On Evolutionary Simulation Machines and My Partial Experience Studying Them
Background: This concept emerged serendipitously. During high school, I attended a lecture that mentioned game theory, which deeply interested me. I purchased a book on social game theory online, where evolutionary games prompted me to explore the essence of biological evolution. Following my cognitive patterns, I extrapolated this to all phenomena and combined it with previously learned knowledge about chaos theory and fractal imagery, leading to this idea: evolution is driven by environmental selective pressure, while environments result from the comprehensive interactions of countless natural elements. If simulated computationally, mapping my will as the operator onto the simulated comprehensive environment, could I guide and compel specific populations to undergo changes aligned with my intentions?
This should be feasible: each guiding force I create in the environment corresponds to altering how groups respond to or interact with external conditions in specific directions. I would then establish a conversion relationship between this program and DNA organizational computation, mapping results back onto DNA sequences.
For instance, to make humans grow wings, I must create an appropriate environment. I could reference evolutionary history—what environmental factors drove animals to develop wings. Though the factors enabling animal flight may be extremely complex (including existing body structure influences, whether aerial lifestyle offers advantages, etc.), I could utilize sufficient simulation schemes to determine which environmental conditions yield specific biological structures and states. By repeatedly refining this process to obtain optimal solutions and converting them into actual DNA sequences, this evolutionary simulation of organisms could essentially constitute a comprehensible computational system (if this DNA editing approach proves viable).
For unprecedented modifications—such as enabling higher mammals to achieve non-aging, infinite lifespans—I, as the theoretical creator, subscribe to The Selfish Gene's assertion that "organisms are extended phenotypes of genes, existing to serve genetic propagation." Individual mortality appears inevitable. However, precisely because genes and their byproducts (viruses) determine primary biological behaviors—birth, aging, disease, death—modifying genes could modify these phenomena. Birth, aging, disease, and death are not necessary; I could simulate organisms lacking these states within configured environments, potentially developing novel structures. The crucial insight here is: I need not understand various physical or chemical mechanisms—I only know that within my selected environment, multiple structures develop, continuously evolving until I achieve desired results through DNA sequences capable of actual biological function.
Compared to current gene editing, this approach would be less disruptive. With proper initial evolutionary samples and controlled environmental changes, organisms would gradually transition to final states as if naturally intended. Gene editing forcibly alters specific sequences from limited perspectives, potentially causing unknown problems, while evolutionary simulation enables sequence evolution through comprehensive environmental changes, producing normal biological states with minimal issues. Moreover, gene editing involves complex gene identification, intricate operations, and limited modification scope—disadvantages obvious compared to evolutionary simulation.
However, I recognize significant model limitations. Primarily, it lacks concrete implementation, and as a humanities-focused high school student, I lack necessary physics, chemistry, and biology knowledge (though this is debatable for complex reasons). At that stage, practical implementation opportunities were impossible—merely minor exploration amid examination pressures. Additionally, while evolutionary simulation appears simple in description, each iteration involves numerous selection operations, potentially creating NP problems no simpler than editing gene segments. Furthermore, how can environments map to specific numerical processing? How can finite symbols express infinite hierarchical, endless environmental selection effects on organisms? (This concept resembles environmental selection but requires sufficient reflection of real-world diversity rather than human-willed genetic algorithms and parameters.) How would I encode program results into DNA sequences? Problems abound—the more considered, the more issues emerge.
(These were my second-year thoughts, poorly remembered, so logical flow may be problematic.) This remained mere speculation. Amid academic duties, I extensively explored related concepts: biomimetics, emergence, automata, self-organization, self-similarity, initial perturbation, nonlinear dynamics, complexity science, etc. (This lengthy process generated numerous ideas but no significant new actions or deeper exploration). Until my senior year, I completely overhauled my original model, conceiving an entirely new framework.
(Throughout high school until May of senior spring, my exploration remained somewhat chaotic—directional but with vague objectives, which wasn't problematic since targets naturally exist at interdisciplinary boundaries. This fueled my passion for continuously learning and deriving new conclusions through various explorations related to other innovations. For simplicity, I'll skip many sections and briefly describe my major conceptual renovation.)
Mathematical Model Establishment: I adopted and expanded cellular automata theory perspectives, transforming environmental pressure into multi-layered (converted to parallel DNA computation) impact systems affecting biological order. Based on potential practical applications, I planned to classify major evolutionary scenarios, intervention methods, and results to ensure smooth industrial implementation.
DNA-based Modification Methods: Recognizing DNA molecules' inherent self-organizing systemic characteristics, I determined two biological modification approaches: A) Intervening in existing biological DNA (viral mechanisms as potential forms), converting computer results to DNA sequences; B) Establishing molecular computers directly on DNA, creating DNA-based "source code" (not literally, just descriptive), running automata on DNA without existing DNA (historical) interference, generating entirely new biological forms.
Computational Capacity: DNA's powerful computational ability could resolve the massive calculation challenges mentioned above (finite computational capacity facing enormous environmental variable spaces). Even computer-based operations could employ dynamically generated new spaces and fuzzy boundary determination methods (conceptual rather than specific, but solutions exist) to significantly reduce computational demands. However, I favor DNA molecular computers as the future.
I have additional descriptions and modifications, but these three points represent major model transformation.
Finally, my reflections: this transcends mere theory or speculation. Through contemplating it, I extensively explored diverse fields, ultimately forming new worldviews and fresh perspectives on many phenomena, plus potential transformation approaches. The described machine is perhaps terrifying upon reflection—conveniently modifying organisms according to personal will. Yet this remains speculation, and even if realized, the world's specific configuration cannot be predicted through simple fear-based reasoning.
Teachers may retain many questions. To understand this concept, I would need to introduce more elements, explaining how my worldview interprets various worldly phenomena and problems, enabling teachers to comprehend how this invention emerged. Naturally, I hope teachers have time and interest to listen face-to-face to my new perspectives on humanity and nature developed while constructing evolutionary simulation machines—perhaps the optimal way for teachers to understand my entrepreneurial innovations.