In the present work, we describe a framework for modeling how models can be built that integrates concepts and methods from a wide range of fields. The information schism between the real-world and that which can be gathered and considered by any individual information processing agent is characterized and discussed, followed by the presentation of a series of the adopted requisites while developing the modeling approach. The issue of mapping from datasets into models is subsequently addressed, as well as some of the respectively implied difficulties and limitations. Based on these considerations, an approach to meta modeling how models are built is then progressively developed. First, the reference M* meta model framework is presented, which relies critically in associating whole datasets and respective models in terms of a strict equivalence relation. Among the interesting features of this model are its ability to bridge the gap between data and modeling, as well as paving the way to an algebra of both data and models which can be employed to combine models into hierarchical manner. After illustrating the M* model in terms of patterns derived from regular lattices, the reported modeling approach continues by discussing how sampling issues, error and overlooked data can be addressed, leading to the $M^{<\epsilon>}$ variant, illustrated respectively to number theory. The situation in which the data needs to be represented in terms of respective probability densities is treated next, yielding the $M^{<\sigma>}$ meta model, which is then illustrated respectively to a real-world dataset (iris flowers data). Several considerations about how the developed framework can provide insights about data clustering, complexity, collaborative research, deep learning, and creativity are then presented, followed by overall conclusions.
翻译：在目前的工作中,我们描述一个模型建模框架,如何建立模型,将各种领域的概念和方法融合起来; 真实世界与任何单个信息处理代理所收集和审议的任何单个信息处理代理所可收集的模型之间的信息分流特点和讨论,随后提出一系列采用的必要要求,同时发展模型方法; 从数据集到模型的绘图问题随后得到解决,以及一些分别隐含的困难和限制; 基于这些考虑,逐渐发展一种元值模型模型,模型是如何构建模型的。 首先,提出了参考M*元模型框架,它从严格等同关系的角度,关键地将整个数据集和各个模型联系起来。 这个模型的有趣特征之一是,它能够弥合数据和建模之间的差距,同时为数据和模型的代数的比值铺平铺平了道路,可以将模型和模型结合起来,然后从固定基数的模型中显示M*模型,然后报告的模型方法继续讨论如何取样问题、错误和忽略的元值模型,然后分别用数字模型来解释,然后将数据推导出一个模型,然后将数据推导出一个模型,然后将数据推导出一个模型,然后将数据推导出一个模型,然后用美元模型, 的模型,然后将数据推导出一个模型,,然后将数据推导出一个模型,,然后将数据推导出一个数据推导出一个模型,然后用一个模型,然后用一个模型,,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型,用一个模型处理,用一个模型处理,用一个模型,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,用,
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