Science Fiction Science

• Science Fiction Science is an interdisciplinary field that fuses narrative modeling, simulation, and design fiction to extend traditional research into counterfactual realms.
• It applies controlled experimental simulations and speculative methodologies to generate actionable insights in risk analysis, AI ethics, and emergent technology evaluation.
• The approach bridges gaps in data-driven science by incorporating immersive narrative scenarios that offer innovative frameworks for forecasting future technological and social impacts.

Science fiction science denotes both a research ethos and a set of methodologies that use imaginative, narrative, or speculative constructions—often in controlled, empirical, or computational frameworks—to generate, analyze, and apply scientific knowledge. Unlike traditional empirical or data-centric scientific paradigms, science fiction science is characterized by its deliberate incorporation of counterfactuals, fictional narratives, simulation of future realities, and the creative extension of scientific method into domains where data is incomplete, human agency is poorly understood, or the technological object of paper has not yet fully materialized. Contemporary discourse positions science fiction science as not merely peripheral or illustrative but as co-equal with computer science and data science in the architecture of “informaticology” (Bergstra, 2012). Its scope spans from interactive game design and fictional narrative engineering to risk analysis in software practice, and quantitative experimental simulation of future social and technological impacts.

1. Foundational Position: Informaticology and Fiction Science

Within the formal framework of “informaticology” (IY), science fiction science—denoted as fiction science (FS)—is defined as an academic discipline on equal footing with computer science (CS) and data science (DS): $\text{IY} = \text{CS} + \text{DS} + \text{FS}$ Applied informatics (INF) is conceptualized as the application of practical methods (P) to this triadic knowledge space: $$\text{INF} = P(\text{IY}) = P(\text{CS} + \text{DS} + \text{FS})$$ Fiction science is not reducible to mere entertainment or narrative artistry; it encompasses interactive literature (e.g., interactive poetry), fictional narrative engineering, game design (serious and entertainment), and research that mobilizes fictional premises as engines for innovation. The explicit inclusion of fiction science extends practical informaticology beyond strictly instrumental objectives and usage perspectives, creating methodologies for the construction and assessment of counterfactual and speculative systems (Bergstra, 2012).

2. Scope and Methodologies of Science Fiction Science

Science fiction science uses narrative constructs, simulation, and experimental design to model and interrogate realities not yet achievable or where traditional data is unavailable or insufficient. Central methodological axes include:

• Fictional Narrative Modeling: Constructing “fictional inseqware” as a bridge between the abstract (e.g., pure mathematical models of instruction sequences) and the concrete (physical artifacts), using the work history of a fictional engineer to frame uncertainties (such as malicious interference or untraced errors) in the software development process. This enables Bayesian-like subjective probability updates to derive a “fictional probability” of failure, particularly where standard statistical inference is constrained by data scarcity, uniqueness, or incomplete production histories (Bergstra, 2012).
• Experimental Simulation of Future Technologies: Assigning human participants to controlled, immersive simulations (including interactive vignettes and virtual reality) of speculative technological regimes (e.g., autonomous vehicles with advanced moral reasoning), allowing the collection of quantitative measures (attitudes, behavioral intentions, or choices) prior to real-world implementation. Analytical strategies may include regression models ($$\text{Support}_i = \beta_0 + \beta_1 T_i + \varepsilon_i$$), conjoint analysis, and agent-based modeling to capture behavioral trade-offs across varied hypothetical future scenarios. This experimental paradigm is labeled “science fiction science” (Editor’s term, (Rahwan et al., 5 Aug 2025)).
• Design Fiction and HCI Prototyping: Adopting scenario-driven design, “design fiction,” and speculative prototyping in human-computer interaction to ideate, critique, and communicate potential futures (Jordan et al., 2018). This often takes the form of referencing or building upon science fiction stories, movies, and visualizations as conceptual springboards for new interaction paradigms or ethical probes in AI and robotics (Sermanet et al., 12 Mar 2025).
• Counterfactual and Ontological Bridging: Casting fictional objects as a third ontological category—alongside abstract mathematical and concrete physical objects—to fill epistemic and definitional gaps, especially in the presence of uncertainty introduced by history, agency, or incomplete factual record (Bergstra, 2012).

3. Applications: Risk Analysis, Quality Assurance, and Predictive Simulation

Science fiction science is employed in several practical domains:

• Software Quality Assessment and Risk Modeling: In systems where safety-critical outcomes depend upon instruction sequences with complex, poorly documented, or singular production histories, fictional narratives (detailing hypothetical production and usage) are constructed. These narratives serve as frameworks for Bayesian updating of subjective failure probabilities in the absence of direct empirical data, forming a “fictional” risk estimation surrogate (Bergstra, 2012).
• Simulation of Social and Behavioral Impact of Emergent Technology: Controlled experimental environments simulate the introduction or widespread adoption of still-to-be-realized technologies, collecting empirical data (qualitative and quantitative) to predict societal acceptance, ethical judgments, and policy-relevant outcomes (Rahwan et al., 5 Aug 2025). This prospective strategy is contrasted with traditional futures studies by its commitment to empirical rigor and the use of experimental randomization.
• Informing AI and Robot Ethics: Key moments and dilemmas from science fiction literature (e.g., autonomous decisions by fictional AI systems) are formalized as benchmark evaluation scenarios for real world AI, using LLM-generated reformulations into multiple-choice ethical dilemmas and iteratively improved constitutions or rules for alignment. Alignment rates are quantified and compared between fictional agents, current AI models, and constitution-augmented agents; the approach demonstrates that science fiction-inspired frameworks can robustly guide AI behavior toward safety and ethical acceptability even in adversarial evaluation settings (Sermanet et al., 12 Mar 2025).

4. Epistemic Status, Controversies, and Methodological Challenges

Despite its expanding scope, science fiction science faces resistance in mainstream scientific communities, largely due to concerns over validity:

• Validity Threats: Key challenges include the “future self” problem (contemporary participants are weak proxies for future actors), potential technological mis-specification (the simulated artifact may differ critically from realized technology), and context shift (unpredictable change in social context between experiment and application). To mitigate these, practitioners are advised to focus on technologies with non-trivial readiness (e.g., TRL ≥ 4), moderate projected impact, and to make the limits of generalizability explicit (Rahwan et al., 5 Aug 2025).
• Immersive Methods: Unlike standard variable-manipulation experiments, sci-fi-sci requires deeply immersive environments (detailed vignettes, virtual reality, or “Wizard of Oz” protocols) to ensure participants “inhabit” the counterfactual reality and provide ecologically valid responses (Rahwan et al., 5 Aug 2025). Such designs enhance behavioral realism but raise novel reproducibility and reporting challenges.
• Normalization Strategies: The field is advancing via interdisciplinary community-building, transparent reporting standards, development of workshops, and targeted funding. The goal is to facilitate a “virtuous cycle of validity improvement” wherein communal knowledge and iterative methodological refinement render speculative simulation increasingly reliable and informative (Rahwan et al., 5 Aug 2025).

5. Broader Implications for Science, Technology, and Society

The epistemic shift underlying science fiction science carries several significant implications:

• Methodological Expansion: By elevating imagination- and narrative-based techniques to methodological parity with empirical and statistical approaches, science fiction science enables rigorous exploration of “strange new spaces” in both social and technical domains—filling epistemic gaps where historical or empirical data is absent or where the future is radically indeterminate.
• Policy and Design Guidance: Early-stage simulation of technology’s social and behavioral impacts can facilitate anticipatory regulation or design constraints, reducing “lock-in” of harmful effects after deployment (Rahwan et al., 5 Aug 2025). For example, early experimental exploration of behavioral responses to nascent nanotechnology or AI aids in shaping policies before technologies become entrenched.
• Theoretical Integration: The central algebraic models (e.g., $\text{IY} = \text{CS} + \text{DS} + \text{FS}$, $\text{INF} = P(\text{IY})$) reframe informaticology to formally admit non-empirical, narrative—or even playful—approaches as essential for theory-building and application, not as marginal supplements (Bergstra, 2012). This expansion enables interdisciplinarity and encourages the integration of speculative, ethical, and design-based rationales into technical science.
• Impacts on Science Communication and Technological Innovation: By treating speculative fiction as a valid, creative substrate for model-building and scenario analysis, science fiction science supports a broader toolkit in both educating about and forecasting technological impacts. This underscores the importance of making imaginative methods scientifically rigorous rather than relegating them to the domain of mere cultural artifacts.

6. Future Integration and Research Agendas

The prospective consolidation of science fiction science within mainstream research practice will rest on several axes:

• Standardization of Reporting and Evaluation: Explicit frameworks for reporting uncertainties, generalizability limits, and the immersive fidelity of simulation environments can foster credible, testable, and repeatable results (Rahwan et al., 5 Aug 2025).
• Community and Institutional Support: Establishment of interdisciplinary “science fiction science” collectives, with dedicated workshops, conferences, and funding lines, is expected to foster methodological convergence and increase the legitimacy and impact of the approach.
• Iterative Model Refinement: Systematic feedback loops between simulated outcomes and real-world technological developments will allow ongoing updating of scenario designs, experimental protocols, and interpretive heuristics, thereby increasing the precision and relevance of science fiction science predictions.

In sum, science fiction science operationalizes counterfactual inquiry, narrative modeling, and immersive simulation—embedding these within the structure of rigorous experimental and analytic practice. Its methodological distinctiveness lies in its willingness and ability to formalize, test, and iteratively refine scenarios from speculative technological and social futures, advancing both the understanding of possible realities and the capacity to shape them proactively.