From Orbital Prediction to Temporal Causation: The Intersections of Film, Physics, and Agency
Abstract
Throughout human history, one of the deepest scientific and philosophical ambitions has been the ability to understand, predict, and ultimately influence future states of reality. From Newtonian mechanics and deterministic physics to modern computational theory, neuroscience, and artificial intelligence, humanity has continuously attempted to transform knowledge of causation into predictive power. This paper explores the intersection of real-world physics (the Great Space Race) and speculative film narrative (Back to the Future) to examine the information problem of causation. It introduces formal models for causal information preservation, visualises the transition from prediction to agency, and synthesises a 3-D analog framework connecting physics, information, and human decision-making.
1. Introduction: Humanity's Search for Predictive Power
Human technological development can be viewed as an increasing ability to convert uncertainty into controlled outcomes. Early human societies responded to nature through observation and adaptation, but modern science introduced a new objective: understand the causal mechanisms of reality sufficiently to predict future states. The scientific revolution established the idea that the universe operates according to discoverable laws.
Newtonian physics provided a mathematical framework where the present determines the future, physical laws govern change, and prediction becomes a computational problem. This created the foundation for the deterministic worldview.
The ultimate expression of this deterministic worldview is found in Laplace’s demon. Laplace’s demon is a thought experiment proposed by Pierre‑Simon Laplace in 1814: an imagined intelligence that, knowing the exact position and momentum of every particle in the universe, could compute all past and future events with perfect certainty. If such an intellect existed, the future would be just as present to its eyes as the past. However, as we explore the boundary problems of computation and the chaotic nature of reality, this perfect deterministic vision faces strict practical and theoretical limits.
2. Prediction, Precision, Information, and The Boundary Problem
"Please excuse the crudity of this model. I didn't have time to build it to scale or paint it." — Doc Brown, Back to the Future (1985)
A fundamental example of predictive limitations comes from computational physics. When calculating extremely small differences between nearly identical values, numerical systems can lose meaningful information. This phenomenon, known as catastrophic cancellation, occurs when:
The underlying physical quantity exists.
The computational representation lacks sufficient precision.
The final result falsely indicates absence.
The philosophical implication is significant: a failure of measurement or representation does not necessarily demonstrate the absence of the underlying phenomenon. This principle extends beyond numerical computation into physics, biology, and cognitive science.
Formal Conceptual Model of Causal Information Preservation
To define how information survives (or perishes) over time, we can establish a formal conceptual model of Causal Information Preservation. Let the complete physical state of a system at time t be defined as a vector St. The embedded agent's measured information about this state is It.
For a system to maintain causal continuity without catastrophic cancellation, the change in available information over time must satisfy a continuity equation bound by environmental noise η and computational friction Κ:
dI⁄dt + ∇ ⨯{J}I = σ {agency} - (η + Κ)
Where:
⨯{J}I is the flow of causal information through the temporal environment.
σ {agency} represents the internal generation of new information via the agent's simulated futures.
Preservation is only defensible if the integral of information over the decision interval Δ t remains strictly greater than the baseline deterministic threshold: ∫ I(t)dt ≥ Ithreshold.
3. The Space Race: Prediction Becoming Control
The Great Space Race transformed theoretical physics into operational reality. The success of orbital missions depended upon accurate predictions involving:
Gravitational mechanics.
Planetary motion.
Spacecraft velocity.
Atmospheric conditions.
Timing.
The Apollo programme demonstrated that humanity could reliably predict and influence future physical states. However, this achievement did not come from complete knowledge. Engineers did not model every particle in the universe. Instead, they identified relevant variables, acceptable error margins, and necessary precision thresholds. This established an important principle: successful prediction requires sufficient causal information, not absolute information.
4. The Limits Revealed by Modern Physics
The Space Race emerged from classical predictive physics, but later discoveries complicated the deterministic picture.
Chaos Theory: Chaotic systems demonstrate that deterministic rules can produce unpredictable outcomes. Small variations in initial conditions may produce large future differences. Therefore, a system may have causes without being practically forecastable.
Quantum Mechanics: Quantum mechanics introduced fundamental probability into physical predictions. The question changed from what exact outcome will occur to what probability distribution governs possible outcomes. However, randomness does not automatically create agency; a random event is not the same as a chosen event.
Computational Limits: Theories associated with Turing computation, Gödel incompleteness, and algorithmic information theory demonstrate that some systems cannot completely predict or describe themselves from within their own structure. This introduces a profound question: can a system fully understand itself when the system performing the understanding is part of the system being analysed?
5. Back to the Future & The Information-Causation Loop
The fictional premise of Back to the Future represents the ultimate predictive scenario: access to future information. The DeLorean functions as a hypothetical device that removes the normal boundary between present and future.
The philosophical consequence is profound: if future information enters the present, does the future remain fixed?. The central paradox is that information about the future can itself become a cause.
A person learns that a future event will occur.
That knowledge changes their behaviour.
Their changed behaviour alters the future event.
The prediction becomes part of the causal system, creating a self-referential loop: Future Information → Present Action → Future Outcome. The observer is no longer separate from the system being observed.
Agency Process Architecture
To understand how an observer transitions from passive measurement to active participant, we must map the cognitive progression from prediction to agency.
6. Free Will, Determinism, and The Agency Question
The same self-referential problem appears in neuroscience. Experiments investigating voluntary action attempt to determine whether conscious decisions are causes or consequences of brain processes. However, they face a fundamental challenge: the human brain is both the object being measured and the mechanism performing the measurement. This creates an observer problem.
The traditional debate asks whether humans are determined or free. However, this framing may be incomplete. A more precise question is: can a physically determined system become an agent by representing possible futures and modifying its own behaviour based on those representations?.
While a thermostat simply responds to temperature, a human can imagine future states, evaluate consequences, change behaviour, and create new causal pathways. In this way, the predictive model becomes part of the mechanism producing the outcome.
7. The Unified Framework: A 3-D Conceptual Physics Model
The Space Race and Back to the Future represent opposite ends of the same conceptual spectrum. To unify these concepts into a defensible physical model, we construct a 3-Dimensional Phase Space of Agency.
Let the state of a system be represented by a state vector Ψ operating within three distinct axes:
X-Axis (Temporal Causation - Tc): The linear progression from past to future governed by standard thermodynamic entropy.
Y-Axis (Information Precision - Ip): The spectrum between absolute ignorance and Laplace’s Demon (perfect precision).
Z-Axis (Agency/Intervention - A): The capacity of an embedded system to simulate non-existent states and inject new causal force back into the environment.
The unified equation of state for human agency in this model is given by the curl of the information field within the temporal domain:
∇ ⨯I = ∂A/∂Tc + μ0Jdecision
Where Jdecision is the current density of active human choices shaping the physical outcome.
Framework Component | X: Temporal Flow | Y: Information Precision | Z: Agency / Intervention |
Space Race | Forward linear | High reliance on mathematics | Low (actions pre-computed) |
Back to the Future | Non-linear / Looping | Future data injected to present | Maximum (changing timeline) |
Human Cognition | Forward linear | Limited by biological bounds | High (internal simulation) |
Space Race: Humanity predicts physical systems, leading from a mathematical model to a physical outcome.
Time Travel Fiction: Humanity attempts to manipulate causal history, leading from future information to a changed present and a changed future.
Human Cognition: The brain internally models futures and acts upon them, leading from prediction to decision to altered reality.
Conclusion
The central challenge linking physics, technology, neuroscience, and philosophy extends beyond a simple debate over determinism. The deeper opportunity lies in understanding the intricate relationship between causation, information, prediction, and agency.
The Space Race demonstrated that understanding causal systems allows humanity to practically influence future physical states. Concurrently, Back to the Future serves as much more than a speculative film narrative. It functions as a meticulously engineered structural model of causality, feedback loops, and psychological identity. By exploring the nonlinear consequences of empirically testing one’s own history, the narrative acts as a deterministic simulation where variables continually update after each intervention. Modern science similarly suggests that reality is neither a simple clockwork machine nor an entirely random process, but a complex structure whose behaviour depends on the limitations and actions of the observers embedded within it.
Therefore, the ongoing conversation surrounding free will might not require us to prove we can entirely escape causation. Instead, it invites us to explore how a deeply embedded causal system—like human cognition—develops genuine agency through its capacity to observe, evaluate, and intentionally reshape possible futures. Ultimately, embracing this interplay offers a more unified understanding of our role: we are naturally shaped by the universe, yet remarkably equipped to actively participate in its unfolding narrative.
Author’s Note on Scientific and Mathematical Frameworks
While this paper draws upon the historical realities of the NASA Space Race and employs structural language derived from classical mechanics, quantum physics, and computational theory, the mathematical models presented herein are strictly conceptual.
The equations introduced—including the continuity equation for Causal Information Preservation and the 3-Dimensional Phase Space of Agency—function as interdisciplinary analogs rather than rigorously provable theorems of theoretical physics. They are designed as philosophical scaffolding to visualise the abstract intersections of narrative theory, deterministic physics, and human cognition.
Furthermore, references to orbital mechanics and the predictive triumphs of the Apollo programme are utilised to illustrate the theoretical boundaries of information, measurement, and control. Ultimately, this paper is an exploration of causality and agency; its scientific and mathematical references should be interpreted as structural metaphors applied to human behaviour and narrative systems, rather than literal engineering guidelines or quantitative physics.
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