**5. Case study 3: informing economic revival initiatives in the northern Illinois region**

#### **5.1. Introduction**

The economy of the Northern Illinois Region, USA with Rockford as its largest city has suffered since the late 1980s as a result of the decline of manufacturing industry. Currently, a diverse initiative has been launched by various agencies to try to reverse the trend.

The case study presents a development of a decision-support tool to inform policymakers and stakeholders to revive the region's economy. To this end, the proposal will implement a holistic system approach. The approach used will be a combined system dynamics and SOS perspective. The issues that will be addressed include the interactions between the city quality of life factors and investment decisions.

#### **5.2. SoS conceptualization**

The combined SOS perspective and system dynamics have a capability to model economic decisions at three different levels such as city, company, and individual. **Figure 9** shows the multi-level decision making occurring within a macroeconomic context.

#### **5.3. System dynamics approach**

System dynamics is an approach to understand the behavior of complex systems over time. It deals with internal feedback loops and time delays that affect the behavior of the entire

**Figure 9.** SoS for urban systems.

**Figure 8.** Sensitivity of favorable scenarios to interest rate.

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**Figure 7.** Investment robustness outcomes (a) Investment1 (563 MW) and (b) Investment2 (264 MW).

system. It employs a causal loop and a stock and flow model to represent the change and accumulation of system variables (**Figure 10a**, **b**, respectively).

#### **5.4. Dynamics at macroeconomic level**

At the macroeconomic level, the approach captures the relationships between key economic factors of production (labor, land, capital, and technology) and economic output (e.g., production and GDP) (**Figure 11**).

At this level, several causal and feedback loops can be identified. We can identify, for example, two loops associated with migration. An increase in net migration will add to work force, and availability of work force will in turn reduce the rate of net migration (Loop 1). In the same way, an increase in migration will increase the competition for housing (reduced house availability) and availability of housing will make the city more attractive (increased net migration) (Loop 2).

#### **5.5. Dynamics at city level**

For the greater Rockford area, for instance, much efforts have been put in to increase the attractiveness for companies to invest (Source). The efforts focus on industrial infrastructure but also on factors contributing to the quality of life (school quality, housing, safety, health care, recreation facilities and parks) (**Figure 12**). They clearly want exploit the positive feedback in which companies expect quality workers, who in turn is attracted by improved quality of life.

**Figure 11.** A macroeconomic view of urban system interdependencies.

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**Figure 12.** Interrelationships between investment decisions and city attractiveness.

#### **5.6. Dynamics at company level**

At company level, an issue of interest is how investment decisions affect the production capacity and company's hiring (**Figure 13**).

**Figure 10.** System dynamics main building blocks (a) causal loop and feedback diagram. (b) Stock and flow diagram.

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**Figure 11.** A macroeconomic view of urban system interdependencies.

system. It employs a causal loop and a stock and flow model to represent the change and

At the macroeconomic level, the approach captures the relationships between key economic factors of production (labor, land, capital, and technology) and economic output (e.g., pro-

At this level, several causal and feedback loops can be identified. We can identify, for example, two loops associated with migration. An increase in net migration will add to work force, and availability of work force will in turn reduce the rate of net migration (Loop 1). In the same way, an increase in migration will increase the competition for housing (reduced house availability) and availability of housing will make the city more attractive (increased net migration) (Loop 2).

For the greater Rockford area, for instance, much efforts have been put in to increase the attractiveness for companies to invest (Source). The efforts focus on industrial infrastructure but also on factors contributing to the quality of life (school quality, housing, safety, health care, recreation facilities and parks) (**Figure 12**). They clearly want exploit the positive feedback in which companies expect quality workers, who in turn is attracted by improved quality of life.

At company level, an issue of interest is how investment decisions affect the production

**Figure 10.** System dynamics main building blocks (a) causal loop and feedback diagram. (b) Stock and flow diagram.

accumulation of system variables (**Figure 10a**, **b**, respectively).

**5.4. Dynamics at macroeconomic level**

duction and GDP) (**Figure 11**).

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**5.5. Dynamics at city level**

**5.6. Dynamics at company level**

capacity and company's hiring (**Figure 13**).

**Figure 12.** Interrelationships between investment decisions and city attractiveness.

The level of regional employment (at macroeconomic level) depends on the number of job vacancies at companies. The demand for workers will be driven by the need to fill production capacity, which in turn depends on customers' orders as they are influenced by how well the

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At individual motivational level, research has suggested psychological behavior that can be best captured using system dynamics [14]. **Figure 14** illustrates factors that are affecting workers' motivation at work and their accomplishments. For instance, quality of work will

Urban systems are facing pressures from population growth, urbanization, and climate change. Keeping the status quo could lead to failures in the systems. Given these challenges, better understanding of various elements of urban systems and their interdependencies is

The chapter presents a system-of-systems (SoS) framework to structure complexity of urban systems. A way to deal with future uncertainties within urban system SoS is described. As a whole, SoS forms a network of decision makers and engineered systems at various levels. Over time, the elements of SoS and their relationships will evolve. Their uncertainties can be handled using a computational approach called exploratory modeling and

The framework described in the chapter is applied to three case studies. Each case study highlights a unique aspect of urban systems. Different tools were employed to generate insights relevant for decision-making. The first case study looked at the vulnerabilities of urban system under perturbations and disruptions. It uses data related to urban infrastructure in Florida, USA that was devastated by hurricane. Network theory was applied to identify nodes of infrastructure that are influential in causing system failures and are critical for

The second case study assesses the performance of alternative investment decisions on electricity power plant. EMA is applied to define and explore uncertainty space in terms of measures of regret and robustness of each investment alternative. Once a preliminary robust decision has been identified, EMA can reveal a set of circumstances that may cause the decision to fail. The last case study conceptualizes the dynamics of urban economic revival within a larger macroeconomic environment. A system dynamics tool was employed to represent the context of an economic region in Midwest, USA. Detailed causal loop and stock-and-flow models were developed to specify factors and their relationships across individual, company,

economy is doing (i.e., economic growth at macroeconomic level).

depend on the effort devoted, which in turn depends on work pressure.

**5.7. Dynamics at individual level**

**6. Concluding remarks**

analysis (EMA).

recovery.

and city level.

needed to inform decisions to improve the systems.

**Figure 13.** Company's hiring mechanism as a response to customer orders.

**Figure 14.** Worker's motivation and work accomplishment dynamics.

The level of regional employment (at macroeconomic level) depends on the number of job vacancies at companies. The demand for workers will be driven by the need to fill production capacity, which in turn depends on customers' orders as they are influenced by how well the economy is doing (i.e., economic growth at macroeconomic level).

#### **5.7. Dynamics at individual level**

At individual motivational level, research has suggested psychological behavior that can be best captured using system dynamics [14]. **Figure 14** illustrates factors that are affecting workers' motivation at work and their accomplishments. For instance, quality of work will depend on the effort devoted, which in turn depends on work pressure.
