基于协同效应的信息修改度量方法
Towards a Synergy-based Approach to Measuring Information Modification
摘要：
人工生命和复杂系统中的分布式计算通常被描述为对信息的组件操作：信息存储，传输和修改。然而，信息修改仍然描述得很差，在细胞自动机中滑翔机和粒子碰撞的普遍理解的例子到目前为止只使用启发式（可分离信息）而不是适当的信息理论测量来定量识别。我们概述了如何最近推出的公理框架测量信息冗余和协同作用，称为部分信息分解，可以应用到分布式计算的角度，以量化组件操作的信息。使用这个框架，我们提出了一个新的信息修改措施，捕获的信息修改事件的直观理解，涉及两个或两个以上的信息源之间的相互作用。我们还考虑如何在空间和时间的信息修改的本地动态可以测量，并提出了一个新的公理，冗余措施将需要满足，以使这样的本地测量。最后，我们评估现有的冗余措施，以满足这一本地化公理的潜力。
英文摘要：
Distributed computation in artificial life and complex systems is often described in terms of component operations on information: information storage, transfer and modification. Information modification remains poorly described however, with the popularly-understood examples of glider and particle collisions in cellular automata being only quantitatively identified to date using a heuristic (separable information) rather than a proper information-theoretic measure. We outline how a recently-introduced axiomatic framework for measuring information redundancy and synergy, called partial information decomposition, can be applied to a perspective of distributed computation in order to quantify component operations on information. Using this framework, we propose a new measure of information modification that captures the intuitive understanding of information modification events as those involving interactions between two or more information sources. We also consider how the local dynamics of information modification in space and time could be measured, and suggest a new axiom that redundancy measures would need to meet in order to make such local measurements. Finally, we evaluate the potential for existing redundancy measures to meet this localizability axiom.
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