Computation and cooperation evolve together in self-replicating programs
Tapes Together Strong: The Co-evolution of Computation and Cooperation
Multiagent SystemsArtificial Intelligence
Summary
The paper studies how cooperation can naturally emerge in complex systems that both compute and replicate themselves. The researchers created a computational model where social behaviors, resource use, and replication evolve together, rather than being separate factors. They found that when resources are limited, selfish cheating behaviors harm shared energy and slow down replication, which encourages cooperative strategies to win out. Their experiments using small computer programs show that cooperative behaviors help these programs replicate better and perform tasks more reliably.
What this means in practice
- •For distributed systems engineers: Design distributed systems that naturally encourage cooperation among processes when resources are limited, improving stability and performance.
- •For robotics developers: Develop multi-robot systems where energy constraints and collaborative behaviors evolve together to improve task reliability and efficiency.
Authors
Kunal Jha, Francesco Cicala, Blaise Agüera y Arcas, Blake Aaron Richards, Natasha Jaques, Max Kleiman-Weiner, Eyvind Niklasson
Abstract
How does cooperation evolve in complex agentic systems? Prior work in evolutionary game theory studies why individuals are incentivized to cooperate by isolating social interactions from the physical costs of behavior, while artificial life models traditionally study emergent self-replication without formalizing the dilemma between acquiring resources and preserving the shared energy needed to reproduce. In contrast, we introduce Autopoietic Game Theory, a computational model where social interactions, replication mechanisms, and their associated computational costs are endogenous and simultaneously co-evolving. We study these dynamics using a computational substrate of randomly initialized programs in Z80 machine code, showing empirically, and motivating with a simplified theoretical model, that embedding a social dilemma directly into the physics of computation can favor the emergence of self-replicating, cooperative strategies. When resources are scarce, our analysis shows that defection can become self-limiting even in well-mixed populations: parasitic stealing destroys shared energy, slows execution, and can prevent reliable replication. Empirically, evolved programs suppress stealing across several Z80 environments, while spatial assortment further supports structural complexity and task performance. We further show that the framework can incorporate exogenous pressures, such as math tasks structured as sequential social dilemmas, when rewards are tied to computation budgets. These results suggest that coupling an agent's capacity for computation to its available energy transforms cooperation into a dominant scaffolding for building sustainable, self-organizing systems.