Generative, Open, and Democratic Science for Post Modern Days
* Article: Generative, Open, and Democratic Science for Post Modern Days. By @Batuhan.
(via DM on X, June 16th)
Abstract
This essay articulates a vision for generative, open, and democratic science in contrast to the prevailing model of predictive yet authoritarian science. Drawing on the ten postulates developed through our extended dialogue — and integrating insights from Deleuze, Guattari, Stiegler, Gödel, Wittgenstein, and contemporary developments in neural-network epistemology — we propose that scientific practice must evolve from “prediction as command” to “generation as collaboration.”
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1. From Predictive Authority to Generative Collaboration
Postulate 1: Truth as Process, Not Representation.
Traditional predictive science treats models as fixed representations of reality, judged by their error margins. By contrast, generative science regards truth as an emergent property of ongoing, participatory processes (Deleuze, 1994; Simondon, 2020).
Postulate 2: Models as Creators of Possibility.
Rather than seeking the single “best approximation,” generative science views every model as a generator of alternative worlds. Its goal is not to decree what will happen, but to enable what might happen (Bhaskar, 1978; Barad, 2007).
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2. Epistemic Pluralism and Collective Participation
Postulate 3: Epistemic Equality and Heterogeneity.
Knowledge production must be open to diverse agents—human, technological, and institutional—on equal footing. Cybernetic governance and proprietary AI systems violate this by restricting knowledge to elite enclaves (Stiegler, 2011).
Postulate 4: Science as Meaning-Making.
Every scientific proposition is also a normative act of meaning-making. Generative science embraces Wittgenstein’s insight that meaning arises in language-games, not in isolated formulas (Wittgenstein, 1953).
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3. Simulational Modesty and Ontological Humility
Postulate 5: Simulation as Dialogue, Not Substitution.
Rather than claiming simulacra can replace reality (Baudrillard, 1981), generative science positions simulations as partners in a creative dialogue, revealing blind spots and novel affordances.
Postulate 6: Gödelian Openness.
Any sufficiently powerful formal system harbors true but unprovable statements (Gödel, 1931). Generative science welcomes this incompleteness as a reservoir of creative potential rather than an obstacle to authority.
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4. Energy, Attention, and Computational Justice
Postulate 7: Equitable Cognitive and Computational Costs.
The resource demands of model training and deployment must be equitably distributed. Generative science challenges the “compute aristocracy” of large-scale neural networks (e.g., deep CNNs for fluid-dynamics surrogates) by prioritizing frugal algorithms inspired by brain-like efficiency (Hebb, 1949; Hopfield, 1982).
Postulate 8: Probabilistic Plurality over Determinism.
Instead of locking systems into deterministic laws, generative science cultivates probability fields of outcomes. Each iteration seeds new differences, echoing Deleuze’s notion of difference and repetition (Deleuze, 1968).
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5. An Open Axiomatic for Problematic Inquiry
Postulate 9: From Closed Axioms to Open Problematics.
Rather than constructing closed, authority-preserving axioms, generative science formulates open-ended problematics that invite user-driven extension (Deleuze & Guattari, 1980).
Postulate 10: Science as Ethical Self-Reflection.
Scientific inquiry must not only be useful but also reflective of its social purpose: Who benefits, and what world is being constructed? This aligns with Foucault’s critique of power-knowledge entanglements (Foucault, 1977).
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6. Integrative Framework: Guattari, Stiegler, and Neural-Network Epistemology
Guattari’s Three Machines (libidinal, symbolic, socio-technical) remind us that generative science must engage the energetic flows of desire, the code of representation, and the material infrastructures of technology (Guattari, 1989).
Stiegler’s Technical Time underscores how digital systems reorder temporal experience, compressing future into present models and thus demanding epistemic patience (Stiegler, 2011).
Neural-Network Epistemology exemplifies the shift from cause-explanation to predictive functionality, highlighting that statistical pattern-matching can outperform explicit equations—even in fluid dynamics—yet must be tempered by interpretability and democratic oversight.
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7. Conclusion
Generative, open, and democratic science is not a mere methodological tweak but a paradigmatic reorientation. It insists that:
1. Truth arises from participatory processes.
2. Models are exploratory tools, not edicts.
3. Knowledge is co-created across diverse agents.
4. Simulation invites collaboration, not colonization.
5. Incompleteness is a resource for innovation.
6. Computational justice counters algorithmic oligarchy.
7. Open problematics replace closed dogmas.
8. Ethical reflexivity anchors scientific purpose.
In embracing these postulates, science can transcend its predictive-authoritarian heritage and become a creative, inclusive, and continually self-transforming endeavor—a science worthy of the complexity and plurality of the world it seeks to inhabit.
References
Barad, K. (2007). Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning. Duke University Press.
Baudrillard, J. (1981). Simulacra and Simulation. Semiotext(e).
Bhaskar, R. (1978). A Realist Theory of Science. Routledge & Kegan Paul.
Deleuze, G. (1968). Difference and Repetition. Columbia University Press.
Deleuze, G. (1994). Difference and Repetition. Continuum.
Deleuze, G., & Guattari, F. (1980). A Thousand Plateaus. University of Minnesota Press.
Foucault, M. (1977). Discipline and Punish: The Birth of the Prison. Pantheon Books.
Gödel, K. (1931). Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I. Monatshefte für Mathematik und Physik, 38, 173–198.
Guattari, F. (1989). The Three Ecologies. Athlone Press.
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Simondon, G. (2020). On the Mode of Existence of Technical Objects. University of Minnesota Press.
Stiegler, B. (2011). Taking Care of Youth and the Generations. Stanford University Press.
Wittgenstein, L. (1953). Philosophical Investigations. Blackwell