Economic Systems as Networks

Abstract

This dissertation develops a circuit-theoretic methodology for modeling economic systems grounded in first principles. Like electrical circuits, economic systems can be understood as networks consisting of many interacting parts whose collective behavior emerges from their interactions. Circuit theory translates physical first principles into standard components, topological rules, and graphical representations for modeling complex dynamical systems. This dissertation applies these principles to economics by developing a methodology in which economic agents are represented by electrical components and economic systems are constructed as networks of these components.

The theoretical foundation of the methodology is an economic-engineering analogy, which describes economic dynamics in terms of mechanical behavior. Since mechanical and electrical systems can be represented through equivalent analogies, this dissertation adopts an equivalent electrical representation of the economic engineering theory. This allows complex networks of interacting economic agents to be modeled and analyzed using the principles of circuit theory and to be represented by analogous circuit diagrams. The advantage of this electrical representation is that circuit theory provides scalable tools for constructing, simulating, and analyzing interconnected dynamical systems. These tools are leveraged in this dissertation to develop the proposed modeling methodology.

The methodology is developed in two main steps. The first step introduces an economic circuit theory. Economic agents are modeled using generalized circuit elements that satisfy constitutive economic relations analogous to the laws governing resistors, inductors, and capacitors. Economic interactions are represented as network connections through which goods, analogous to currents, and incentives, analogous to voltages, are exchanged. Circuit diagrams provide both graphical and computational representations of economic systems, allowing standard circuit simulation tools such as LTspice to be used directly to simulate economic dynamics.

The second step focuses on scalability. While economic circuit theory provides a systematic way to describe interactions, large and highly interconnected economic systems quickly become difficult to manage when modeled only with elementary circuit elements. To address this, the dissertation extends economic circuit theory into a multiport network methodology. This allows complex economic systems to be constructed from modular subsystems with well-defined interfaces, while preserving interpretability and scalability.

The dissertation demonstrates the applicability of the methodology through several examples, ranging from textbook models to contemporary practical problems. A Robinson–Crusoe economy illustrates how classical microeconomic reasoning can be represented and simulated using circuit diagrams. A supply chain model shows how frequency-domain analysis, central to engineering practice, reveals resonance and oscillatory effects in inventory behavior. An electricity market with storage demonstrates how structural changes in a market alter system dynamics. Finally, a modular macroeconomic model illustrates how the methodology can scale to systems with many interacting sectors, while preserving interpretability and allowing shocks to be traced through the network.

Beyond the dissertation, the methodology has already been applied in a range of MSc theses across domains such as energy markets, industrial competition, financial planning, and macroeconomic modeling. These applications highlight the accessibility of the methodology, particularly for students with an engineering background, and its potential to become a practical tool for research, teaching, and policy analysis.

The methodology introduces several elements from engineering modeling practice into economics: explicit dynamics, modular construction, graphical representation, and scalable system analysis. Because the models can be drawn as circuit-style diagrams and executed in standard simulation environments such as LTspice, the methodology makes it possible to explore economic dynamics computationally using established engineering tools. Taken together, these elements provide a systematic way to examine how economic behavior emerges from agent interactions and how system-wide dynamics are shaped by the underlying network structure.