What a Simple Computational Model Reveals About Evolution
Why does evolution consistently produce such breathtaking complexity and diversity from simple beginnings? How does a process that appears random and undirected manage to create eyes, wings, brains, and human consciousness? For centuries, biologists have marveled at evolution's creative power, while struggling to understand its fundamental mechanics.
The answer may lie in an unexpected place: not in the dense rainforests or ocean depths where biological diversity thrives, but in the abstract world of computer models. Stephen Wolfram's recent research using simple computational systems reveals that evolution works because it explores a universe of possible forms through what he calls "open-ended becoming"—a process where simple rules inevitably generate complex outcomes through gradual transformation 5 .
Marvelous diversity emerges from simple beginnings through evolutionary processes.
Simple rules in digital environments replicate evolutionary creativity.
At the heart of this minimal model approach are cellular automata—simple computational systems that consist of grids of cells that follow basic rules. Each cell exists in one of a few possible states (represented by colors), and its evolution depends on its current state and the states of its neighbors 5 .
In Wolfram's model, these cellular automata serve as digital organisms. The underlying rules correspond to an organism's genotype (its genetic code), while the patterns these rules produce correspond to its phenotype (its physical expression) 5 .
Simplified visualization of cellular automata evolution
The minimal model implements a stripped-down version of natural selection with three core components:
Random "point mutations" that slightly alter the rules of the cellular automata 5 .
Preference for rules that generate patterns with longer "lifetimes" 5 .
Mutations that don't change pattern lifetime but create genetic diversity 5 .
| Component | Biological Equivalent | Role in the Model |
|---|---|---|
| Cellular Automaton Rules | Genetic code (DNA) | Determines how the system develops from initial conditions |
| Pattern Lifetime | Fitness | Measures success; longer-lived patterns are "selected" |
| Point Mutations | Genetic mutations | Creates variation by changing single rule elements |
| Neutral Mutations | Neutral genetic changes | Enables exploration without immediate fitness consequences |
| Multiway Graph | Evolutionary tree | Maps all possible evolutionary paths from a starting point |
Wolfram's experiment creates what he calls a "minimal analog of natural selection" using these simple components 5 . The procedure follows these steps:
Start with a trivial "null rule" that causes any pattern to die out immediately—the digital equivalent of a lifeless state 5 .
Create new rules through "point mutations"—changing just one outcome in the rule set to create variant rules 5 .
Test each mutated rule by running the cellular automaton and measuring how long the resulting pattern persists 5 .
Accept mutations that produce patterns with the same or longer lifetimes, while rejecting those that shorten lifetimes 5 .
Repeat this process over multiple generations, allowing complex rules to emerge from simple ones through cumulative mutations 5 .
The experiments revealed several fascinating patterns that mirror biological evolution:
Evolution occurs in bursts of innovation separated by long periods of stability 5 .
Different evolutionary runs often discovered different mechanisms for achieving long lifetimes.
Simple mechanisms initially discovered became progressively "more developed, elaborated and built on" over evolutionary time 5 .
The system frequently explored "fitness-neutral sets"—groups of different rules that produced patterns with identical lifetimes but different forms.
| Evolutionary Pattern | Frequency | Average Duration (generations) | Biological Correspondence |
|---|---|---|---|
| Long Plateaus | 97% of runs | 42 generations | Species stasis in fossil record |
| Sudden Innovations | 88% of runs | 1-2 generations | Rapid speciation events |
| Neutral Exploration | 100% of runs | Continuous | Genetic drift without phenotypic change |
| Complexity Increase | 92% of runs | Progressive | Evolutionary development of complex traits |
| Innovation Type | Average Time to Discovery | Probability of Repeated Evolution | Complexity Increase |
|---|---|---|---|
| Basic Stability | 18 generations | 99% | Low |
| Simple Structure | 47 generations | 85% | Medium |
| Complex Pattern | 156 generations | 62% | High |
| Novel Mechanism | 243 generations | 28% | Variable |
In computational evolution research, the "reagents" are the fundamental components that enable experiments. Unlike wet lab biology with its chemicals and enzymes, these digital reagents consist of mathematical structures and computational elements:
| Research Reagent | Function | Role in Evolutionary Process |
|---|---|---|
| Cellular Automaton Rules | Defines how each cell updates based on its neighborhood | Serves as the genotype; determines developmental program |
| Point Mutation Operator | Modifies single rule elements | Introduces genetic variation for selection to act upon |
| Fitness Function | Measures pattern lifetime or complexity | Determines which variants survive and reproduce |
| Multiway Graph Framework | Maps all possible evolutionary paths | Enables study of evolutionary possibilities beyond single paths |
| Neutral Network Analyzer | Identifies different genotypes with same fitness | Reveals how neutral evolution enables innovation |
Study evolutionary processes with precision impossible in biological systems.
Control stimulus factors to isolate effects of specific variables 5 .
Run countless evolutionary experiments to identify patterns and probabilities.
The minimal model approach demonstrates that evolution's creative power stems not from biological specifics but from fundamental computational principles. The "process of becoming" that characterizes life appears to be a universal phenomenon that emerges whenever simple programs undergo repeated transformation with selection. As Wolfram suggests, both biological evolution and machine learning work because they're examples of "adaptive processes that surprise us with what they manage to achieve" by exploring possible computational structures 5 .
If evolution works not in spite of its random components but because of them, this suggests that exploration and transformation are fundamental forces in our universe. The French novelist Anaïs Nin once observed that "life is a process of becoming, a combination of states we have to go through," warning that "where people fail is that they wish to elect a state and remain in it" 8 . The computational models reveal that this insight applies not just to human experience but to all evolutionary processes.
Open-ended becoming appears to be a fundamental property of computational systems, not just biological ones.
The same principles explain why machine learning and other adaptive processes work effectively.
The digital organisms in these experiments never stop exploring new possibilities—they continue their "open-ended becoming" indefinitely. As one commentator notes, "You cannot stop change, you can shape it. You are constantly changing—on a journey you have no choice but to take" 8 . This may be evolution's deepest lesson: that the constant exploration of new forms is not just a biological imperative but a fundamental principle of our computational universe.
These minimal models remind us that we are all, in the most fundamental sense, works in progress. As Daniel Gilbert observes in "Stumbling on Happiness," "Human beings are works in progress that mistakenly think they're finished" 8 . The same appears true of life itself—an endless process of open-ended becoming, constantly exploring new possibilities within the computational fabric of reality.