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Supply chains, or supply networks (SNs), exist in a multitude of different topologies, yet little is known concerning how such topologies grow, evolve, and adapt over time. To study this complex phenomenon, we begin by identifying some primary topological structures that SNs may form. Then, to investigate the evolution of such structures, a theory-based framework is developed that combines aspects of complex adaptive systems theory, industrial growth theory, network theory, market structure, and game theory. This framework specifies categories of rules that may evoke different behaviors in the two fundamental components of any adaptive SN, i.e., the environment and the Arms in that environment. The framework is implemented as a multiparadigm simulation utilizing software agents and it joins discrete-time with discrete-event simulation formalisms. This methodology allows the spontaneous generation of network structures so that it is possible to examine the potential factors behind the evolution of different SN topologies. Using data and parameters extracted from 80 years of the U.S. automobile industry, we have been able to "grow" a wide range of SN topologies and preliminary results show that certain environmental and firm-level factors may impact the eventual evolution of such structures.