自然界中的软件：一种面向分层涌现的计算方法
Software in the natural world: A computational approach to hierarchical emergence
AI总结：
针对现有涌现理论难以解释其发生机制的问题，提出一种基于计算能力的涌现框架，将宏观过程视为自包含的计算层级，揭示了复杂系统的多层功能架构，并展示了其在高效模拟、预测和控制方面的潜力。
摘要：
理解复杂系统的功能架构对于阐明其内部运作机制并实现有效的预测与控制方法至关重要。近期的进展引入了表征涌现宏观层面的工具；然而，尽管这些方法在识别涌现何时发生方面取得了成功，但在确定其如何发生方面却存在局限性。在此，我们通过开发一种涌现的计算方法来解决这一局限，该方法根据计算能力来表征宏观过程。具体而言，我们基于软件的工作原理提出了一种涌现观，其植根于一种数学形式体系，阐明了宏观过程如何表达自包含的信息性、干预性和计算性属性。该框架建立了一个嵌套的自包含过程层级，确定了在何种层级发生何种计算，这进而描绘了复杂系统的功能架构。这种方法在统计物理学和计算神经科学文献中的经典模型上得到了说明，这些模型被证明展现出类似于人类工程系统中软件的宏观过程。总体而言，该框架能够更深入地理解复杂系统的多层结构，揭示了其可被高效模拟、预测和控制的具体方式。
英文摘要：
Understanding the functional architecture of complex systems is crucial to illuminate their inner workings and enable effective methods for their prediction and control. Recent advances have introduced tools to characterise emergent macroscopic levels; however, while these approaches are successful in identifying when emergence takes place, they are limited in the extent they can determine how it does. Here we address this limitation by developing a computational approach to emergence, which characterises macroscopic processes in terms of their computational capabilities. Concretely, we articulate a view on emergence based on how software works, which is rooted on a mathematical formalism that articulates how macroscopic processes can express self-contained informational, interventional, and computational properties. This framework establishes a hierarchy of nested self-contained processes that determines what computations take place at what level, which in turn delineates the functional architecture of a complex system. This approach is illustrated on paradigmatic models from the statistical physics and computational neuroscience literature, which are shown to exhibit macroscopic processes that are akin to software in human-engineered systems. Overall, this framework enables a deeper understanding of the multi-level structure of complex systems, revealing specific ways in which they can be efficiently simulated, predicted, and controlled.
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