, 289–299 (2018). This article reviews methods for forecasting emotional health risks during a Mars mission, an extreme case of sci-fi-sci aimed at predicting human responses in radically novel, high-stakes environments.

    ADS 

    Google Scholar
     

  • Riva, P., Rusconi, P., Pancani, L. & Chterev, K. Social isolation in space: an investigation of LUNARK, the first human mission in an Arctic Moon analog habitat. Acta Astronautica 195, 215–225 (2022).

    ADS 

    Google Scholar
     

  • Candy, S. & Potter, C. Design and Futures, vol. 1. J. Futures Stud. (2019).

  • Candy, S. & Potter, C. Design and Futures, vol. 2. J. Futures Stud. (2019). The two volumes of this special issue are a treasure trove of innovative methods for designing immersive future simulations, offering sci-fi-sci researchers a rich toolbox for experimental protocols grounded in a tangible experience.

  • Palmer, A. Too Like the Lightning (Tor Books, 2016).

  • Gall, T., Vallet, F. & Yannou, B. How to visualise futures studies concepts: revision of the futures cone. Futures 143, 103024 (2022).


    Google Scholar
     

  • Coolidge, F. L., Wynn, T., Overmann, K. A. & Hicks, J. M. in Human Paleoneurology (ed. Bruner, E.) 177–208 (Springer, 2015).

  • North, D. C. Structure and performance: the task of economic history. J. Econ. Lit. 16, 963–978 (1978).


    Google Scholar
     

  • von Schenk, A., Klockmann, V., Bonnefon, J.-F., Rahwan, I. & Köbis, N. Lie detection algorithms disrupt the social dynamics of accusation behavior. iScience 27, 110201 (2024).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Héder, M. From NASA to EU: the evolution of the TRL scale in public sector innovation. Innov. J. 22, 1–23 (2017). This article unpacks the evolution and potential misuses of the technology readiness level scale, which can help sci-fi-sci researchers to choose speculative technologies that are still uncertain, but not untethered from reality.


    Google Scholar
     

  • Coccia, M. Measuring intensity of technological change: the seismic approach. Technol. Forecast. Soc. Change 72, 117–144 (2005).


    Google Scholar
     

  • Goodall, N. J. Ethical decision making during automated vehicle crashes. Transp. Res. Rec. 2424, 58–65 (2014).


    Google Scholar
     

  • Hess, D. J. Incumbent-led transitions and civil society: autonomous vehicle policy and consumer organizations in the united states. Technol. Forecast. Soc. Change 151, 119825 (2020).


    Google Scholar
     

  • Kriebitz, A., Max, R. & Lütge, C. The German act on autonomous driving: why ethics still matters. Philos. Technol. 35, 29 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mac Síthigh, D. & Siems, M. The Chinese social credit system: a model for other countries? Mod. L. Rev. 82, 1034–1071 (2019).


    Google Scholar
     

  • Orgad, L. & Reijers, W. A Dystopian Future? The Rise of Social Credit Systems. Technical Report RSCAS 2019/94 (Robert Schuman Centre for Advanced Studies, 2019).

  • Tirole, J. Digital dystopia. Am. Econ. Rev. 111, 2007–2048 (2021).


    Google Scholar
     

  • Purcell, Z. A. & Bonnefon, J.-F. Humans feel too special for machines to score their morals. PNAS Nexus 2, pgad179 (2023).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Proposal for a Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52021PC0206 (European Commission, 2021).

  • Turley, P. et al. Problems with using polygenic scores to select embryos. N. Engl. J. Med. 385, 78–86 (2021).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Anomaly, J. Creating Future People: The Ethics of Genetic Enhancement (Taylor & Francis, 2020).

  • Anomaly, J., Gyngell, C. & Savulescu, J. Great minds think different: preserving cognitive diversity in an age of gene editing. Bioethics 34, 81–89 (2020).

    PubMed 

    Google Scholar
     

  • Gyngell, C. & Douglas, T. Stocking the genetic supermarket: reproductive genetic technologies and collective action problems. Bioethics 29, 241–250 (2015).

    PubMed 

    Google Scholar
     

  • Cavaliere, G. Ectogenesis and gender-based oppression: resisting the ideal of assimilation. Bioethics 34, 727–734 (2020).

    PubMed 

    Google Scholar
     

  • Hooton, V. & Romanis, E. C. Artificial womb technology, pregnancy, and EU employment rights. J. L. Biosci. 9, lsac009 (2022).


    Google Scholar
     

  • Horn, C. Ectogenesis, inequality, and coercion: a reproductive justice-informed analysis of the impact of artificial wombs. BioSocieties 18, 523–544 (2023).


    Google Scholar
     

  • MacKay, K. The ‘tyranny of reproduction’: could ectogenesis further women’s liberation? Bioethics 34, 346–353 (2020).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Lin, P. in Autonomous Driving: Technical, Legal and Social Aspects (eds Maurer, M.) 69–85 (Springer Vieweg, 2016).

  • Milakis, D. Long-term implications of automated vehicles: an introduction. Transp. Rev. 39, 1–8 (2019).


    Google Scholar
     

  • Soteropoulos, A., Berger, M. & Ciari, F. Impacts of automated vehicles on travel behaviour and land use: an international review of modelling studies. Transp. Rev. 39, 29–49 (2019).


    Google Scholar
     

  • Fernández Llorca, D. & Gómez, E. Trustworthy Autonomous Vehicles—Assessment Criteria for Trustworthy AI in the Autonomous Driving Domain (European Union, 2021).

  • Asimov, I. I, Robot (Gnome Press, 1950).

  • McDermott, D. in Machine Ethics (eds Anderson, M. & Anderson, S. L.) 88–114 (Cambridge Univ. Press, 2011).

  • Hirai, K., Hirose, M., Haikawa, Y. & Takenaka, T. The Development of Honda Humanoid Robot. In Proc. 1998 IEEE International Conference on Robotics and Automation Vol. 2, 1321–1326 (IEEE, 1998).

  • Ishiguro, H. et al. Robovie: an interactive humanoid robot. Ind. Robot 28, 498–504 (2001).


    Google Scholar
     

  • Breazeal, C. Designing Sociable Robots (MIT Press, 2004).

  • Jennings, N. R., Norman, T. J., Faratin, P., O’Brien, P. & Odgers, B. Autonomous agents for business process management. Appl. Artif. Intel. 14, 145–189 (2000).


    Google Scholar
     

  • Reijers, H. A. Business process management: the evolution of a discipline. Comput. Ind. 126, 103404 (2021).


    Google Scholar
     

  • Humanoid Robot Market Size, Share & Industry Analysis, by Motion Type (Biped and Wheel Drive), by Component (Hardware and Software), by Application (Industrial, Household, and Services), and Regional Forecast 2024–2032. Technical Report (Fortune Business Insights, 2024).

  • Dafoe, A. et al. Cooperative AI: machines must learn to find common ground. Nature 593, 33–36 (2021).

    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Acemoglu, D. & Restrepo, P. Automation and new tasks: how technology displaces and reinstates labor. J. Economic Perspect. 33, 3–30 (2019).


    Google Scholar
     

  • Crandall, J. W. et al. Cooperating with machines. Nat. Commun. 9, 233 (2018).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ishowo-Oloko, F. et al. Behavioural evidence for a transparency–efficiency tradeoff in human–machine cooperation. Nat. Mach. Intell. 1, 517–521 (2019).


    Google Scholar
     

  • Karpus, J., Krüger, A., Verba, J. T., Bahrami, B. & Deroy, O. Algorithm exploitation: humans are keen to exploit benevolent AI. iScience 24, 102679 (2021).

    ADS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Oudah, M., Makovi, K., Gray, K., Battu, B. & Rahwan, T. Perception of experience influences altruism and perception of agency influences trust in human–machine interactions. Sci. Rep. 14, 12410 (2024).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Makovi, K., Sargsyan, A., Li, W., Bonnefon, J.-F. & Rahwan, T. Trust within human–machine collectives depends on the perceived consensus about cooperative norms. Nat. Commun. 14, 3108 (2023).

    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dong, M., Bonnefon, J.-F. & Rahwan, I. Toward human-centered AI management: methodological challenges and future directions. Technovation 131, 102953 (2024).


    Google Scholar
     

  • Bubeck, S. et al. Sparks of artificial general intelligence: early experiments with GPT-4. Preprint at https://doi.org/10.48550/arXiv.2303.12712 (2023).

  • Wu, Q. et al. Autogen: enabling next-gen LLM applications via multi-agent conversation framework. In First Conference on Language Modeling https://openreview.net/forum?id=BAakY1hNKS (OpenReview, 2024).

  • Bamberger, S., Clark, N., Ramachandran, S. & Sokolova, V. How generative AI is already transforming customer service. Boston Consulting Group www.bcg.com/publications/2023/how-generative-ai-transforms-customer-service (2023).

  • RELATED ARTICLES

    Most Popular

    Recent Comments