The Cycle of Objects: Life, Death, and the Limits of Understanding
The Nature of Objects and Their Lifecycle
Everything we interact with—whether physical or conceptual—follows a natural cycle: it comes into existence, evolves, and eventually ceases to be relevant. This cycle applies to everything from physical objects to ideas, scientific models, and technological frameworks.
Objects do not exist in isolation—they are often embedded within other objects, much like Russian nesting dolls, where each layer contains and depends on the one before it (Minsky, 1986).
This layered nature of objects influences how we perceive and interact with reality. Our sensory experience is inherently limited, revealing only surface interactions rather than the true essence of things. Just as a video game’s physics system operates based on complex code that looks nothing like what appears on the screen, reality itself may be governed by deeper structures that are inaccessible to direct observation. What we see and touch is merely an interface for a much more mysteriously hidden system (Turing, 1936).
Objects Within Objects: The Hidden Structure of Reality
In both nature and technology, objects exist within larger frameworks. Cells exist within organisms, planets within solar systems, and subatomic particles within atoms. This hierarchical structure is mirrored in software development, particularly in object-oriented programming (OOP), where objects contain other objects and encapsulate behavior and data in a modular way (Gamma et al., 1994).
However, the code that defines an object often bears little resemblance to its on-screen representation in a game or simulation.
This disconnect between representation and underlying reality is not unique to technology. The same principle applies to scientific theories, where models simplify and approximate reality rather than revealing its true nature. Every scientific model is an object within a larger conceptual framework, and as our understanding evolves, these models undergo refinement, replacement, or integration into broader theories (Feynman, 1965).
We are Agents that Modify Nature’s Objects
One profound realisation is that nature does not merely inspire our creations—it is its own analogue. The logic of a computer is not an artificial system imposed on reality; it emerges naturally from how the universe structures information.
If we see:
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the brain as a biological processor,
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the laws of physics as computational rules,
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and DNA as a form of code,
then computation is not an invention but a discovery (Bateson, 1972).
Even object-oriented programming mirrors the way the natural world is structured. Objects contain properties and behaviors, much like biological organisms possess traits and functions. Hierarchical inheritance in programming echoes genetic inheritance in evolution. Encapsulation—where an object contains and protects its own data—is reflected in cellular structures, where membranes control interactions and maintain internal processes (Gamma et al., 1994).
The Evolution and Death of Objects
No object, concept, or model exists indefinitely in its current form. They evolve through a cycle of refinement and obsolescence.
For example:
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Classical physics, once considered the ultimate framework for understanding the universe, eventually gave way to quantum mechanics and relativity when its limitations became apparent (Einstein, 1915; Planck, 1900).
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Even these newer models are incomplete, suggesting that they too will eventually be replaced by something more comprehensive.
Technology follows a similar path. Early mechanical computers gave way to electronic ones, which are now being reshaped by quantum computing and artificial intelligence. Each iteration builds on the past but also discards outdated elements. This process mirrors biological evolution, where organisms adapt and change, and those that fail to keep up with shifting environments are left behind.
The key takeaway: Complexity is not an end in itself.
When complexity becomes unwieldy, simplification must occur. Scientific theories that grow too convoluted collapse under their own weight, just as bloated software becomes inefficient and must be rewritten (Feynman, 1965).
Gödel’s Incompleteness and the Limits of Theoretical Frameworks
Kurt Gödel’s incompleteness theorems demonstrate that within any sufficiently complex system, there will always be truths that cannot be proven using the system’s own rules (Gödel, 1931).
This suggests:
In the context of objects, this means that no object—whether a scientific theory, a technological model, or a conceptual framework—can fully encapsulate all knowledge within itself.
There will always be missing pieces, aspects that must be understood through indirect observation, inference, and creative reinterpretation.
Rather than seeking a single "unified theory of everything", the more practical approach is to develop localized models that function effectively within their domains. This aligns with the philosophy of OOP, where objects are self-contained and operate within their intended scope.
Unification, if it occurs, must happen at the level of frameworks, not within any single theory (Turing, 1936).
Conclusion: Embracing the Object Cycle
Understanding objects as evolving, interconnected entities allows us to appreciate the necessity of both complexity and simplification.
Objects emerge, grow, and eventually reach a point where they must be replaced or restructured. Whether in science, technology, or philosophy, this cycle is essential for progress.
The key is to recognize that:
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No model or system can fully encapsulate reality.
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Instead of seeking absolute completeness, we should focus on developing adaptable, efficient frameworks that remain functional within their scope.
By embracing:
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the limitations of knowledge, and
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the necessity of continual refinement,
we move closer to a more profound understanding of the nature of objects and the reality they inhabit.
References
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Bateson, G. (1972). Steps to an Ecology of Mind. Ballantine Books.
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Einstein, A. (1915). Die Feldgleichungen der Gravitation. Sitzungsberichte der Königlich Preußischen Akademie der Wissenschaften.
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Feynman, R. P. (1965). The Character of Physical Law. MIT Press.
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Gamma, E., Helm, R., Johnson, R., & Vlissides, J. (1994). Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley.
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Gödel, K. (1931). Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I. Monatshefte für Mathematik und Physik, 38(1), 173–198.
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Planck, M. (1900). On the Theory of the Energy Distribution Law of the Normal Spectrum. Annalen der Physik, 4(3), 553–563.
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Turing, A. M. (1936). On Computable Numbers, with an Application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, 2(42), 230–265.
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Minsky, M. (1986). The Society of Mind. Simon and Schuster.
