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M.B. Mendel
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1
Modeling and Management of Short-Term Electricity Markets using Systems and Control
An Economic Circuit Theory Application
Decarbonization is structurally changing the electricity generation mix and shifting market operation toward real time. As renewable penetration increases, short-term markets are characterized by higher price volatility and continuous position adjustment.
Two classes of models are commonly used to analyze these markets: fundamental and statistical electricity price forecasting (EPF) models. Both face limitations in representing modern market dynamics. Fundamental models are developed for stable, dispatchable systems and are unable to capture highly dynamic market behavior, while statistical models rely on historical data and lose validity under structural change.
To address these limitations, this thesis develops a dynamical systems model using economic engineering. The day-ahead, intraday, and balancing stages are consolidated into a single formulation, enabling the representation of real-time market dynamics. The model remains
valid under structural change by restricting exogenous inputs to renewable generation forecasts and demand profiles. Price volatility and trading behavior emerge endogenously from the system dynamics.
The dynamical formulation enables real-time market management using control theory. The transmission system operator (TSO) is modeled as an incentive-based feedback controller that steers trading behavior and promotes proactive imbalance resolution. Similarly, generator-level control mitigates the impact of forecast errors.
The resulting closed-loop system is constructed using economic circuit theory. The controllers are shown to reduce reliance on balancing reserves and improve system stability under high renewable penetration and supply shocks. Using dynamic scenario analysis, this thesis further evaluates how system flexibility and sector heterogeneity affect prices and market liquidity
across market stages. ...
Two classes of models are commonly used to analyze these markets: fundamental and statistical electricity price forecasting (EPF) models. Both face limitations in representing modern market dynamics. Fundamental models are developed for stable, dispatchable systems and are unable to capture highly dynamic market behavior, while statistical models rely on historical data and lose validity under structural change.
To address these limitations, this thesis develops a dynamical systems model using economic engineering. The day-ahead, intraday, and balancing stages are consolidated into a single formulation, enabling the representation of real-time market dynamics. The model remains
valid under structural change by restricting exogenous inputs to renewable generation forecasts and demand profiles. Price volatility and trading behavior emerge endogenously from the system dynamics.
The dynamical formulation enables real-time market management using control theory. The transmission system operator (TSO) is modeled as an incentive-based feedback controller that steers trading behavior and promotes proactive imbalance resolution. Similarly, generator-level control mitigates the impact of forecast errors.
The resulting closed-loop system is constructed using economic circuit theory. The controllers are shown to reduce reliance on balancing reserves and improve system stability under high renewable penetration and supply shocks. Using dynamic scenario analysis, this thesis further evaluates how system flexibility and sector heterogeneity affect prices and market liquidity
across market stages. ...
Decarbonization is structurally changing the electricity generation mix and shifting market operation toward real time. As renewable penetration increases, short-term markets are characterized by higher price volatility and continuous position adjustment.
Two classes of models are commonly used to analyze these markets: fundamental and statistical electricity price forecasting (EPF) models. Both face limitations in representing modern market dynamics. Fundamental models are developed for stable, dispatchable systems and are unable to capture highly dynamic market behavior, while statistical models rely on historical data and lose validity under structural change.
To address these limitations, this thesis develops a dynamical systems model using economic engineering. The day-ahead, intraday, and balancing stages are consolidated into a single formulation, enabling the representation of real-time market dynamics. The model remains
valid under structural change by restricting exogenous inputs to renewable generation forecasts and demand profiles. Price volatility and trading behavior emerge endogenously from the system dynamics.
The dynamical formulation enables real-time market management using control theory. The transmission system operator (TSO) is modeled as an incentive-based feedback controller that steers trading behavior and promotes proactive imbalance resolution. Similarly, generator-level control mitigates the impact of forecast errors.
The resulting closed-loop system is constructed using economic circuit theory. The controllers are shown to reduce reliance on balancing reserves and improve system stability under high renewable penetration and supply shocks. Using dynamic scenario analysis, this thesis further evaluates how system flexibility and sector heterogeneity affect prices and market liquidity
across market stages.
Two classes of models are commonly used to analyze these markets: fundamental and statistical electricity price forecasting (EPF) models. Both face limitations in representing modern market dynamics. Fundamental models are developed for stable, dispatchable systems and are unable to capture highly dynamic market behavior, while statistical models rely on historical data and lose validity under structural change.
To address these limitations, this thesis develops a dynamical systems model using economic engineering. The day-ahead, intraday, and balancing stages are consolidated into a single formulation, enabling the representation of real-time market dynamics. The model remains
valid under structural change by restricting exogenous inputs to renewable generation forecasts and demand profiles. Price volatility and trading behavior emerge endogenously from the system dynamics.
The dynamical formulation enables real-time market management using control theory. The transmission system operator (TSO) is modeled as an incentive-based feedback controller that steers trading behavior and promotes proactive imbalance resolution. Similarly, generator-level control mitigates the impact of forecast errors.
The resulting closed-loop system is constructed using economic circuit theory. The controllers are shown to reduce reliance on balancing reserves and improve system stability under high renewable penetration and supply shocks. Using dynamic scenario analysis, this thesis further evaluates how system flexibility and sector heterogeneity affect prices and market liquidity
across market stages.
The Mechanics of Asset Pricing
An Economic Multiport Model for Forecasting Returns and their Volatility
Asset pricing models determine the market prices of securities under uncertainty. Existing approaches use statistical models to describe time variation in trend, volatility, and returns. In these models, price formation is typically inferred from correlations by estimating or calibrating model parameters on historical data.
In this thesis, we formulate an incentive-driven asset pricing model using mechanical analogues; specifically, a model wherein mechanical forces act as analogues to economic incentives. The key insight is that uncertainty in market prices can be characterized by two incentives.
The first incentive is an analogue to the Langevin force that drives an agent to bear volatility: the volatility incentive. The second incentive is an analogue to temperature that drives agents to invest: the investment incentive. These incentives drive separate flows of investment and money, which together determine a fully incentive-driven market price.
To show how market prices and household returns are driven by incentives, we build a multiport model of the economy with households, firms, and a financial market. We analyze how households allocate wage income among investment, saving, and consumption by balancing the incentives for each. We further show how the model provides market price projections, which we use to analyze household returns. Finally, we show how external shocks induce volatility regime switching and use this to provide a causal explanation for price crashes. ...
In this thesis, we formulate an incentive-driven asset pricing model using mechanical analogues; specifically, a model wherein mechanical forces act as analogues to economic incentives. The key insight is that uncertainty in market prices can be characterized by two incentives.
The first incentive is an analogue to the Langevin force that drives an agent to bear volatility: the volatility incentive. The second incentive is an analogue to temperature that drives agents to invest: the investment incentive. These incentives drive separate flows of investment and money, which together determine a fully incentive-driven market price.
To show how market prices and household returns are driven by incentives, we build a multiport model of the economy with households, firms, and a financial market. We analyze how households allocate wage income among investment, saving, and consumption by balancing the incentives for each. We further show how the model provides market price projections, which we use to analyze household returns. Finally, we show how external shocks induce volatility regime switching and use this to provide a causal explanation for price crashes. ...
Asset pricing models determine the market prices of securities under uncertainty. Existing approaches use statistical models to describe time variation in trend, volatility, and returns. In these models, price formation is typically inferred from correlations by estimating or calibrating model parameters on historical data.
In this thesis, we formulate an incentive-driven asset pricing model using mechanical analogues; specifically, a model wherein mechanical forces act as analogues to economic incentives. The key insight is that uncertainty in market prices can be characterized by two incentives.
The first incentive is an analogue to the Langevin force that drives an agent to bear volatility: the volatility incentive. The second incentive is an analogue to temperature that drives agents to invest: the investment incentive. These incentives drive separate flows of investment and money, which together determine a fully incentive-driven market price.
To show how market prices and household returns are driven by incentives, we build a multiport model of the economy with households, firms, and a financial market. We analyze how households allocate wage income among investment, saving, and consumption by balancing the incentives for each. We further show how the model provides market price projections, which we use to analyze household returns. Finally, we show how external shocks induce volatility regime switching and use this to provide a causal explanation for price crashes.
In this thesis, we formulate an incentive-driven asset pricing model using mechanical analogues; specifically, a model wherein mechanical forces act as analogues to economic incentives. The key insight is that uncertainty in market prices can be characterized by two incentives.
The first incentive is an analogue to the Langevin force that drives an agent to bear volatility: the volatility incentive. The second incentive is an analogue to temperature that drives agents to invest: the investment incentive. These incentives drive separate flows of investment and money, which together determine a fully incentive-driven market price.
To show how market prices and household returns are driven by incentives, we build a multiport model of the economy with households, firms, and a financial market. We analyze how households allocate wage income among investment, saving, and consumption by balancing the incentives for each. We further show how the model provides market price projections, which we use to analyze household returns. Finally, we show how external shocks induce volatility regime switching and use this to provide a causal explanation for price crashes.
Economic Systems as Networks
A Circuit-Theoretic Methodology
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.
...
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.
...
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.
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.
Economic Engineering for Proactive Asset and Liability Management
Managing Liquidity and Solvency Risk with Process Control
Although banks currently manage their assets and liabilities reactively, there has been a push towards a proactive approach. For the latter, they need models to provide accurate cash flow projections and tools to design and test management policies, which they lack.
This thesis puts forward an economic engineering solution. It exploits dynamic systems modeling to provide cash flow projections that are accurate and process control to design and test liquidity and solvency risk management policies in real-time. To ensure that it models the dynamics that govern the bank's cash flows realistically, we integrate several macroeconomic theories into its design.
We demonstrate the potential of the approach through example applications provided by ALM experts at Rabobank. These include scenarios for which they have to rely on their intuition to assess their impacts. Our results show that a competitor bank that disrupts the banking sector with a low interest rate spread becomes unsustainable under solvency constraints. A tariff shock leads to a short-term rise in interest rates, and in hindsight, the model correctly predicts a liquidity surge and a decrease in interest rates during the COVID-19 pandemic. ...
This thesis puts forward an economic engineering solution. It exploits dynamic systems modeling to provide cash flow projections that are accurate and process control to design and test liquidity and solvency risk management policies in real-time. To ensure that it models the dynamics that govern the bank's cash flows realistically, we integrate several macroeconomic theories into its design.
We demonstrate the potential of the approach through example applications provided by ALM experts at Rabobank. These include scenarios for which they have to rely on their intuition to assess their impacts. Our results show that a competitor bank that disrupts the banking sector with a low interest rate spread becomes unsustainable under solvency constraints. A tariff shock leads to a short-term rise in interest rates, and in hindsight, the model correctly predicts a liquidity surge and a decrease in interest rates during the COVID-19 pandemic. ...
Although banks currently manage their assets and liabilities reactively, there has been a push towards a proactive approach. For the latter, they need models to provide accurate cash flow projections and tools to design and test management policies, which they lack.
This thesis puts forward an economic engineering solution. It exploits dynamic systems modeling to provide cash flow projections that are accurate and process control to design and test liquidity and solvency risk management policies in real-time. To ensure that it models the dynamics that govern the bank's cash flows realistically, we integrate several macroeconomic theories into its design.
We demonstrate the potential of the approach through example applications provided by ALM experts at Rabobank. These include scenarios for which they have to rely on their intuition to assess their impacts. Our results show that a competitor bank that disrupts the banking sector with a low interest rate spread becomes unsustainable under solvency constraints. A tariff shock leads to a short-term rise in interest rates, and in hindsight, the model correctly predicts a liquidity surge and a decrease in interest rates during the COVID-19 pandemic.
This thesis puts forward an economic engineering solution. It exploits dynamic systems modeling to provide cash flow projections that are accurate and process control to design and test liquidity and solvency risk management policies in real-time. To ensure that it models the dynamics that govern the bank's cash flows realistically, we integrate several macroeconomic theories into its design.
We demonstrate the potential of the approach through example applications provided by ALM experts at Rabobank. These include scenarios for which they have to rely on their intuition to assess their impacts. Our results show that a competitor bank that disrupts the banking sector with a low interest rate spread becomes unsustainable under solvency constraints. A tariff shock leads to a short-term rise in interest rates, and in hindsight, the model correctly predicts a liquidity surge and a decrease in interest rates during the COVID-19 pandemic.
Modeling, Design, Analysis, and Control of a Spot Market for Hydrogen
An Application of Economic Circuit Theory
In this thesis, we demonstrate the potential of economic circuit theory for the modeling, design, analysis, and control of a spot market for hydrogen. Spot prices are expected to be significantly more volatile than the current marginal-cost-plus pricing schemes and new methods are required to model the dynamics of price as it reacts to fluctuations in supply and demand.
To model such dynamic pricing, we use recently developed economic circuit theory, a subfield of economic engineering where markets are modeled as electrical circuits. We model market agents, including production operators, storage facilities, market makers, and industry clusters, as analogous electrical components. We structure their interactions in an economic network that reflects the flow of hydrogen through the backbone and the balancing of price-driving incentives among the market agents.
We design three economic networks: a core network and two extensions. The core network incorporates a renewable wind-hydrogen supply chain, a low-carbon hydrogen supply chain, and imports of gaseous and liquid hydrogen. One extension divides the hydrogen spot market into renewable and low-carbon segments, and the other integrates an ammonia market. We implement and simulate the networks in MATLAB Simulink.
We analyze the networks' dynamics using dynamic scenario analyses, a technique within economic circuit theory. This permits us to quantify the dynamic resilience of the market under supply disruptions and demand extensions; in particular, we use overshoot and settling time as metrics for the volatility and recovery of stocks, prices, and hydrogen supply. In addition, we establish how wind variability generates a ripple effect that impacts the dynamics across the coupled commodity markets.
In conclusion, we model and analyze a market design that includes intervention by a regulatory agency. Specifically, we design a PID controller to implement a subsidized price-settlement system. We demonstrate that such a controller effectively mitigates excessive spot price levels, reduces price volatility, and secures the supply of hydrogen. ...
To model such dynamic pricing, we use recently developed economic circuit theory, a subfield of economic engineering where markets are modeled as electrical circuits. We model market agents, including production operators, storage facilities, market makers, and industry clusters, as analogous electrical components. We structure their interactions in an economic network that reflects the flow of hydrogen through the backbone and the balancing of price-driving incentives among the market agents.
We design three economic networks: a core network and two extensions. The core network incorporates a renewable wind-hydrogen supply chain, a low-carbon hydrogen supply chain, and imports of gaseous and liquid hydrogen. One extension divides the hydrogen spot market into renewable and low-carbon segments, and the other integrates an ammonia market. We implement and simulate the networks in MATLAB Simulink.
We analyze the networks' dynamics using dynamic scenario analyses, a technique within economic circuit theory. This permits us to quantify the dynamic resilience of the market under supply disruptions and demand extensions; in particular, we use overshoot and settling time as metrics for the volatility and recovery of stocks, prices, and hydrogen supply. In addition, we establish how wind variability generates a ripple effect that impacts the dynamics across the coupled commodity markets.
In conclusion, we model and analyze a market design that includes intervention by a regulatory agency. Specifically, we design a PID controller to implement a subsidized price-settlement system. We demonstrate that such a controller effectively mitigates excessive spot price levels, reduces price volatility, and secures the supply of hydrogen. ...
In this thesis, we demonstrate the potential of economic circuit theory for the modeling, design, analysis, and control of a spot market for hydrogen. Spot prices are expected to be significantly more volatile than the current marginal-cost-plus pricing schemes and new methods are required to model the dynamics of price as it reacts to fluctuations in supply and demand.
To model such dynamic pricing, we use recently developed economic circuit theory, a subfield of economic engineering where markets are modeled as electrical circuits. We model market agents, including production operators, storage facilities, market makers, and industry clusters, as analogous electrical components. We structure their interactions in an economic network that reflects the flow of hydrogen through the backbone and the balancing of price-driving incentives among the market agents.
We design three economic networks: a core network and two extensions. The core network incorporates a renewable wind-hydrogen supply chain, a low-carbon hydrogen supply chain, and imports of gaseous and liquid hydrogen. One extension divides the hydrogen spot market into renewable and low-carbon segments, and the other integrates an ammonia market. We implement and simulate the networks in MATLAB Simulink.
We analyze the networks' dynamics using dynamic scenario analyses, a technique within economic circuit theory. This permits us to quantify the dynamic resilience of the market under supply disruptions and demand extensions; in particular, we use overshoot and settling time as metrics for the volatility and recovery of stocks, prices, and hydrogen supply. In addition, we establish how wind variability generates a ripple effect that impacts the dynamics across the coupled commodity markets.
In conclusion, we model and analyze a market design that includes intervention by a regulatory agency. Specifically, we design a PID controller to implement a subsidized price-settlement system. We demonstrate that such a controller effectively mitigates excessive spot price levels, reduces price volatility, and secures the supply of hydrogen.
To model such dynamic pricing, we use recently developed economic circuit theory, a subfield of economic engineering where markets are modeled as electrical circuits. We model market agents, including production operators, storage facilities, market makers, and industry clusters, as analogous electrical components. We structure their interactions in an economic network that reflects the flow of hydrogen through the backbone and the balancing of price-driving incentives among the market agents.
We design three economic networks: a core network and two extensions. The core network incorporates a renewable wind-hydrogen supply chain, a low-carbon hydrogen supply chain, and imports of gaseous and liquid hydrogen. One extension divides the hydrogen spot market into renewable and low-carbon segments, and the other integrates an ammonia market. We implement and simulate the networks in MATLAB Simulink.
We analyze the networks' dynamics using dynamic scenario analyses, a technique within economic circuit theory. This permits us to quantify the dynamic resilience of the market under supply disruptions and demand extensions; in particular, we use overshoot and settling time as metrics for the volatility and recovery of stocks, prices, and hydrogen supply. In addition, we establish how wind variability generates a ripple effect that impacts the dynamics across the coupled commodity markets.
In conclusion, we model and analyze a market design that includes intervention by a regulatory agency. Specifically, we design a PID controller to implement a subsidized price-settlement system. We demonstrate that such a controller effectively mitigates excessive spot price levels, reduces price volatility, and secures the supply of hydrogen.
Design and Analysis of Macro-Economic Models in the Laplace Domain
An Economic-Engineering Approach
In this thesis, we demonstrate the efficiency of Laplace domain techniques for the design and analysis of economic systems. To make the techniques applicable to economic modeling, we establish the economic analogs to the various tools and nomenclature in the engineering literature. We show that the Laplace domain provides an alternative description of economic systems, offering insights into behavior not apparent in the time domain. This allows economic discounting and cycles to be efficiently analyzed using pole-zero maps, Bode plots, and similar techniques. In addition, we demonstrate that transforming the linear differential equations of economic engineering into algebraic equations in the Laplace domain simplifies the design of economic systems.
We use the Laplace domain techniques to design and analyze a macroeconomic model. By designing the model in the Laplace domain, we are able to integrate supply chain dynamics and the housing market using two-port network theory.
By analyzing the model using a pole-zero map, we show that the economy's discount rates and business cycles are represented by complex poles and the economy's transmission blocking rates by complex zeros. Additionally, we demonstrate that the Bullwhip effect, a supply chain phenomenon, can be intuitively visualized using a Bode plot. These applications illustrate how Laplace-domain techniques enable the efficient design and analysis of economic systems. ...
We use the Laplace domain techniques to design and analyze a macroeconomic model. By designing the model in the Laplace domain, we are able to integrate supply chain dynamics and the housing market using two-port network theory.
By analyzing the model using a pole-zero map, we show that the economy's discount rates and business cycles are represented by complex poles and the economy's transmission blocking rates by complex zeros. Additionally, we demonstrate that the Bullwhip effect, a supply chain phenomenon, can be intuitively visualized using a Bode plot. These applications illustrate how Laplace-domain techniques enable the efficient design and analysis of economic systems. ...
In this thesis, we demonstrate the efficiency of Laplace domain techniques for the design and analysis of economic systems. To make the techniques applicable to economic modeling, we establish the economic analogs to the various tools and nomenclature in the engineering literature. We show that the Laplace domain provides an alternative description of economic systems, offering insights into behavior not apparent in the time domain. This allows economic discounting and cycles to be efficiently analyzed using pole-zero maps, Bode plots, and similar techniques. In addition, we demonstrate that transforming the linear differential equations of economic engineering into algebraic equations in the Laplace domain simplifies the design of economic systems.
We use the Laplace domain techniques to design and analyze a macroeconomic model. By designing the model in the Laplace domain, we are able to integrate supply chain dynamics and the housing market using two-port network theory.
By analyzing the model using a pole-zero map, we show that the economy's discount rates and business cycles are represented by complex poles and the economy's transmission blocking rates by complex zeros. Additionally, we demonstrate that the Bullwhip effect, a supply chain phenomenon, can be intuitively visualized using a Bode plot. These applications illustrate how Laplace-domain techniques enable the efficient design and analysis of economic systems.
We use the Laplace domain techniques to design and analyze a macroeconomic model. By designing the model in the Laplace domain, we are able to integrate supply chain dynamics and the housing market using two-port network theory.
By analyzing the model using a pole-zero map, we show that the economy's discount rates and business cycles are represented by complex poles and the economy's transmission blocking rates by complex zeros. Additionally, we demonstrate that the Bullwhip effect, a supply chain phenomenon, can be intuitively visualized using a Bode plot. These applications illustrate how Laplace-domain techniques enable the efficient design and analysis of economic systems.
The relativistic dynamics of labor economics
An economic engineering treatment
In this thesis, the analogy between the special theory of relativity and the dynamics of a laborer is developed in the context of labor economics. At the basis of this analogy stands an individual laborer who cannot supply more than 24$hours of labor in a day. This represents the theoretical limit to the flow of labor services (velocity). We argue this limit is analogous to the speed of light. The development of the analogy continues using hyperbolic functions independent of the demand frame of reference (frame of reference) and dependent on the degree of demand (rapidity) as well as the wage inelasticity (mass). This analogy describes the behavior of an individual laborer, detailing the quantity of labor services (position) and their flow, the wage (momentum) and the causation of changes in the flow of labor services (forces). These dynamics in labor economics are consistent with the theory of special relativity, demonstrating economic engineering principles.
Economic engineering is applied to model an individual laborer using the newly developed analogy. The laborer's supply curve shows that wage inelasticity does not change when a laborer performs more labor. Instead, the nonlinear supply curve is attributed to the difference in a laborer's perception of time (proper time). The perception of time depends on the flow of labor services of the observer, making it possible to observe the labor market from different perspectives, including those of companies and laborers. The laborer's perspective on their supply is visualized on the Poincaré disk, from which occupational compositions and job transitions can be analyzed. ...
Economic engineering is applied to model an individual laborer using the newly developed analogy. The laborer's supply curve shows that wage inelasticity does not change when a laborer performs more labor. Instead, the nonlinear supply curve is attributed to the difference in a laborer's perception of time (proper time). The perception of time depends on the flow of labor services of the observer, making it possible to observe the labor market from different perspectives, including those of companies and laborers. The laborer's perspective on their supply is visualized on the Poincaré disk, from which occupational compositions and job transitions can be analyzed. ...
In this thesis, the analogy between the special theory of relativity and the dynamics of a laborer is developed in the context of labor economics. At the basis of this analogy stands an individual laborer who cannot supply more than 24$hours of labor in a day. This represents the theoretical limit to the flow of labor services (velocity). We argue this limit is analogous to the speed of light. The development of the analogy continues using hyperbolic functions independent of the demand frame of reference (frame of reference) and dependent on the degree of demand (rapidity) as well as the wage inelasticity (mass). This analogy describes the behavior of an individual laborer, detailing the quantity of labor services (position) and their flow, the wage (momentum) and the causation of changes in the flow of labor services (forces). These dynamics in labor economics are consistent with the theory of special relativity, demonstrating economic engineering principles.
Economic engineering is applied to model an individual laborer using the newly developed analogy. The laborer's supply curve shows that wage inelasticity does not change when a laborer performs more labor. Instead, the nonlinear supply curve is attributed to the difference in a laborer's perception of time (proper time). The perception of time depends on the flow of labor services of the observer, making it possible to observe the labor market from different perspectives, including those of companies and laborers. The laborer's perspective on their supply is visualized on the Poincaré disk, from which occupational compositions and job transitions can be analyzed.
Economic engineering is applied to model an individual laborer using the newly developed analogy. The laborer's supply curve shows that wage inelasticity does not change when a laborer performs more labor. Instead, the nonlinear supply curve is attributed to the difference in a laborer's perception of time (proper time). The perception of time depends on the flow of labor services of the observer, making it possible to observe the labor market from different perspectives, including those of companies and laborers. The laborer's perspective on their supply is visualized on the Poincaré disk, from which occupational compositions and job transitions can be analyzed.
The Geometry of Dissipative Mechanical Systems
Using Jacobi Manifolds and the Split-Quaternion Algebra
Conservative mechanical systems admit a symplectic structure.
However, since real systems typically exhibit energy dissipation, this symplectic structure is often too restrictive for engineering purposes.
Also in economic systems, dissipative phenomena are ubiquitous in the form of consumption and depreciation.
In this thesis, we develop an extension of the symplectic structure that does incorporate dissipation in an intrinsic manner.
This geometric structure is presented in a way that makes it usable for engineering applications, which is done in two steps.
We first construct a contact Hamiltonian system for the damped harmonic oscillator by combining the symplectic structure of conservative mechanics and the contact-geometric description of thermodynamics.
This system is then modified for the harmonic oscillator with both a parallel and serial damper.
We show how the widely adopted Caldirola-Kanai Hamiltonian for the damped harmonic oscillator emerges from the symplectification of the contact Hamiltonian system.
In order to deal with general, multi-degree of freedom systems, the contact structure is then extended to a Jacobi structure.
In contrast to the contact structure, the Jacobi structure encodes the pairing of conjugate variables and the dissipation as two separate entities. We argue that this makes it possible to construct a Hamiltonian system for any mechanical system and illustrate the practicality of this formalism by applying it to a multi-degree of freedom system.
Second, we propose split-quaternions as an alternative to the traditional matrix representation of two-dimensional linear mechanical systems.
We demonstrate how the properties of the dynamical system are directly reflected in its split-quaternion representation.
As a result, the split-quaternion representation offers several advantages for practical applications, e.g., for the classification of fixed points or when computing the system solution.
We use models of the hyperbolic plane to find a relation between the solution geometry of underdamped systems and their split-quaternion representation.
...
However, since real systems typically exhibit energy dissipation, this symplectic structure is often too restrictive for engineering purposes.
Also in economic systems, dissipative phenomena are ubiquitous in the form of consumption and depreciation.
In this thesis, we develop an extension of the symplectic structure that does incorporate dissipation in an intrinsic manner.
This geometric structure is presented in a way that makes it usable for engineering applications, which is done in two steps.
We first construct a contact Hamiltonian system for the damped harmonic oscillator by combining the symplectic structure of conservative mechanics and the contact-geometric description of thermodynamics.
This system is then modified for the harmonic oscillator with both a parallel and serial damper.
We show how the widely adopted Caldirola-Kanai Hamiltonian for the damped harmonic oscillator emerges from the symplectification of the contact Hamiltonian system.
In order to deal with general, multi-degree of freedom systems, the contact structure is then extended to a Jacobi structure.
In contrast to the contact structure, the Jacobi structure encodes the pairing of conjugate variables and the dissipation as two separate entities. We argue that this makes it possible to construct a Hamiltonian system for any mechanical system and illustrate the practicality of this formalism by applying it to a multi-degree of freedom system.
Second, we propose split-quaternions as an alternative to the traditional matrix representation of two-dimensional linear mechanical systems.
We demonstrate how the properties of the dynamical system are directly reflected in its split-quaternion representation.
As a result, the split-quaternion representation offers several advantages for practical applications, e.g., for the classification of fixed points or when computing the system solution.
We use models of the hyperbolic plane to find a relation between the solution geometry of underdamped systems and their split-quaternion representation.
...
Conservative mechanical systems admit a symplectic structure.
However, since real systems typically exhibit energy dissipation, this symplectic structure is often too restrictive for engineering purposes.
Also in economic systems, dissipative phenomena are ubiquitous in the form of consumption and depreciation.
In this thesis, we develop an extension of the symplectic structure that does incorporate dissipation in an intrinsic manner.
This geometric structure is presented in a way that makes it usable for engineering applications, which is done in two steps.
We first construct a contact Hamiltonian system for the damped harmonic oscillator by combining the symplectic structure of conservative mechanics and the contact-geometric description of thermodynamics.
This system is then modified for the harmonic oscillator with both a parallel and serial damper.
We show how the widely adopted Caldirola-Kanai Hamiltonian for the damped harmonic oscillator emerges from the symplectification of the contact Hamiltonian system.
In order to deal with general, multi-degree of freedom systems, the contact structure is then extended to a Jacobi structure.
In contrast to the contact structure, the Jacobi structure encodes the pairing of conjugate variables and the dissipation as two separate entities. We argue that this makes it possible to construct a Hamiltonian system for any mechanical system and illustrate the practicality of this formalism by applying it to a multi-degree of freedom system.
Second, we propose split-quaternions as an alternative to the traditional matrix representation of two-dimensional linear mechanical systems.
We demonstrate how the properties of the dynamical system are directly reflected in its split-quaternion representation.
As a result, the split-quaternion representation offers several advantages for practical applications, e.g., for the classification of fixed points or when computing the system solution.
We use models of the hyperbolic plane to find a relation between the solution geometry of underdamped systems and their split-quaternion representation.
However, since real systems typically exhibit energy dissipation, this symplectic structure is often too restrictive for engineering purposes.
Also in economic systems, dissipative phenomena are ubiquitous in the form of consumption and depreciation.
In this thesis, we develop an extension of the symplectic structure that does incorporate dissipation in an intrinsic manner.
This geometric structure is presented in a way that makes it usable for engineering applications, which is done in two steps.
We first construct a contact Hamiltonian system for the damped harmonic oscillator by combining the symplectic structure of conservative mechanics and the contact-geometric description of thermodynamics.
This system is then modified for the harmonic oscillator with both a parallel and serial damper.
We show how the widely adopted Caldirola-Kanai Hamiltonian for the damped harmonic oscillator emerges from the symplectification of the contact Hamiltonian system.
In order to deal with general, multi-degree of freedom systems, the contact structure is then extended to a Jacobi structure.
In contrast to the contact structure, the Jacobi structure encodes the pairing of conjugate variables and the dissipation as two separate entities. We argue that this makes it possible to construct a Hamiltonian system for any mechanical system and illustrate the practicality of this formalism by applying it to a multi-degree of freedom system.
Second, we propose split-quaternions as an alternative to the traditional matrix representation of two-dimensional linear mechanical systems.
We demonstrate how the properties of the dynamical system are directly reflected in its split-quaternion representation.
As a result, the split-quaternion representation offers several advantages for practical applications, e.g., for the classification of fixed points or when computing the system solution.
We use models of the hyperbolic plane to find a relation between the solution geometry of underdamped systems and their split-quaternion representation.
Economic Engineering & Statistical Physics
Linking Microeconomics and Macroeconomics using Statistical Physics
Economic engineering models individual agents as inertia elements and can be viewed as a microeconomic theory based on analogs with classical mechanics. In this thesis the economic engineering concept of modeling individual agents is used to model economic systems consisting of many agents, e.g. an entire country. This is done using classical statistical physics. In statistical physics the microscopic movement of individual gas particles as described by classical mechanics and the macroscopic properties of gases as described by thermodynamics are linked. Using this insight, microeconomics and macroeconomics are linked within the economic engineering framework.
The critical contribution of this thesis is finding the analog of Gibbs' interpretation of entropy, calling it the amount of diversification. This is done as follows. A thermodynamic system in equilibrium is seen as the analog of a Pareto optimal economy or macroeconomic equilibrium. The 2nd law of thermodynamics guarantees the existence of thermodynamic equilibrium and it follows that the entropy is maximized in equilibrium. Clausius interpreted the entropy as an "arrow of time" that pushes the system towards equilibrium. In this thesis Adam Smith's invisible hand is then viewed as an economic analog of Clausius' entropy. Gibbs gives a statistical interpretation to entropy. By calling the amount of diversification the analog of the Gibbs entropy, it follows that a Pareto optimal economy is fully diversified. The amount of diversification contains both the distribution of economic rent over agents and the portfolio diversification of agents for different goods.
Based on the diversification analog, the thesis develops several further analogs. The analog of the partition function is called the opportunity function and gives the opportunities for extracting profits from an economic system by trading. From this the economic engineering analog of the free energy follows. The temperature and chemical potential are given economic engineering analogs as well, namely the level of welfare and the disposable income per capita respectively. The thesis is finalized with applications of the theory developed. ...
The critical contribution of this thesis is finding the analog of Gibbs' interpretation of entropy, calling it the amount of diversification. This is done as follows. A thermodynamic system in equilibrium is seen as the analog of a Pareto optimal economy or macroeconomic equilibrium. The 2nd law of thermodynamics guarantees the existence of thermodynamic equilibrium and it follows that the entropy is maximized in equilibrium. Clausius interpreted the entropy as an "arrow of time" that pushes the system towards equilibrium. In this thesis Adam Smith's invisible hand is then viewed as an economic analog of Clausius' entropy. Gibbs gives a statistical interpretation to entropy. By calling the amount of diversification the analog of the Gibbs entropy, it follows that a Pareto optimal economy is fully diversified. The amount of diversification contains both the distribution of economic rent over agents and the portfolio diversification of agents for different goods.
Based on the diversification analog, the thesis develops several further analogs. The analog of the partition function is called the opportunity function and gives the opportunities for extracting profits from an economic system by trading. From this the economic engineering analog of the free energy follows. The temperature and chemical potential are given economic engineering analogs as well, namely the level of welfare and the disposable income per capita respectively. The thesis is finalized with applications of the theory developed. ...
Economic engineering models individual agents as inertia elements and can be viewed as a microeconomic theory based on analogs with classical mechanics. In this thesis the economic engineering concept of modeling individual agents is used to model economic systems consisting of many agents, e.g. an entire country. This is done using classical statistical physics. In statistical physics the microscopic movement of individual gas particles as described by classical mechanics and the macroscopic properties of gases as described by thermodynamics are linked. Using this insight, microeconomics and macroeconomics are linked within the economic engineering framework.
The critical contribution of this thesis is finding the analog of Gibbs' interpretation of entropy, calling it the amount of diversification. This is done as follows. A thermodynamic system in equilibrium is seen as the analog of a Pareto optimal economy or macroeconomic equilibrium. The 2nd law of thermodynamics guarantees the existence of thermodynamic equilibrium and it follows that the entropy is maximized in equilibrium. Clausius interpreted the entropy as an "arrow of time" that pushes the system towards equilibrium. In this thesis Adam Smith's invisible hand is then viewed as an economic analog of Clausius' entropy. Gibbs gives a statistical interpretation to entropy. By calling the amount of diversification the analog of the Gibbs entropy, it follows that a Pareto optimal economy is fully diversified. The amount of diversification contains both the distribution of economic rent over agents and the portfolio diversification of agents for different goods.
Based on the diversification analog, the thesis develops several further analogs. The analog of the partition function is called the opportunity function and gives the opportunities for extracting profits from an economic system by trading. From this the economic engineering analog of the free energy follows. The temperature and chemical potential are given economic engineering analogs as well, namely the level of welfare and the disposable income per capita respectively. The thesis is finalized with applications of the theory developed.
The critical contribution of this thesis is finding the analog of Gibbs' interpretation of entropy, calling it the amount of diversification. This is done as follows. A thermodynamic system in equilibrium is seen as the analog of a Pareto optimal economy or macroeconomic equilibrium. The 2nd law of thermodynamics guarantees the existence of thermodynamic equilibrium and it follows that the entropy is maximized in equilibrium. Clausius interpreted the entropy as an "arrow of time" that pushes the system towards equilibrium. In this thesis Adam Smith's invisible hand is then viewed as an economic analog of Clausius' entropy. Gibbs gives a statistical interpretation to entropy. By calling the amount of diversification the analog of the Gibbs entropy, it follows that a Pareto optimal economy is fully diversified. The amount of diversification contains both the distribution of economic rent over agents and the portfolio diversification of agents for different goods.
Based on the diversification analog, the thesis develops several further analogs. The analog of the partition function is called the opportunity function and gives the opportunities for extracting profits from an economic system by trading. From this the economic engineering analog of the free energy follows. The temperature and chemical potential are given economic engineering analogs as well, namely the level of welfare and the disposable income per capita respectively. The thesis is finalized with applications of the theory developed.
Modeling the Price Dynamics of Competition using Economic Engineering
A solution for regulators and hedge funds
Although competition is a dynamic phenomenon, currently used competition models do not take price dynamics into account, and some models are not even quantitative. This poses a problem for regulators or hedge funds and M&A departments who rely on these models to either quantify price and demand movements, or to determine the cost of competition.
This thesis solves that problem by using Economic Engineering to build on the existing game-theoretic models of competition to include price dynamics. A bond-graph model of a competitive market is developed, from which the price dynamics are derived. Model-predictive controllers are used to model profit-maximizing companies within this model, and to simulate competitive behavior and its effects on prices and demand flows.
Finally, this thesis shows how control engineering tools in both the time and the frequency domain can then be exploited by regulators and hedge funds. Time-domain simulations enable regulators to quantify the effects of competition on prices and demands, and analyses in the frequency domain enable hedge funds to determine the change in company valuations due to changes in competition, i.e., the cost of competition. ...
This thesis solves that problem by using Economic Engineering to build on the existing game-theoretic models of competition to include price dynamics. A bond-graph model of a competitive market is developed, from which the price dynamics are derived. Model-predictive controllers are used to model profit-maximizing companies within this model, and to simulate competitive behavior and its effects on prices and demand flows.
Finally, this thesis shows how control engineering tools in both the time and the frequency domain can then be exploited by regulators and hedge funds. Time-domain simulations enable regulators to quantify the effects of competition on prices and demands, and analyses in the frequency domain enable hedge funds to determine the change in company valuations due to changes in competition, i.e., the cost of competition. ...
Although competition is a dynamic phenomenon, currently used competition models do not take price dynamics into account, and some models are not even quantitative. This poses a problem for regulators or hedge funds and M&A departments who rely on these models to either quantify price and demand movements, or to determine the cost of competition.
This thesis solves that problem by using Economic Engineering to build on the existing game-theoretic models of competition to include price dynamics. A bond-graph model of a competitive market is developed, from which the price dynamics are derived. Model-predictive controllers are used to model profit-maximizing companies within this model, and to simulate competitive behavior and its effects on prices and demand flows.
Finally, this thesis shows how control engineering tools in both the time and the frequency domain can then be exploited by regulators and hedge funds. Time-domain simulations enable regulators to quantify the effects of competition on prices and demands, and analyses in the frequency domain enable hedge funds to determine the change in company valuations due to changes in competition, i.e., the cost of competition.
This thesis solves that problem by using Economic Engineering to build on the existing game-theoretic models of competition to include price dynamics. A bond-graph model of a competitive market is developed, from which the price dynamics are derived. Model-predictive controllers are used to model profit-maximizing companies within this model, and to simulate competitive behavior and its effects on prices and demand flows.
Finally, this thesis shows how control engineering tools in both the time and the frequency domain can then be exploited by regulators and hedge funds. Time-domain simulations enable regulators to quantify the effects of competition on prices and demands, and analyses in the frequency domain enable hedge funds to determine the change in company valuations due to changes in competition, i.e., the cost of competition.
Economic Engineering for Supply Chain Management
Mitigating the Bullwhip Effect Using PID Control
The purpose of Supply Chain Management (SCM) is to maximize and stabilize the flow of goods through a supply chain. Disruptions to this flow, such as the Bullwhip Effect (BWE), have become increasingly severe and dynamic, challenging current methods for counteracting these disruptions. This thesis develops a systems and control framework for designing SCM policies to meet this challenge.
The thesis makes use of economic-engineering modeling principles to model the dynamics of the supply chain and calculate the flow of goods. Specifically, the supply chain is considered to be analogous to an electrical circuit, with the flow of goods analogous to the current and price changes analogous to voltage drops. Based on these analogies, the thesis develops the building blocks required for modeling a supply chain, consisting of storage, production and external markets. The building blocks are used to construct a serial supply chain with multiple stages and products. The supply chain is analyzed in both the time and frequency domain, quantifying how disruptions influence the flow of goods.
To regulate the flow of goods through a supply chain, this thesis models the procurement, production and product pricing policies of SCM as individual PID controllers. The PID controllers are shown to regulate the flow of goods in a manner similar to how an actual manager would.
The effectiveness of the framework is demonstrated in a simulation study of the BWE in the supply chain of Valtris Specialty Chemicals (VSC). It is shown how tools in classical control theory, like Bode diagrams, are effective in the analysis of the dynamics and severity of the BWE in terms of the resonance frequency and the amplification, respectively. In addition, this thesis shows how the tuning of the PID controllers relates to specific adjustments in the policies of VSC managers. ...
The thesis makes use of economic-engineering modeling principles to model the dynamics of the supply chain and calculate the flow of goods. Specifically, the supply chain is considered to be analogous to an electrical circuit, with the flow of goods analogous to the current and price changes analogous to voltage drops. Based on these analogies, the thesis develops the building blocks required for modeling a supply chain, consisting of storage, production and external markets. The building blocks are used to construct a serial supply chain with multiple stages and products. The supply chain is analyzed in both the time and frequency domain, quantifying how disruptions influence the flow of goods.
To regulate the flow of goods through a supply chain, this thesis models the procurement, production and product pricing policies of SCM as individual PID controllers. The PID controllers are shown to regulate the flow of goods in a manner similar to how an actual manager would.
The effectiveness of the framework is demonstrated in a simulation study of the BWE in the supply chain of Valtris Specialty Chemicals (VSC). It is shown how tools in classical control theory, like Bode diagrams, are effective in the analysis of the dynamics and severity of the BWE in terms of the resonance frequency and the amplification, respectively. In addition, this thesis shows how the tuning of the PID controllers relates to specific adjustments in the policies of VSC managers. ...
The purpose of Supply Chain Management (SCM) is to maximize and stabilize the flow of goods through a supply chain. Disruptions to this flow, such as the Bullwhip Effect (BWE), have become increasingly severe and dynamic, challenging current methods for counteracting these disruptions. This thesis develops a systems and control framework for designing SCM policies to meet this challenge.
The thesis makes use of economic-engineering modeling principles to model the dynamics of the supply chain and calculate the flow of goods. Specifically, the supply chain is considered to be analogous to an electrical circuit, with the flow of goods analogous to the current and price changes analogous to voltage drops. Based on these analogies, the thesis develops the building blocks required for modeling a supply chain, consisting of storage, production and external markets. The building blocks are used to construct a serial supply chain with multiple stages and products. The supply chain is analyzed in both the time and frequency domain, quantifying how disruptions influence the flow of goods.
To regulate the flow of goods through a supply chain, this thesis models the procurement, production and product pricing policies of SCM as individual PID controllers. The PID controllers are shown to regulate the flow of goods in a manner similar to how an actual manager would.
The effectiveness of the framework is demonstrated in a simulation study of the BWE in the supply chain of Valtris Specialty Chemicals (VSC). It is shown how tools in classical control theory, like Bode diagrams, are effective in the analysis of the dynamics and severity of the BWE in terms of the resonance frequency and the amplification, respectively. In addition, this thesis shows how the tuning of the PID controllers relates to specific adjustments in the policies of VSC managers.
The thesis makes use of economic-engineering modeling principles to model the dynamics of the supply chain and calculate the flow of goods. Specifically, the supply chain is considered to be analogous to an electrical circuit, with the flow of goods analogous to the current and price changes analogous to voltage drops. Based on these analogies, the thesis develops the building blocks required for modeling a supply chain, consisting of storage, production and external markets. The building blocks are used to construct a serial supply chain with multiple stages and products. The supply chain is analyzed in both the time and frequency domain, quantifying how disruptions influence the flow of goods.
To regulate the flow of goods through a supply chain, this thesis models the procurement, production and product pricing policies of SCM as individual PID controllers. The PID controllers are shown to regulate the flow of goods in a manner similar to how an actual manager would.
The effectiveness of the framework is demonstrated in a simulation study of the BWE in the supply chain of Valtris Specialty Chemicals (VSC). It is shown how tools in classical control theory, like Bode diagrams, are effective in the analysis of the dynamics and severity of the BWE in terms of the resonance frequency and the amplification, respectively. In addition, this thesis shows how the tuning of the PID controllers relates to specific adjustments in the policies of VSC managers.
The Ripple Effect in the Housing Market
An Economic Engineering Model to Control Spatial Price Dynamics
In this thesis, an economic-engineering model of the ripple effect in the housing market is put forward. The ripple effect in the housing market is modeled by integrating into one model the local price dynamics of the rental market, the spatial price dynamics between local rental markets, and the valuation of real estate. Such a model is lacking in current housing-market literature.
The economic-engineering model design has two main aspects. First, economic-engineering model design uses analogs between mechanical systems and the housing market, ensuring that the model contains only economically and mechanically interpretable parameters. In this way input optimization and parameter optimization have a direct implementation in the real world. Second, economic-engineering model design relies on classical-mechanical modeling techniques that make the model suitable for optimally and robustly determining governmental policy using control formalism.
This thesis takes the perspective of the housing market as a heterogeneous economic space, where the dimensions are geographic proximity and economic influence. The model put forward in this thesis spatially discretizes this heterogeneous economic space into homogeneous local markets, such that local price dynamics govern the local markets and inter-local differences add spatial price dynamics.
The model put forward in this thesis is designed to predict where shortages will arise due to the introduction of rent control. Such a model guides policymakers in where to build for effectively relieving shortages in the housing market. Finally, such a model informs investors about the value change to expect due to policy changes and migration trends. ...
The economic-engineering model design has two main aspects. First, economic-engineering model design uses analogs between mechanical systems and the housing market, ensuring that the model contains only economically and mechanically interpretable parameters. In this way input optimization and parameter optimization have a direct implementation in the real world. Second, economic-engineering model design relies on classical-mechanical modeling techniques that make the model suitable for optimally and robustly determining governmental policy using control formalism.
This thesis takes the perspective of the housing market as a heterogeneous economic space, where the dimensions are geographic proximity and economic influence. The model put forward in this thesis spatially discretizes this heterogeneous economic space into homogeneous local markets, such that local price dynamics govern the local markets and inter-local differences add spatial price dynamics.
The model put forward in this thesis is designed to predict where shortages will arise due to the introduction of rent control. Such a model guides policymakers in where to build for effectively relieving shortages in the housing market. Finally, such a model informs investors about the value change to expect due to policy changes and migration trends. ...
In this thesis, an economic-engineering model of the ripple effect in the housing market is put forward. The ripple effect in the housing market is modeled by integrating into one model the local price dynamics of the rental market, the spatial price dynamics between local rental markets, and the valuation of real estate. Such a model is lacking in current housing-market literature.
The economic-engineering model design has two main aspects. First, economic-engineering model design uses analogs between mechanical systems and the housing market, ensuring that the model contains only economically and mechanically interpretable parameters. In this way input optimization and parameter optimization have a direct implementation in the real world. Second, economic-engineering model design relies on classical-mechanical modeling techniques that make the model suitable for optimally and robustly determining governmental policy using control formalism.
This thesis takes the perspective of the housing market as a heterogeneous economic space, where the dimensions are geographic proximity and economic influence. The model put forward in this thesis spatially discretizes this heterogeneous economic space into homogeneous local markets, such that local price dynamics govern the local markets and inter-local differences add spatial price dynamics.
The model put forward in this thesis is designed to predict where shortages will arise due to the introduction of rent control. Such a model guides policymakers in where to build for effectively relieving shortages in the housing market. Finally, such a model informs investors about the value change to expect due to policy changes and migration trends.
The economic-engineering model design has two main aspects. First, economic-engineering model design uses analogs between mechanical systems and the housing market, ensuring that the model contains only economically and mechanically interpretable parameters. In this way input optimization and parameter optimization have a direct implementation in the real world. Second, economic-engineering model design relies on classical-mechanical modeling techniques that make the model suitable for optimally and robustly determining governmental policy using control formalism.
This thesis takes the perspective of the housing market as a heterogeneous economic space, where the dimensions are geographic proximity and economic influence. The model put forward in this thesis spatially discretizes this heterogeneous economic space into homogeneous local markets, such that local price dynamics govern the local markets and inter-local differences add spatial price dynamics.
The model put forward in this thesis is designed to predict where shortages will arise due to the introduction of rent control. Such a model guides policymakers in where to build for effectively relieving shortages in the housing market. Finally, such a model informs investors about the value change to expect due to policy changes and migration trends.
Modeling the Green-Hydrogen Supply Chain as a Dynamic Economic System to Quantify the Value of Investments
An Engineering Design Approach
The green-hydrogen supply chain is part of the new energy business and will be operating in the new-energy markets. The new-energy markets are markets with volatile prices, and the prices are expected to become even more volatile in the future. A continuous-time model of the price and product flow dynamics is needed to quantify the value investments in the
green-hydrogen supply chain.
The economic engineering theory is used to create a dynamic economic model. In the economic engineering theory, analogs from the engineering domains are used to the economic domain. The use of the analog enables one to design a continuous-time model of the green-hydrogen supply chain, including the price and product-flow dynamics.
The engineering design approach allows one to include investments in the model of an existing economic system. The impact of the investment can be analyzed as one uses engineering analogs combined with dynamical systems theory and engineering tools. The analogy of the Laplace transformation as present value calculus enables one to quantify the impact of the value of investments in the green-hydrogen supply chain.
In this thesis, the Holland Hydrogen I project of Shell is used as an pplication to the framework. ...
green-hydrogen supply chain.
The economic engineering theory is used to create a dynamic economic model. In the economic engineering theory, analogs from the engineering domains are used to the economic domain. The use of the analog enables one to design a continuous-time model of the green-hydrogen supply chain, including the price and product-flow dynamics.
The engineering design approach allows one to include investments in the model of an existing economic system. The impact of the investment can be analyzed as one uses engineering analogs combined with dynamical systems theory and engineering tools. The analogy of the Laplace transformation as present value calculus enables one to quantify the impact of the value of investments in the green-hydrogen supply chain.
In this thesis, the Holland Hydrogen I project of Shell is used as an pplication to the framework. ...
The green-hydrogen supply chain is part of the new energy business and will be operating in the new-energy markets. The new-energy markets are markets with volatile prices, and the prices are expected to become even more volatile in the future. A continuous-time model of the price and product flow dynamics is needed to quantify the value investments in the
green-hydrogen supply chain.
The economic engineering theory is used to create a dynamic economic model. In the economic engineering theory, analogs from the engineering domains are used to the economic domain. The use of the analog enables one to design a continuous-time model of the green-hydrogen supply chain, including the price and product-flow dynamics.
The engineering design approach allows one to include investments in the model of an existing economic system. The impact of the investment can be analyzed as one uses engineering analogs combined with dynamical systems theory and engineering tools. The analogy of the Laplace transformation as present value calculus enables one to quantify the impact of the value of investments in the green-hydrogen supply chain.
In this thesis, the Holland Hydrogen I project of Shell is used as an pplication to the framework.
green-hydrogen supply chain.
The economic engineering theory is used to create a dynamic economic model. In the economic engineering theory, analogs from the engineering domains are used to the economic domain. The use of the analog enables one to design a continuous-time model of the green-hydrogen supply chain, including the price and product-flow dynamics.
The engineering design approach allows one to include investments in the model of an existing economic system. The impact of the investment can be analyzed as one uses engineering analogs combined with dynamical systems theory and engineering tools. The analogy of the Laplace transformation as present value calculus enables one to quantify the impact of the value of investments in the green-hydrogen supply chain.
In this thesis, the Holland Hydrogen I project of Shell is used as an pplication to the framework.
To match supply from intermittent renewable energy sources (RES) with demand, it is proposed in literature to introduce flexibility in the electricity market of the future. Flexibility can be provided by energy storage, demand response and cross-border transmission. In this thesis flexibility is modeled explicitly through the price mechanism of demand and supply.
This price mechanism can be made explicit with the principles of Economic Engineering. With that price mechanism a price-dynamic bond graph model of the electricity market of the future is built. This model can be used with the various tools that control engineering has to offer to aid investors and regulators in designing the electricity market of the future.
It can aid specifically in determining the adequate generation capacity, but also in determining the necessary power and energy capacity of storage, demand response and cross-border transmission.
As an example of application, this thesis demonstrates the use of the price-dynamic model by simulating a future scenario. By simulating trading behavior of a market participant the change of prices for a market with flexibility can be quantified. It is shown that passive control does not represent realistic trading behavior, so optimal control is used. To this end, an Economic Model Predictive Controller (EMPC) is designed to simulate how market prices change when a trader maximizes his profits through energy arbitrage. Based on these price changes it is advised that the Transmission System Operator (TSO) implements an energy storage reserve market to account for risk and ensure grid stability in the electricity market
of the future. ...
This price mechanism can be made explicit with the principles of Economic Engineering. With that price mechanism a price-dynamic bond graph model of the electricity market of the future is built. This model can be used with the various tools that control engineering has to offer to aid investors and regulators in designing the electricity market of the future.
It can aid specifically in determining the adequate generation capacity, but also in determining the necessary power and energy capacity of storage, demand response and cross-border transmission.
As an example of application, this thesis demonstrates the use of the price-dynamic model by simulating a future scenario. By simulating trading behavior of a market participant the change of prices for a market with flexibility can be quantified. It is shown that passive control does not represent realistic trading behavior, so optimal control is used. To this end, an Economic Model Predictive Controller (EMPC) is designed to simulate how market prices change when a trader maximizes his profits through energy arbitrage. Based on these price changes it is advised that the Transmission System Operator (TSO) implements an energy storage reserve market to account for risk and ensure grid stability in the electricity market
of the future. ...
To match supply from intermittent renewable energy sources (RES) with demand, it is proposed in literature to introduce flexibility in the electricity market of the future. Flexibility can be provided by energy storage, demand response and cross-border transmission. In this thesis flexibility is modeled explicitly through the price mechanism of demand and supply.
This price mechanism can be made explicit with the principles of Economic Engineering. With that price mechanism a price-dynamic bond graph model of the electricity market of the future is built. This model can be used with the various tools that control engineering has to offer to aid investors and regulators in designing the electricity market of the future.
It can aid specifically in determining the adequate generation capacity, but also in determining the necessary power and energy capacity of storage, demand response and cross-border transmission.
As an example of application, this thesis demonstrates the use of the price-dynamic model by simulating a future scenario. By simulating trading behavior of a market participant the change of prices for a market with flexibility can be quantified. It is shown that passive control does not represent realistic trading behavior, so optimal control is used. To this end, an Economic Model Predictive Controller (EMPC) is designed to simulate how market prices change when a trader maximizes his profits through energy arbitrage. Based on these price changes it is advised that the Transmission System Operator (TSO) implements an energy storage reserve market to account for risk and ensure grid stability in the electricity market
of the future.
This price mechanism can be made explicit with the principles of Economic Engineering. With that price mechanism a price-dynamic bond graph model of the electricity market of the future is built. This model can be used with the various tools that control engineering has to offer to aid investors and regulators in designing the electricity market of the future.
It can aid specifically in determining the adequate generation capacity, but also in determining the necessary power and energy capacity of storage, demand response and cross-border transmission.
As an example of application, this thesis demonstrates the use of the price-dynamic model by simulating a future scenario. By simulating trading behavior of a market participant the change of prices for a market with flexibility can be quantified. It is shown that passive control does not represent realistic trading behavior, so optimal control is used. To this end, an Economic Model Predictive Controller (EMPC) is designed to simulate how market prices change when a trader maximizes his profits through energy arbitrage. Based on these price changes it is advised that the Transmission System Operator (TSO) implements an energy storage reserve market to account for risk and ensure grid stability in the electricity market
of the future.
Forecasting Mortgage Prepayments in Changing Interest Rate Regimes
A Hybrid Economic-Engineering Model with EMPC
Master thesis
(2022)
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B.N.M. Krabbenborg, M.B. Mendel, M. Mazo Espinosa, C. Hutters, Pieter van Zwol
Banks such as Rabobank depend on multi-year mortgage prepayment forecasts in order to make provisions for the associated prepayment risks. The econometric models they use are fitted to historical data, and as a consequence their models are fitted to a decreasing interest rate regime. Given the current economic climate of increasing interest rates, Rabobank has ascertained that their models are underperforming. They expressed the need for an alternative modeling approach that performs better in changing interest rate regimes.
This thesis takes a systems and control approach motivated by this need. We split the development into two parts; a dynamical system for modeling mortgage payments and prepayments, and a controller for simulating mortgagor behavior.
We model the dynamical system by following the principles of economic engineering. Economic engineering is based on the method of analogs, and we develop specific analogies applicable to the mortgage market. We first derive a continuous model describing the mortgage payment and partial prepayment dynamics. This model is then extended towards a hybrid model to include the dynamics of full prepayment. The parameters of this economic engineering model can be identified with historical data and are relatively constant. The resulting model is not affected by the variation of interest rates and performs well in any interest rate regime.
We design an Economic Model Predictive Controller (EMPC) to simulate mortgagor behavior that minimizes an objective function of its costs. This controller minimizes an economic objective which is needed to simulate the behavior of mortgagors in changing interest rate regimes. For different interest rate scenarios, we forecast prepayments with the model by simulating this minimizing behavior. We perform simulations for different kinds of mortgagors by varying the model parameters and the objective function. Based on these simulations, we describe for each mortgagor both the exact cause and dynamics behind the mortgage prepayments supplied. ...
This thesis takes a systems and control approach motivated by this need. We split the development into two parts; a dynamical system for modeling mortgage payments and prepayments, and a controller for simulating mortgagor behavior.
We model the dynamical system by following the principles of economic engineering. Economic engineering is based on the method of analogs, and we develop specific analogies applicable to the mortgage market. We first derive a continuous model describing the mortgage payment and partial prepayment dynamics. This model is then extended towards a hybrid model to include the dynamics of full prepayment. The parameters of this economic engineering model can be identified with historical data and are relatively constant. The resulting model is not affected by the variation of interest rates and performs well in any interest rate regime.
We design an Economic Model Predictive Controller (EMPC) to simulate mortgagor behavior that minimizes an objective function of its costs. This controller minimizes an economic objective which is needed to simulate the behavior of mortgagors in changing interest rate regimes. For different interest rate scenarios, we forecast prepayments with the model by simulating this minimizing behavior. We perform simulations for different kinds of mortgagors by varying the model parameters and the objective function. Based on these simulations, we describe for each mortgagor both the exact cause and dynamics behind the mortgage prepayments supplied. ...
Banks such as Rabobank depend on multi-year mortgage prepayment forecasts in order to make provisions for the associated prepayment risks. The econometric models they use are fitted to historical data, and as a consequence their models are fitted to a decreasing interest rate regime. Given the current economic climate of increasing interest rates, Rabobank has ascertained that their models are underperforming. They expressed the need for an alternative modeling approach that performs better in changing interest rate regimes.
This thesis takes a systems and control approach motivated by this need. We split the development into two parts; a dynamical system for modeling mortgage payments and prepayments, and a controller for simulating mortgagor behavior.
We model the dynamical system by following the principles of economic engineering. Economic engineering is based on the method of analogs, and we develop specific analogies applicable to the mortgage market. We first derive a continuous model describing the mortgage payment and partial prepayment dynamics. This model is then extended towards a hybrid model to include the dynamics of full prepayment. The parameters of this economic engineering model can be identified with historical data and are relatively constant. The resulting model is not affected by the variation of interest rates and performs well in any interest rate regime.
We design an Economic Model Predictive Controller (EMPC) to simulate mortgagor behavior that minimizes an objective function of its costs. This controller minimizes an economic objective which is needed to simulate the behavior of mortgagors in changing interest rate regimes. For different interest rate scenarios, we forecast prepayments with the model by simulating this minimizing behavior. We perform simulations for different kinds of mortgagors by varying the model parameters and the objective function. Based on these simulations, we describe for each mortgagor both the exact cause and dynamics behind the mortgage prepayments supplied.
This thesis takes a systems and control approach motivated by this need. We split the development into two parts; a dynamical system for modeling mortgage payments and prepayments, and a controller for simulating mortgagor behavior.
We model the dynamical system by following the principles of economic engineering. Economic engineering is based on the method of analogs, and we develop specific analogies applicable to the mortgage market. We first derive a continuous model describing the mortgage payment and partial prepayment dynamics. This model is then extended towards a hybrid model to include the dynamics of full prepayment. The parameters of this economic engineering model can be identified with historical data and are relatively constant. The resulting model is not affected by the variation of interest rates and performs well in any interest rate regime.
We design an Economic Model Predictive Controller (EMPC) to simulate mortgagor behavior that minimizes an objective function of its costs. This controller minimizes an economic objective which is needed to simulate the behavior of mortgagors in changing interest rate regimes. For different interest rate scenarios, we forecast prepayments with the model by simulating this minimizing behavior. We perform simulations for different kinds of mortgagors by varying the model parameters and the objective function. Based on these simulations, we describe for each mortgagor both the exact cause and dynamics behind the mortgage prepayments supplied.
An Engineering Grey-Box Approach to Macroeconomic Scenario Modelling
Time- and Frequency Domain Analysis of Macroeconomic Systems
This thesis is a first effort to develop a grey-box model of a macroeconomic system. This is in contrast to current black-box modelling approaches. These black-box modelling approaches result in models where the variables and parameters have no economic interpretation. Companies who develop macroeconomic scenario models such as Ortec Finance, have identified this as a major limitation. In this thesis, an economic-engineering approach is taken. Engineers use the laws of physics to develop grey-box models. Economic-engineering theory is based on the analogies between the dynamics of economic phenomena on the one hand and mechanical phenomena on the other hand. By applying these analogues, a structural method is developed that translates the National accounting and the circular flow theory of macroeconomics into the laws of physics and engineering concepts. The engineering approach leads to three main contribution: 1) A bond graph model for the United States economy. 2) A linear time-invariant state-space model derived from the bond graph model to perform time domain analysis and 3) The Laplace transformation of the model to perform valuation and analysis in the frequency domain. In the financial industry, the use of the frequency domain for evaluating and modelling economic systems is a relatively new development. This thesis demonstrates the potential of an economic-engineering approach to formally use both the time- and frequency domain for macroeconomic scenario analysis and modelling, making them intuitive to both engineers and economists.
...
This thesis is a first effort to develop a grey-box model of a macroeconomic system. This is in contrast to current black-box modelling approaches. These black-box modelling approaches result in models where the variables and parameters have no economic interpretation. Companies who develop macroeconomic scenario models such as Ortec Finance, have identified this as a major limitation. In this thesis, an economic-engineering approach is taken. Engineers use the laws of physics to develop grey-box models. Economic-engineering theory is based on the analogies between the dynamics of economic phenomena on the one hand and mechanical phenomena on the other hand. By applying these analogues, a structural method is developed that translates the National accounting and the circular flow theory of macroeconomics into the laws of physics and engineering concepts. The engineering approach leads to three main contribution: 1) A bond graph model for the United States economy. 2) A linear time-invariant state-space model derived from the bond graph model to perform time domain analysis and 3) The Laplace transformation of the model to perform valuation and analysis in the frequency domain. In the financial industry, the use of the frequency domain for evaluating and modelling economic systems is a relatively new development. This thesis demonstrates the potential of an economic-engineering approach to formally use both the time- and frequency domain for macroeconomic scenario analysis and modelling, making them intuitive to both engineers and economists.
Model-Driven Objective Functions in MPC using Economic Engineering Systems Theory
With an Application to Supply-Chain Scheduling at Shell
This thesis introduces a theory for model-driven objective functions in Model Predictive Control (MPC) algorithms. For scheduling supply chains, such model-driven objective functions allow the MPC algorithm to make optimal scheduling decisions by anticipating future changes in product flow and transfer price dynamics. Including such dynamics introduces new insights in the decision-making process for supply chains, as current supply-chain management relies on professional expertise and modelling techniques with static product flows and transfer prices. In this thesis, a dynamic model for product flows and transfer prices at a storage depot in the supply chain is developed with Economic Engineering Systems Theory. We develop a model-driven objective function for profit-maximization in an MPC scheduling algorithm using the Economic Engineering storage depot model. The advantage of the model-driven objective function is the ability to assess the product flow and transfer price dynamics that affect the revenues and costs for various decisions. The MPC algorithm for scheduling shipments towards storage depots includes the constraints in the supply chain and offers the potential to control processes in the supply chain in a dynamic and automated way. This thesis applies the modelling technique and scheduling algorithm to the refined oil product supply chain of Shell for DACH. The algorithm automates processes that form the bridge between the yearly tactical planning and the day-to-day scheduling operations. Supply-chain companies like Shell benefit from the scheduling algorithm by optimal decision-making, additional time for strategic activities and less room for human error.
...
This thesis introduces a theory for model-driven objective functions in Model Predictive Control (MPC) algorithms. For scheduling supply chains, such model-driven objective functions allow the MPC algorithm to make optimal scheduling decisions by anticipating future changes in product flow and transfer price dynamics. Including such dynamics introduces new insights in the decision-making process for supply chains, as current supply-chain management relies on professional expertise and modelling techniques with static product flows and transfer prices. In this thesis, a dynamic model for product flows and transfer prices at a storage depot in the supply chain is developed with Economic Engineering Systems Theory. We develop a model-driven objective function for profit-maximization in an MPC scheduling algorithm using the Economic Engineering storage depot model. The advantage of the model-driven objective function is the ability to assess the product flow and transfer price dynamics that affect the revenues and costs for various decisions. The MPC algorithm for scheduling shipments towards storage depots includes the constraints in the supply chain and offers the potential to control processes in the supply chain in a dynamic and automated way. This thesis applies the modelling technique and scheduling algorithm to the refined oil product supply chain of Shell for DACH. The algorithm automates processes that form the bridge between the yearly tactical planning and the day-to-day scheduling operations. Supply-chain companies like Shell benefit from the scheduling algorithm by optimal decision-making, additional time for strategic activities and less room for human error.
Time-discounting in behavioural economics is modelled using mechanical system dynamics through the economic engineering framework. The economic engineering framework is being developed at the Delft Center for Systems and Control, and uses mechanical system dynamics to model economic processes and systems. Time-discounting is the calculation of the present value of the received utility from future consumption. Presently behavioural economists have
not been able to reach a consensus on how to model time-discounting behaviour. Two theories dominate economic literature: exponential discounting theory and hyperbolic discounting theory. These theories are treated separately by economists and have separate fields of application. Exponential discounting theory and hyperbolic discounting theory are shown to be related through the dynamics of the damped harmonic oscillator. Exponential and hyperbolic discounting theory are linked to the dynamics of the critically damped and overdamped mechanical system respectively. The dynamics of the underdamped mechanical system are linked to the time-discounting behaviour of a trader. Moreover, the parameters of the damped harmonic oscillator are interpreted economically, resulting in the following analogues: the natural frequency is analogous to the risk-free discount rate, the damping ratio is analogous to time-preference, and the real part of the eigenvalues are analogous to the exponential discount rate. Modelling time-discounting using mechanical system dynamics therefore results in a time-discounting model based on economic first principles. ...
not been able to reach a consensus on how to model time-discounting behaviour. Two theories dominate economic literature: exponential discounting theory and hyperbolic discounting theory. These theories are treated separately by economists and have separate fields of application. Exponential discounting theory and hyperbolic discounting theory are shown to be related through the dynamics of the damped harmonic oscillator. Exponential and hyperbolic discounting theory are linked to the dynamics of the critically damped and overdamped mechanical system respectively. The dynamics of the underdamped mechanical system are linked to the time-discounting behaviour of a trader. Moreover, the parameters of the damped harmonic oscillator are interpreted economically, resulting in the following analogues: the natural frequency is analogous to the risk-free discount rate, the damping ratio is analogous to time-preference, and the real part of the eigenvalues are analogous to the exponential discount rate. Modelling time-discounting using mechanical system dynamics therefore results in a time-discounting model based on economic first principles. ...
Time-discounting in behavioural economics is modelled using mechanical system dynamics through the economic engineering framework. The economic engineering framework is being developed at the Delft Center for Systems and Control, and uses mechanical system dynamics to model economic processes and systems. Time-discounting is the calculation of the present value of the received utility from future consumption. Presently behavioural economists have
not been able to reach a consensus on how to model time-discounting behaviour. Two theories dominate economic literature: exponential discounting theory and hyperbolic discounting theory. These theories are treated separately by economists and have separate fields of application. Exponential discounting theory and hyperbolic discounting theory are shown to be related through the dynamics of the damped harmonic oscillator. Exponential and hyperbolic discounting theory are linked to the dynamics of the critically damped and overdamped mechanical system respectively. The dynamics of the underdamped mechanical system are linked to the time-discounting behaviour of a trader. Moreover, the parameters of the damped harmonic oscillator are interpreted economically, resulting in the following analogues: the natural frequency is analogous to the risk-free discount rate, the damping ratio is analogous to time-preference, and the real part of the eigenvalues are analogous to the exponential discount rate. Modelling time-discounting using mechanical system dynamics therefore results in a time-discounting model based on economic first principles.
not been able to reach a consensus on how to model time-discounting behaviour. Two theories dominate economic literature: exponential discounting theory and hyperbolic discounting theory. These theories are treated separately by economists and have separate fields of application. Exponential discounting theory and hyperbolic discounting theory are shown to be related through the dynamics of the damped harmonic oscillator. Exponential and hyperbolic discounting theory are linked to the dynamics of the critically damped and overdamped mechanical system respectively. The dynamics of the underdamped mechanical system are linked to the time-discounting behaviour of a trader. Moreover, the parameters of the damped harmonic oscillator are interpreted economically, resulting in the following analogues: the natural frequency is analogous to the risk-free discount rate, the damping ratio is analogous to time-preference, and the real part of the eigenvalues are analogous to the exponential discount rate. Modelling time-discounting using mechanical system dynamics therefore results in a time-discounting model based on economic first principles.
Business Valuation in the Frequency Domain
A Dynamical Systems Approach
Business valuation is a set of procedures used by financial market participants to determine the price they are willing to pay or receive for businesses. Valuations play a crucial role in financial reporting, capital budgeting, and investment analysis. Current approaches to business valuation rely on professional expertise, causing the valuation to be as good as the analyst's assumptions. In this thesis, the entire valuation effort is translated into a systems and control problem. Economic engineering is used to formulate signal- and energy-based analogs for valuation concepts. A bond-graph model is developed to express general business dynamics as a set of differential equations. The model's analogy with the economic theory is shown by representing the model as a complex port-Hamiltonian system, and giving all elements and signals economic interpretations. A theory is developed to perform valuations in the frequency domain. This theory extends both the existing economic-engineering framework, and the existing valuation effort. Business valuation is performed by using this theory, by using the bond-graph model for business dynamics. The developed theory goes beyond the field of business valuations, as frequency-domain valuation can be applied to any type of financial instrument.
...
Business valuation is a set of procedures used by financial market participants to determine the price they are willing to pay or receive for businesses. Valuations play a crucial role in financial reporting, capital budgeting, and investment analysis. Current approaches to business valuation rely on professional expertise, causing the valuation to be as good as the analyst's assumptions. In this thesis, the entire valuation effort is translated into a systems and control problem. Economic engineering is used to formulate signal- and energy-based analogs for valuation concepts. A bond-graph model is developed to express general business dynamics as a set of differential equations. The model's analogy with the economic theory is shown by representing the model as a complex port-Hamiltonian system, and giving all elements and signals economic interpretations. A theory is developed to perform valuations in the frequency domain. This theory extends both the existing economic-engineering framework, and the existing valuation effort. Business valuation is performed by using this theory, by using the bond-graph model for business dynamics. The developed theory goes beyond the field of business valuations, as frequency-domain valuation can be applied to any type of financial instrument.
Dynamic Modelling and Control of the Oil Market
An Economic Engineering Approach
This thesis develops dynamical state-space models of the oil market and suggests opportunities for the application of control theory. The emphasis of this thesis is on the modelling, rather than the control theory applications. Most models that are currently employed by oil companies do not model short-term transient responses, but focus on long-term equilibrium modelling. Short-term transient response modelling of oil-economic systems can benefit trading and supply activities of oil companies. The models that are provided in this thesis have the ability to model short-term transient responses, due to the their dynamical nature and economic basis. To obtain these models, this thesis uses economic engineering. Economic engineering is a discipline that models economic systems, using an analogy between economic variables and engineering variables. The model development in this thesis consists of three parts. First, a fundamental economic-engineering model is built that represents the global crude-oil market. The model forecasts crude-oil prices using global crude-oil production as an exogenous input. The parameters of this model are estimated using system identification. Second, this thesis provides specialized models for common oil-market phenomena within the economic-engineering framework. These include refinery modelling, geographic dispersion, the futures market and economic growth. Finally, this thesis suggests applications for control theory. For these applications, various controllers are discussed. The identified fundamental model yields positive first results that do no reject the validity of the modelling approach. Moreover, the specialized models provide satisfactory solutions for several oil market mechanisms.
...
This thesis develops dynamical state-space models of the oil market and suggests opportunities for the application of control theory. The emphasis of this thesis is on the modelling, rather than the control theory applications. Most models that are currently employed by oil companies do not model short-term transient responses, but focus on long-term equilibrium modelling. Short-term transient response modelling of oil-economic systems can benefit trading and supply activities of oil companies. The models that are provided in this thesis have the ability to model short-term transient responses, due to the their dynamical nature and economic basis. To obtain these models, this thesis uses economic engineering. Economic engineering is a discipline that models economic systems, using an analogy between economic variables and engineering variables. The model development in this thesis consists of three parts. First, a fundamental economic-engineering model is built that represents the global crude-oil market. The model forecasts crude-oil prices using global crude-oil production as an exogenous input. The parameters of this model are estimated using system identification. Second, this thesis provides specialized models for common oil-market phenomena within the economic-engineering framework. These include refinery modelling, geographic dispersion, the futures market and economic growth. Finally, this thesis suggests applications for control theory. For these applications, various controllers are discussed. The identified fundamental model yields positive first results that do no reject the validity of the modelling approach. Moreover, the specialized models provide satisfactory solutions for several oil market mechanisms.