磁带协同强：计算与合作的共同演化
Tapes Together Strong: The Co-evolution of Computation and Cooperation
- University of Washington（华盛顿大学）
- Google Paradigms of Intelligence（谷歌智能范式团队）
- McGill University（麦吉尔大学）
- Mila（米拉研究所）
- Google DeepMind（谷歌DeepMind）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出自创生博弈论计算框架，将社会困境嵌入Z80机器码计算物理中，实证表明资源稀缺时合作策略可涌现并抑制窃取，为可持续自组织系统提供新路径。
AI中文摘要：
复杂智能体系统中的合作是如何演化的？先前演化博弈论的研究通过将社会互动与行为的物理成本相分离，来探究个体为何有动力合作；而人工生命模型传统上研究涌现性自我复制，却未形式化获取资源与保存繁殖所需共享能量之间的困境。相比之下，我们引入了自创生博弈论（Autopoietic Game Theory），这是一种计算模型，其中社会互动、复制机制及其相关计算成本是内生的，并同时共同演化。我们使用随机初始化的Z80机器码程序作为计算基质来研究这些动态，经验性地表明（并用简化理论模型加以论证），将社会困境直接嵌入计算物理中，可以促进自我复制、合作策略的涌现。当资源稀缺时，我们的分析表明，即使在充分混合的群体中，背叛也可能变得自我限制：寄生性窃取会破坏共享能量、减慢执行速度，并可能阻碍可靠复制。经验上，演化出的程序在多个Z80环境中抑制了窃取行为，而空间分选进一步支持了结构复杂性和任务性能。我们还表明，该框架可以纳入外部压力，例如结构化为顺序社会困境的数学任务，当奖励与计算预算挂钩时。这些结果表明，将智能体的计算能力与其可用能量耦合，可将合作转化为构建可持续、自组织系统的主导性支架。
英文摘要：
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.
