Why Chaos Theory Should Really Be Called “Order Theory”: Metaphysics, Plotnitsky, Kermode, and the Denial of Undecidability

Introduction: The Misnomer of Chaos Theory

The name “chaos theory” suggests a radical break from traditional scientific and mathematical models of order. One might assume that chaos theory embraces indeterminacy, unpredictability, and the breakdown of classical structures—but in reality, it does the opposite. At its core, chaos theory is not about chaos at all. Instead, it is a highly deterministic metaphysical framework, one that paradoxically reinforces the very structures it claims to question. It insists that apparent randomness is simply order in disguise, reducible to deeper laws, fractal patterns, and calculable equations. If anything, chaos theory should be renamed “order theory”, since its true function is to maintain the fantasy that the world remains fundamentally knowable—even in its most unpredictable forms.

Drawing on Arkady Plotnitsky’s work on nonclassical epistemology, Frank Kermode’s insights on narrative structure, Derridean undecidability, and my own research on AI, this post argues that chaos theory does not embrace uncertainty so much as it seeks to contain it. It denies undecidability, reducing the radically unknowable to hidden structures of order. This has profound implications—not just for science but for AI, authoritarianism, and climate discourse, where the impulse to impose narrative closure and predictive control shapes contemporary political and technological realities.

Chaos Theory as a Metaphysics of Hidden Order

Chaos theory appears to challenge classical physics, yet it ultimately reinforces a metaphysical belief in underlying order. Rather than accepting that some events may be inherently undecidable, irreducible, or beyond calculation, chaos theory argues that all unpredictability is merely the result of our epistemic limitations. If only we had more processing power, better algorithms, or finer initial conditions, we could, in principle, uncover the order beneath the disorder.

This is a metaphysical commitment to determinism, not a true embrace of chaos. The butterfly effect, often cited as an example of chaos theory, is not about randomness—it is about the hyper-sensitivity of deterministic systems. The idea that a butterfly’s wings could set off a hurricane does not mean the hurricane is unpredictable—only that it is extremely dependent on initial conditions. Given enough data, in theory, we could still predict it.

This is precisely where chaos theory departs from nonclassical epistemology and aligns itself with classical determinism. It is, in Plotnitsky’s terms, a classical, or at best semiclassical, theory masquerading as revolutionary.

Plotnitsky, Undecidability, and the Limits of Predictability

Arkady Plotnitsky, in his work on nonclassical epistemology, contrasts classical (predictable, deterministic) knowledge with nonclassical (indeterminate, undecidable) knowledge. He shows that certain domains—especially quantum mechanics, deconstruction, and aspects of nonclassical AI—force us to confront radical undecidability.

This is where chaos theory fails. Rather than acknowledging the limits of prediction, it doubles down on the classical dream that all systems can ultimately be reduced to equations, patterns, and knowable structures. Chaos theory does not lead us into Plotnitsky’s nonclassical space of genuine undecidability, where prediction collapses in principle. Instead, it remains within the classical paradigm, insisting that disorder is only a temporary veil over deeper deterministic structures.

In this way, chaos theory mirrors AI models that attempt to reduce uncertainty to calculable probability. While AI, particularly machine learning and neural networks, appears to engage with uncertainty, it too functions within an epistemological model that seeks to contain undecidability. AI models, like chaos theory, assume that given enough data, everything can ultimately be known, structured, and predicted.

But what if some forms of knowledge are not merely unknown but fundamentally undecidable? What if uncertainty is not a problem to be solved but a constitutive feature of reality itself?

Kermode, the Desire for Order, and Authoritarian Control

Frank Kermode’s The Sense of an Ending (1967) examines how humans impose narrative structures onto chaotic, uncertain realities. Eschatological thinking—the drive to shape history as a coherent story with a beginning, middle, and end—is not just a theological impulse but a broader human tendency. We want crisis to have resolution, disorder to have a final meaning, and history to move toward some kind of intelligible closure.

Chaos theory is a scientific version of this same impulse: the refusal to accept radical uncertainty. Instead of allowing for true unpredictability, it offers a hidden structure, a reassuring order beneath apparent disorder. This is the same logic that underpins:

1. Authoritarianism: Totalitarian regimes thrive on the illusion of order, portraying history as a narrative of control, inevitability, and destiny. Fascist movements, for example, often reduce political chaos to conspiratorial order, claiming that behind every crisis is an identifiable enemy (immigrants, elites, intellectuals, AI, etc.) who must be eliminated to restore balance.

2. AI’s Predictive Systems: Just as chaos theory refuses to let go of deterministic order, AI assumes that all patterns can ultimately be predicted. Big Data, machine learning, and predictive policing are based on statistical totalitarianism—the belief that given enough inputs, human behavior, crime, and social unrest can be mapped and controlled.

3. Climate Change Narratives: The climate crisis resists traditional narrative closure. Instead of a singular apocalypse, it unfolds as a slow, uncanny catastrophe. Kermode’s work helps us see how many climate discourses try to impose order onto this chaos, either by embracing eschatological doom narratives or by insisting on technological salvation through AI and geoengineering—both of which deny the fundamental unpredictability of ecological collapse.

Conclusion: Toward a Theory of Radical Uncertainty

If chaos theory is really a deterministic theory of hidden order, then what we need is a true theory of chaos—one that does not seek to reduce uncertainty to mere epistemic limitation but acknowledges the fundamental undecidability at the heart of reality.

• Chaos theory should be renamed “order theory” because it masks a deep metaphysical commitment to determinism.

• Plotnitsky’s work on nonclassical epistemology shows that true undecidability challenges the entire foundation of chaos theory—which remains trapped in a classical worldview.

• AI follows the same logic as chaos theory, attempting to reduce uncertainty rather than embrace it.

• Kermode’s work reveals that this impulse to impose order onto chaos has deep ties to authoritarian politics, AI control, and climate discourse.

A true “chaos theory” would not be about managing disorder but about confronting the limits of human knowledge, prediction, and control. It would force us to accept that some realities are not just difficult to calculate but impossible to resolve—a truly radical departure from the deterministic metaphysics that still govern science, AI, and philosophy today.

Final Thought: The Future of Undecidability

As AI advances and climate crises accelerate, the desire to impose order onto chaos will only intensify. But if we want to truly think beyond classical epistemologies, we must embrace Plotnitsky’s nonclassical approach—one that accepts radical uncertainty as an irreducible part of reality. Until then, chaos theory remains less about chaos and more about our unwillingness to let go of order.