This article discusses the challenges and complexities of economic networks, emphasizing the need for a new understanding of their structure and dynamics. Economic systems are increasingly built on interdependencies, such as transnational credit, investment networks, trade relations, and supply chains, which are difficult to predict and control. The article highlights the importance of systemic complexity in economic networks and the need for approaches that can revise and extend established economic theories. This will help in designing policies that reduce conflicts between individual interests and global efficiency, as well as reduce the risk of global failure by making economic networks more robust. The economy, like any complex system, involves a dynamic interaction of many agents, not just a few key players. The resulting systemic behavior, observable at the aggregate level, often has consequences that are hard to predict, as illustrated by the current economic crisis, which cannot be simply explained by the failure of a few major agents. Therefore, a more fundamental insight into the system's dynamics and how they can be traced back to the structural properties of the underlying interaction network is needed. Research on economic networks has been studied from two perspectives: one from economics and sociology, and the other from research on complex systems in physics and computer science. In both, nodes represent individual agents, such as firms, banks, or countries, and links between nodes represent their interactions, such as trade, ownership, R&D alliances, or credit-debt relationships. Different agents may have different behaviors under the same conditions and have strategic interactions. These evolving interactions can be represented by network dynamics that are bound in space and time and can change with the environment and coevolve with the agents. The socioeconomic perspective emphasizes understanding how the strategic behavior of interacting agents is influenced by and reciprocally shapes relatively simple network architectures. One common example is the star-spoke network, like a very centralized organization, in which a central "hub" channels all communication among agents. In this "micro" perspective, we focus on the individual system elements and their detailed network of relations. In contrast, for large setups, one adopts a "macro" perspective that focuses on the statistical regularities of the network as a whole. Each approach has its advantages and disadvantages. Previous work on the micro perspective was strongly rooted in oversimplifying assumptions on both the structure of the network and on agents' behaviors. For example, the micro approach may have emphasized agent incentives in the development of informal links within firms and may have failed to successfully predict realistic dynamic outcomes. The macro approach better accounts for the large-scale system properties, but fails in linking these to the economic motivation of individual agents. In recent micro approaches, economic networks were often viewed as the result of a network-formation game among competing and cooperating agents. In this regard, agents include firms that collaborate in joint R&D projects or workers who share information on job opportunities; their links are added or deleted as the consequence of purposeful decisions attempting to maximize their payoffs. In this context, agents must rely on andThis article discusses the challenges and complexities of economic networks, emphasizing the need for a new understanding of their structure and dynamics. Economic systems are increasingly built on interdependencies, such as transnational credit, investment networks, trade relations, and supply chains, which are difficult to predict and control. The article highlights the importance of systemic complexity in economic networks and the need for approaches that can revise and extend established economic theories. This will help in designing policies that reduce conflicts between individual interests and global efficiency, as well as reduce the risk of global failure by making economic networks more robust. The economy, like any complex system, involves a dynamic interaction of many agents, not just a few key players. The resulting systemic behavior, observable at the aggregate level, often has consequences that are hard to predict, as illustrated by the current economic crisis, which cannot be simply explained by the failure of a few major agents. Therefore, a more fundamental insight into the system's dynamics and how they can be traced back to the structural properties of the underlying interaction network is needed. Research on economic networks has been studied from two perspectives: one from economics and sociology, and the other from research on complex systems in physics and computer science. In both, nodes represent individual agents, such as firms, banks, or countries, and links between nodes represent their interactions, such as trade, ownership, R&D alliances, or credit-debt relationships. Different agents may have different behaviors under the same conditions and have strategic interactions. These evolving interactions can be represented by network dynamics that are bound in space and time and can change with the environment and coevolve with the agents. The socioeconomic perspective emphasizes understanding how the strategic behavior of interacting agents is influenced by and reciprocally shapes relatively simple network architectures. One common example is the star-spoke network, like a very centralized organization, in which a central "hub" channels all communication among agents. In this "micro" perspective, we focus on the individual system elements and their detailed network of relations. In contrast, for large setups, one adopts a "macro" perspective that focuses on the statistical regularities of the network as a whole. Each approach has its advantages and disadvantages. Previous work on the micro perspective was strongly rooted in oversimplifying assumptions on both the structure of the network and on agents' behaviors. For example, the micro approach may have emphasized agent incentives in the development of informal links within firms and may have failed to successfully predict realistic dynamic outcomes. The macro approach better accounts for the large-scale system properties, but fails in linking these to the economic motivation of individual agents. In recent micro approaches, economic networks were often viewed as the result of a network-formation game among competing and cooperating agents. In this regard, agents include firms that collaborate in joint R&D projects or workers who share information on job opportunities; their links are added or deleted as the consequence of purposeful decisions attempting to maximize their payoffs. In this context, agents must rely on and