Learning systems are characterized by distinctive properties of complementarities between variables and their relations in reference to the epistemology of unity of knowledge. Such learning systems are in contrast to the methods of optimization and steady‐state equilibrium as opposed to simulation methods. The distinctive analytical properties of learning systems relate to evolutionary general equilibrium states. Here disequilibrium systems are also considered. In all the cases, the essential goal of explaining and using the properties of evolutionary general equilibrium systems with pervasive complementarities within and across systems are shown to establish these distinctive conceptual and applied implications of analysis.
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