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Tapes Together Strong: The Co-evolution of Computation and Cooperation — arXiv:2609.10817

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.10817
License (as archived): CC0-1.0
Archived: 2026-10-16T05:20:27.322Z
SHA-256 of the archived text: 7904544e8f0652cd0c1c4f7e33cde6ed9b2f8adab0807dd0c52c0419a964ec78
Stable URL of this page: https://metraipolis.com/documento/dc_583bbafcdfeb

How to cite

Tapes Together Strong: The Co-evolution of Computation and Cooperation — arXiv:2609.10817 — arXiv (en), https://arxiv.org/abs/2609.10817 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_583bbafcdfeb (Metraipolis archive dc_583bbafcdfeb, sha256 7904544e8f0652cd0c1c4f7e33cde6ed9b2f8adab0807dd0c52c0419a964ec78)

BibTeX

@misc{dc583bbafcdfeb,
  title = {Tapes Together Strong: The Co-evolution of Computation and Cooperation — arXiv:2609.10817},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.10817},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_583bbafcdfeb; sha256 7904544e8f0652cd0c1c4f7e33cde6ed9b2f8adab0807dd0c52c0419a964ec78},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.10817. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-11 in cs.MA).
Categorie arXiv: Multiagent Systems (cs.MA) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.10817 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T05:15:07.133Z dalla pagina https://arxiv.org/catchup/cs.MA/2026-09-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).