Human Simulation is Possible - by Humans

 Human Simulation: Agent-Based Modeling and the Computational Case for Our Reality

 

1.      Introduction

In the essay, we introduce the notion that humanity could be a deliberate simulation based on an extremely simple single-celled organism, hardly anything more complex than that. This will allow an (apparently) new interpretation of the notion that humanity is simulated, but that there is no master engineer at a massive number of dials and switches, responsible for every twist and turn of our universe and lives. We begin with the notion that the universe exists and operates under its own laws. So, given the Earth[2], when and where life first began, it was infused with a living agent, self-actualized, reproductive, and with other basic rules for its operations, interactions, and much of what single-celled organisms do today. The idea from this occurred some years back when writing code in ActionScript (formerly Flash), I was able to create multiple instantiations of the same code with similar rules of operation. It then functioned according to the rules set forth with no input from me. It was a simplistic world with only a few dozen inhabitants that interacted independently of myself. Being an average coder at best, I never pursued it, but the idea of agent-based-modeling has never left me. This essay is about a far more clever agent basis, programmed with options for reproduction and even evolution. It offers a plausible argument that we could be a computational simulation with no one in charge. It runs parallel to notions of a God,[3] championed by Voltaire, Benjamin Franklin, and others, who sets the clockwork of life and then walks away, allowing it to progress as it may. 

We note that the foundations are not new philosophically (see §3) and have been widely considered. However, the method outlined here for doing so appears to be original. Moreover, we are not solving the God hypothesis, though perhaps shifting its focus.

2.      Introduction to Human Simulation as Evolved from the Simplest Organisms

The notion of human simulation posits that conscious beings like ourselves are not fundamental but emergent products of a vast computational framework; they are essentially autonomous software agents executing within a programmed universe. This perspective begins not with complex human societies but with the simplest possible organisms, mirroring the evolutionary arc observed in both biology and artificial life experiments. In the earliest stages of any such simulation, primitive agents would operate under minimal rule sets: basic sensory inputs for detecting resources or threats, rudimentary survival behaviors such as energy procurement (foraging), rest and restoration (maintenance cycles, also stimulating dangerous so-called greedy algorithms), reproduction, and an innate sense of danger (fight-or-flight responses). These agents would resemble single-celled prokaryotes in the primordial soup, self-replicating entities driven by local rules rather than centralized control. Over iterative cycles, imperfect replication introduces variation, allowing natural selection to sculpt more sophisticated forms: multicellular aggregates, specialized sensory apparatuses, and eventually social structures. This progression is not hypothetical; it is precisely how artificial life simulations demonstrate the emergence of complexity from simplicity. Just as biological evolution scaled from bacteria to Homo sapiens through incremental adaptations, a simulated reality could bootstrap human-level cognition from the most elementary programmable units. 

The agents self-actualize: once instantiated with their initial properties, they operate independently within the environment’s physics engine, generating behaviors the original “programmers” (whether posthuman simulators or some deeper substrate) never explicitly scripted. Imagine this: the code evolves before the biological agent. This bottom-up emergence provides a convincing computational scaffold for why our lived experience, from microbial origins to technological civilizations, feels organically real rather than top-down orchestrated. Even the free-will problem is satisfied. With such complexity as has evolved, and with so many millions of variables, it becomes indistinguishable what is free will (meaning what, exactly?) from system programming.

3.      The Philosophical Basis for Simulation

The simulation hypothesis gained rigorous philosophical articulation through the work of Nick Bostrom, who formalized it as a trilemma in his 2003 paper. Bostrom argues that at least one of the following must be true: (1) the fraction of human-level civilizations that reach a “posthuman” stage is very close to zero (i.e., most go extinct); (2) the fraction of posthuman civilizations interested in running “ancestor-simulations” (detailed recreations of their evolutionary history) is very close to zero; or (3) the fraction of all observers with experiences like ours who are living in a simulation is very close to one. Given the enormous computational resources available to posthuman societies, the statistical likelihood tilts heavily toward the third proposition: we are almost certainly simulated. 

This modern argument builds on deeper roots in digital physics. In 1969, computer pioneer Konrad Zuse proposed in Rechnender Raum , likely a cellular automaton, challenging the assumption of continuous physical laws and suggesting reality is fundamentally digital. 

Similarly, physicist John Archibald Wheeler’s “It from Bit” doctrine (1989) asserted that every physical “it”—particles, fields, spacetime itself—derives its existence from binary yes/no questions and informational bits, rendering the universe participatory and information-theoretic at its core.Conway's Game of Life, conceived by mathematician John Horton Conway in 1970, stands as one of the simplest yet most profound demonstrations of emergent complexity in a computational universe. It consists of an infinite two-dimensional grid of cells, each either alive or dead, that evolves in discrete time steps according to four local rules applied simultaneously to every cell: any live cell with fewer than two live neighbors dies (underpopulation), any live cell with two or three live neighbors survives, any live cell with more than three live neighbors dies (overpopulation), and any dead cell with exactly three live neighbors becomes alive (reproduction). Despite the extreme minimalism of these rules—no central controller, no global plan, no explicit programming of higher-order behavior—the grid spontaneously generates intricate, lifelike patterns: stable “still lifes,” oscillating “blinkers,” gliding “spaceships,” self-replicating structures, and even configurations capable of universal computation (Turing completeness). In the context of human simulation and agent-based modeling, Conway’s Game of Life serves as a foundational proof-of-concept: if such breathtaking complexity, evolution-like adaptation, and apparent “agency” can arise from nothing more than local neighbor interactions on a digital substrate, then the leap to full-scale simulated realities—complete with sensory agents, evolutionary pressures, inter-agent communication, and emergent civilizations—becomes not merely plausible but inevitable. It illustrates how a simulation hypothesis could bootstrap human experience from the simplest programmable rules, mirroring the bottom-up emergence we observe in biological evolution and artificial life models, and reinforcing that our own rich, self-actualizing existence may be the natural outcome of an unimaginably vast cellular-automaton-style computation.

Together, these ideas frame simulation not as science fiction but as a coherent metaphysical possibility: our reality could be a high-fidelity computation running on an unknowable substrate, with humans as self-actualizing subprocesses.

4.      What Is Agent-Based Modeling (ABM)?

Agent-based modeling is a computational methodology in which individual autonomous “agents” (software objects) are defined with internal states, sensory capabilities, decision rules, and goals, then placed in a simulated environment where they interact locally to produce global, emergent phenomena. Unlike equation-based models that describe systems from the top down, ABMs build complexity from the bottom up: simple local rules generate unpredictable macro-level outcomes without a central controller. Each agent operates as an independent thread—perceiving its surroundings, evaluating options according to programmed heuristics (e.g., energy thresholds, threat detection), and acting accordingly. The programmer supplies only the initial class definition and rule engine; thereafter, the agents self-actualize, adapting through interaction. Classic implementations, such as Joshua Epstein and Robert Axtell’s Sugarscape (1996), equip agents with metabolism, vision, movement, and reproduction on a resource-scarce grid, allowing entire artificial societies, complete with trade, conflict, and cultural transmission, to arise spontaneously. 

ABM thus serves as both a scientific tool for studying complex systems and a practical demonstration that lifelike behavior can emerge from coded units.

5.      How ABM Explains Bird Flocking (Murmuration) and Other Multi-Unit Behavior

One of the most elegant illustrations of ABM’s power is the simulation of bird flocking, or murmuration—those mesmerizing, synchronized aerial ballets performed by starlings and other species. In 1986, computer graphics researcher Craig Reynolds developed the “Boids” model, which reproduces flocking using only three simple local rules per agent:

1. Separation: Steer to avoid crowding nearby flockmates.
2. Alignment: Steer toward the average heading of nearby flockmates.
3. Cohesion: Steer toward the average position of nearby flockmates.

No global “flock leader” or precomputed choreography is required. Each bird perceives only its immediate neighbors through a limited sensory radius, yet the collective result is fluid, organic group motion indistinguishable from real avian behavior. The same (crowd intelligence) framework explains fish schools, insect swarms, and even pedestrian crowd dynamics: local rules plus inter-agent proximity yield global coherence. In a simulated universe, such multi-unit behaviors demonstrate how higher-order phenomena such as herds, societies, ecosystems, scale effortlessly from individual agent interactions, reinforcing the plausibility that our own social and biological complexities could likewise emerge within a computational framework.

6. How Imperfect Reproduction Allows Evolution

Evolution in ABM arises directly from imperfect reproduction coupled with selection pressure. Agents are typically endowed with a “genome,” a set of heritable parameters (metabolic rate, sensory range, behavioral biases) that is copied during reproduction. Crucially, this copying is noisy: small random mutations or crossover events introduce variation. Successful agents (those that acquire more resources, avoid threats, and reproduce more often) pass on their genomes disproportionately, while less fit variants die out. This mirrors natural selection but runs orders of magnitude faster in silico. In Sugarscape, agents reproduce with genetic inheritance and mutation, leading to evolutionary adaptation: populations shift toward optimal vision-metabolism trade-offs or develop cooperative strategies.

 Melanie Mitchell’s (Mitchell, 1994) work on genetic algorithms in artificial life further demonstrates how such mechanisms enable open-ended evolution, from simple trait optimization to complex behaviors such as learning and tool use.

 Perfect reproduction is therefore the engine of novelty; without it, agents would remain static clones. In a human simulation, this same mechanism would explain the transition from simple prokaryotic agents to the genetic and cultural diversity we observe, providing a self-sustaining pathway for ever-greater complexity without external intervention. Perfect reproduction, on the other hand, would render a static and probably unstable continuation. One might conclude that reproductive imperfections may grow with succeeding evolutionary changes, possibly nullifying continuing intellectual improvements over multiple generations - a built-in terminal effect.

7. What Is the Impact of Inter-Agent Communication? Why Is It Necessary for Advances in Knowledge?

Inter-agent communication transforms isolated survival machines into collective intelligences capable of cumulative progress. In ABM, agents exchange information via signals, pheromones in simple models, symbolic languages in advanced ones, enabling coordination, division of labor, and the transmission of learned behaviors across generations. Without communication, knowledge remains trapped within individual lifespans; with it, agents build shared cultural repositories, refine strategies collaboratively, and accelerate adaptation. Luc Steels’ agent-based models of language emergence, for instance, show how populations of agents spontaneously develop grammatical systems through iterated interactions, solving coordination problems that no single agent could master alone.

In Sugarscape extensions, trade and cultural exchange produce inequality, norms, and technological innovation. Communication is thus indispensable for “advances in knowledge” because it creates a ratchet effect: discoveries compound rather than reset with each death. In a simulated reality, human language, science, and technology would be the inevitable outcome of scaling this rule upward, thereby turning a population of self-actualizing agents into a civilization that questions its own existence.

8. Why Is the Concept of ABM and Self-Actualization Viable for the Simulation Hypothesis to Be Valid?

The marriage of ABM and self-actualization supplies the missing mechanistic bridge for Bostrom’s simulation argument to hold. If advanced civilizations can—and would—run ancestor-simulations, the agents within them must feel indistinguishable from “base reality” inhabitants. ABM delivers exactly this: agents are not remote-controlled puppets but sovereign objects executing their own decision loops, complete with sensory inputs, rule-based autonomy, evolutionary pressures, and social dynamics. From the inside, there is no observable “code”; qualia, free will, and meaning arise as emergent properties of the rule engine. Self-actualization ensures the simulation is worth running: static or centrally dictated agents would produce boring, predictable outcomes. Only autonomous agents generate genuine novelty, creativity, and the rich observer experiences Bostrom requires for his statistical conclusion. Because we ourselves routinely build such lifelike simulations (Boids, Sugarscape, modern multi-agent AI), the recursive implication is clear: if we can do it, posthumans could do it better. Thus, ABM renders the simulation hypothesis not merely philosophically plausible but computationally inevitable. As well, simply beginning with the notion of ABM, one can derive many of the predetermined philosophical, genetic, and evolutionary ruminations.

9.     Conclusion

Viewing humanity through the lens of agent-based modeling transforms the simulation hypothesis from abstract speculation into a concrete, testable framework. From the simplest rule-governed organisms to murmuring flocks, evolving populations, communicating societies, and finally self-reflective humans, every layer emerges naturally once the basic survival heuristics and advanced social rules are instantiated. Bostrom’s trilemma, Zuse’s digital physics, and Wheeler’s informational ontology provide the philosophical scaffolding; ABM supplies the working blueprint. We are the agents, self-actualizing, evolving, communicating, running our loops within a substrate whose full nature may forever remain hidden. Whether base reality or simulation, the elegance of this architecture suggests that the universe, at its core, could be a grand computational experiment in emergence. Recognizing ourselves as such need not diminish our wonder; it may instead deepen our appreciation for the rules that make existence possible.

These possibilities do not rely on probabilistic models of chemistry, as in intelligent design, or a master engineer that keeps the wheels of existence turning. It is the simplicity of allowing all the work to be done through agents that reproduce themselves. It does not, however, resolve the God problem. What we offer is a mechanism for simulation,  not a mechanism to support the mechanism.

 Finally, while I’m not convinced we exist in a simulation, even a loosely agent-modeled one, it is certainly a way to test and experiment with how evolution works. This brings us to reality, but reality itself is very tricky. To modify a statement by Richard Feynman about quantum mechanics, “Just when you think you understand reality, you don’t really understand reality.”

References

• Bostrom, N. (2003). “Are You Living in a Computer Simulation?” Philosophical Quarterly, 53(211), 243–255. Available at: https://simulation-argument.com/simulation.pdf.
• Reynolds, C. W. (1987). “Flocks, Herds, and Schools: A Distributed Behavioral Model.” Proceedings of SIGGRAPH ’87. (Original Boids work, 1986).
• Epstein, J. M., & Axtell, R. (1996). Growing Artificial Societies: Social Science from the Bottom Up. Brookings Institution Press/MIT Press.
• Zuse, K. (1969). Rechnender Raum (Calculating Space). English translation available via various archives.
• Wheeler, J. A. (1990). “Information, Physics, Quantum: The Search for Links.” In Complexity, Entropy, and the Physics of Information.
• Mitchell, M. (1994). “Genetic Algorithms and Artificial Life.” Artificial Life, 1(3).
• Steels, L. (2016). “Agent-based models for the emergence and evolution of language.” Philosophical Transactions of the Royal Society B.

 

 G Donald Allen[1]
Department of Mathematics
Texas A&M University
College Station, TX 77845

©2026

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[1]. Professor Emeritus.

[2]. Of course, it could be Mars or any other planet with the agent model modified for the conditions at hand. As well, Earth has changes in conditions according to its own dynamics – the drivers of evolution.  

[3]. The God, in this case, would not need to be much smarter than we are, just more capable of building biological models. This is 17^th and 18^th-century Deism, probably a consequence of the success of Newtonian mechanics.