Public Sector Systems

Abstract

A discussion of the nature of public systems and their management. Examples of public systems and the services they provide show how complicated and complex they can be, and the challenges analysts have in providing information useful to those responsible for providing and managing them. Case studies involving modeling to improve system performance are briefly described as are the lessons learned from them.

2.1 Introduction

Let us begin with some definitions. Each discipline has its jargon, and the decision sciences and systems analysis disciplines are no exception. Probably the most common term used in this book on public policy modeling is the term ‘system’. For us, a system refers to a set of interdependent components that work together to accomplish the desired outcome. Wikipedia defines a system as a group of interacting or interrelated elements that act according to a set of rules to form a unified whole. A system, surrounded and influenced by its environment, is described by its boundaries, components, structure and purpose.

What distinguishes systems analyses from other analysis exercises is their focus on the performance of the system as a whole, rather than on each of the system’s components separately. They address the question of how each component, say of an urban transportation system or a community public school system, should be designed and operated to provide the maximum net benefits, however, measured, derived from the system. Determining just what is included in the system, as opposed to its environment, and how to describe that system in mathematical expressions, is part of the art of systems modeling, an art that this book introduces.

There are many types of systems, of course, and in this case, we are primarily interested in those in the public sector, such as those managed by governmental agencies or non-governmental organizations. Figure 2.1 illustrates a public health system, where depending on the issues being addressed, each of the components could be a system of interacting components itself. Most systems are systems of systems. It is up to the analyst to define the appropriate detail to include in each component of any model depending on the issues being addressed, and the data and time available for the study, among other factors.

Fig. 2.1

A public health system of interacting interdependent components

This public health system is just one sub-system of any urban system, even relatively small ones as shown in Fig. 2.2.

Fig. 2.2

A community consisting of interacting systems that provide the educational, environmental, public health, recreational, social, and transportation services people need and expect

The systems referred to in Figs. 2.1 and 2.2 are obviously both complicated and complex. There are many possible ways of designing and managing them and many possible measures of performance. Furthermore, given any decision, the results are not always predictable. The purpose of this book is to introduce some tools that may help identify, analyze, and evaluate the estimated impacts of alternative system design or management policies that one could face working in public or non-governmental organizations. Such information should be helpful to anyone having to decide what decision to make or what course of action to take to address a particular issue or problem. Depending on the problem or issue being addressed, the possible impacts of any decision may be physical (including medical), economic, environmental or ecological, political, and/or social. Models can be developed and used to estimate any or all these impacts, as appropriate. It is up to those developing and using models to decide what to include in any analysis and what information is needed to best inform those involved in the decision-making processes.

2.1.1 Managing Public Systems

Some public agencies are using systems approaches to successfully manage complicated issues (e.g., banking regulation, trade treaties, community transportation, and healthcare systems). Such systems may have many components and uncertainties, but it is possible to understand how each of these systems can be designed and managed to achieve specified goals. However, the nature of other public sector problems, frequently referred to as wicked or messy ones, are more difficult to assess and, therefore, are more challenging to manage. Rather than having discrete components linked together in ways that are clear, often the functioning of components as well as their interactions with others in public systems are not clear. For example, it might be hard to establish whether the reduced use of plastic is a result of improved industrial packaging, changing consumer habits, or stricter plastic disposal controls. Policy decisions for such wicked systems can have unintended consequences. For example, the construction of a simple road overpass in Somerville, Massachusetts—which was needed from an infrastructure development perspective—led to a rise in childhood obesity rates due to part of the community being cut off from leisure and sporting facilities (Curtatone & Esposito, 2014).

Systems approaches have been usefully applied in a variety of public policy fields. For example,

• Childhood obesity and social policy in Australia (Allender et al., 2015; Canty-Waldron, 2014).

• Child protection in England (Lane et al., 2016).

• Design/management of children’s services in England and Wales (Gibson & O’Donovan, 2014).

• Health issues including obesity, tobacco use, and mental health services in North Wales (Evans et al., 2013) and public health more generally (WHO, 2009).

• Higher education in the United Kingdom (Dunnion & O’Donovan, 2014).

• Environmental issues in Sweden (Lundberg, 2011), waste oil management in Finland (Kapustina et al., 2014), and sustainable food consumption in Norway.

• Infrastructure planning in Australia (Pepper et al., 2016).

• Military and political affairs in the United States (de Czege, 2009).

• Energy production and ecosystem preservation in South East Asia (Thomas et al., 2017).

In complex systems, cause and effect may only be obvious in hindsight, highlighting the need for different analytical tools that together can identify and evaluate adaptive policies and produce a better understanding of how particular systems function. It is important to understand the systems being analyzed and not underestimate the possibility of being surprised.

Few would disagree that the public policy world of today can be volatile, uncertain, complex, and ambiguous. Solutions proposed to address problems or opportunities are often strongly contested. Not everyone has the same goals or desires. Therefore, many policies developed to address problems fail because of a lack of sufficient political support or from unforeseen side effects or difficulties in communication, coordination, and monitoring. The challenge for systems analysts is, therefore, to generate meaningful (and useful) policy options that can adapt to future surprises and conditions that are today unknowable, while satisfying today’s goals and needs. To introduce more jargon, some call such policies robust.

The process of solving a problem involves understanding the nature of that problem. Those advising policymakers have a collective responsibility to collect, verify, and synthesize information in pursuit of a more coherent and complete knowledge, say for ‘what can be done about x’. However, no amount of modeling and data analyses will answer political or normative questions like ‘what should be done about x’. That is a political decision. But again, models and data can inform those who make such decisions.

Politics is more difficult than physics.

Albert Einstein

(Conference in Princeton, N.J. January 1946.)

Public systems modelers will be working in a political environment and will likely find that more challenging than the modeling itself. Examples of public systems challenges can include the following:

• Criminal justice system reform with respect to the death penalty, controlling the use of addictive drugs, reducing gun violence, and prison terms and conditions.

• Economic issues such as distribution of resources, collection and amount of taxes and trade tariffs, minimum wages, and sick leave policies.

• Educational elementary and secondary educational system issues such as funding, setting of school capacities, school districts, class sizes, staffing, and school food programs.

• Legal system policies with respect to sports betting, sexual harassment, affordable housing, immigration policy, disaster response and insurance requirements, drinking water and air quality standards, driving speed limits, gun control, data privacy, voter registration and voting rights, political redistricting, child abuse, and domestic violence.

• Environmental systems policies related to water and air quality and noise, clean energy and climate change mitigation measures, and wetland and wildlife protection.

• Health system issues such as healthcare access; use of opioids, medical marijuana, and prescription drugs; insurance requirements; and controlling pandemics.

• Social system issues such as welfare policies, homeless management, food programs, police protection, workers and labor union rights, animal rights, and social media regulations.

• Transportation system issues involving the use of motor vehicles, bikes, scooters, and buses, pedestrian walkways, licensing, infrastructure capacity and maintenance, and control of drones and airplanes.

These, like many public sector systems, often have design, organization, and management issues that can be analyzed to identify and evaluate alternative ways of addressing them. Obviously, we can’t address each of these issues in this introductory book but we can begin to introduce some of the tools that one might use to analyze such issues. The problems in this book are simpler than those listed above, but still interesting or complex enough to warrant and illustrate the use of what is called systems analysis. Systems analysis includes developing and solving models of systems. Solutions of models can help us determine what, where, when, and how much to do to accomplish some goal or objective. We will use various modeling approaches to identify preferred system designs and operating policies with respect to various objectives or goals that might be considered.

2.2 Why Apply a Systems Approach to Public Policy?

The increasing development and use of technology and the automation and information it brings into our lives are creating challenges in our workplaces as well as for both our education and health and welfare systems. Ensuring a high-quality, active life for an aging population puts pressure on developing improved ways of providing medical and social care. Climate change, obesity, radicalization of social behavior, income inequality, and poverty are all issues faced by today’s public policymakers. What causes these and other public policy challenges and to what extent? How can they be effectively addressed without generating even more problems?

More holistic policy approaches that can define the major factors impacting a policy issue, that can identify the interactions among relevant components of systems, that can focus on the performance of the whole system rather than only on its separate components, and that embrace the goals of stakeholders have the potential to substantially improve the policy-making process. Such systems approaches can inform policymakers on the impacts of what they might decide to do and thus allow them to focus on the bigger picture, i.e., on the areas where change can have the greatest impact and on the goals they want to achieve.

Government agencies and those NGOs and others who serve them are increasingly using systems approaches to address problems and to identify and evaluate possible decisions impacting the performance of their policies and programs. Public institutions are slowly changing from a procurement-driven policy of only using external consultants and contractors to perform systems studies, toward employing systems analysts to have systems analysis capabilities within their organizations and to be able to perform analyses continually as part of their everyday practice.

Implementing change in the public sector can often be difficult. Not everyone wants the same change, or even any change. Decision-makers are typically risk averse especially regarding the possibility of failure. In many cases, one cannot stop providing an existing public service, such as air traffic control, or water and wastewater treatment, or protection from natural hazards, as changes are made in providing those services. Systems approaches can help navigate such transitions. Systems approaches can help organizations continue to provide services while changing the design and/or operation of the entire system at the same time.

Changing a system or service often requires building new skills into organizations to help them face and adapt to new circumstances. Systems changes impact people as well as infrastructure. As such, they invariably spur debates about the relative value of policy choices and the tradeoffs among conflicting goals to be made. Consider the efforts of public health experts attempting to achieve higher percentages of vaccinated people. This has proved to be more difficult than expected even when it would seem the best decision for each individual is obvious, at least for those wishing to avoid sickness or death. In the case of car-sharing in Canada, having a flexible transportation system took precedence over other work condition concerns. In Iceland, domestic violence had to be labeled a public health issue rather than a private matter to gain public support. It is not easy to transform public systems and public opinion. But again, applying systems methods to identify and evaluate alternatives and their benefits, costs, and possible environmental, ecological, and social impacts can help provide the information needed to help generate the support and understanding needed to enable change to happen (OECD, 2017).

Complexity and uncertainty are common properties of public systems. The defense and intelligence communities refer to this state as ‘VUCA’, a state of Volatility, Uncertainty, Complexity, and Ambiguity. One can argue that VUCA characterizes much of the public sector as a whole, even if administrations do not understand how, where, or why. One key concern is how best to account for uncertainty while managing greater complexity and still deliver effective services. To a degree, the answer lies in a policy-making approach that leads to robust systems and adaptive policies. The effectiveness of the decisions made to address a problem or issue will depend on how completely the problem and the system it is a part of are understood. It also requires acknowledging uncertainty as part of everyday decision-making. Changing public policy dealing with problems stemming from interconnectivity, cyber threats, climate change, changing demographic profiles, and migration, to mention a few of today’s issues, is not easy. The complex process of seeing, understanding, and deciding is fundamentally challenging our institutions. Appropriate use of systems approaches and modeling can often help inform those involved in this process. They can help policymakers identify what, at a more detailed systems level, may be impacting their view of the system at a higher level. Figure 2.3 shows, at least conceptually, how a system may look quite different at a detailed, say at ground level, compared to at 3000 feet or 1000 m—the higher level. Both reveal information the other does not.

Fig. 2.3

Getting into the detail may reveal entirely different perceptions of urban sub-systems needs than at higher levels of policy-making

Public policymakers have traditionally dealt with social problems by making only incremental change decisions. While often perceived as being a safer approach in terms of political risk, such incremental changes may only shift consequences from one part of the system to another or just address symptoms while ignoring causes. Part of learning the art of developing and applying systems models is in defining the system that is to be analyzed. Typically, each component of a system is a system in itself, and hence, the detail to be included in a model of a system of systems is determined by the modeler. Clearly, it also depends on the issues being addressed, the time and data available to address them, and the questions being asked and the decisions being considered by policymakers, which indeed can change over time. The umbrella phrase ‘systems approaches’ is used to describe a set of processes, methods, and practices that aim to define systems and improve their performance. Using systems approaches to address public sector problems and issues can prove challenging for many reasons, and one may be due to limited institutional authorities and capabilities, but applying them can sometimes motivate changes in institutional missions and structures as well.

2.2.1 When to Use the Systems Approach

It is reasonable to ask when does it make sense to consider using a systems approach to address a public policy issue. What are the necessary conditions? What unknown decision variables should be considered? In other words, what is to be decided? What is to be achieved? There are no common answers to these questions because each situation is different. However, in general, if the following conditions apply, the use of systems analysis methods within an institution may be beneficial.

• An ‘innovative’ attitude and desire for improving the services provided by a decision-making institution, whether local or national or international.

• The inclusion of stakeholders, the public, in decision-making is not only possible but a priority.

• Satisfying stakeholder interests is an institutional goal.

• There is sufficient trust and capacity in government to think outside the box, i.e., to experiment.

• Policy issues are complex enough to be difficult to address within disciplinary or institutional silos.

• There exist one or more champions (persons or institutions) committed to leading the study and able to implement change.

• There exist sufficient funding and time and data and expertise to perform the analyses.

Policing, community recreational services, environmental protection, planning, forest, crop and water management, housing, infrastructure capacity expansion planning, waste disposal, and energy production and use are all domains in which systems approaches have shown to be of value. In later chapters, you will have an opportunity to model such systems. The common denominator is that these services directly interface with the needs and lives of people whose expectations and realities are changing in an environment of technological, economic, and global change. Successfully addressing an issue today does not mean it will not have to be addressed again at a later time.

2.3 Data: Are There Ever Enough?

To understand policy problems better, analysts require data. Models can be helpful in identifying just what data are needed to make decisions. Modeling can be helpful in identifying not only the types or kinds of data but also their needed accuracy. Just how this is done will be illustrated in some of the following chapters of this book. For example, a model developed in Chap. 8 for finding the least-cost pollution control policy can identify the least-cost decisions even without knowing the precise costs of those decisions themselves. Hence, the common temptation to divide a systems study into two parts, the first being to collect all the available data, and the second part to think about how to use these data, should be replaced with a simultaneous coordinated modeling and data collection effort. Models can help identify what is needed, and data collection can identify what data are potentially available.

Today, collecting ‘enough’ of even the needed data for some policy analysis studies may be too resource-intensive or even impossible. The sufficiency of information is always an issue. In such situations, how to proceed with confidence? There is often no definitive answer. But it is worth remembering that the results of models are always based on assumptions. They address and provide answers to ‘what if’ questions. This allows decision-makers to focus on what they think the best assumptions might be rather than on what is best given some assumptions.

Appendix

2.1.1 Some Case Study Summaries

• (a) Tackling domestic violence in Iceland.

The Icelandic government has used systems analysis to develop and implement a program addressing violence against women. The program introduces a new integrated support system for victims based on the concept that domestic violence is a social (and not private) harm affecting everyone. Following research findings on domestic violence, and supported by new legislation, the program supports the victim and concentrates on stabilizing the family, rather than focusing on providers and authorities (lawyers, police, social services, etc.). Today, the police, social, and child protective services (and increasingly schools and healthcare providers) are working in a coordinated fashion to detect and respond effectively to domestic violence across Iceland (OECD, 2017).

• (b) Reshaping the child protection services in The Netherlands.

CYPSA (Jeugdbescherming Regio Amsterdam) is a regional Dutch organization certified to provide Child and Youth Protection Services in the Amsterdam area. Since 2008, the organization has worked to redefine its purpose using a systems approach. As a result, the organization adopted a new mission for its activities entitled “Every Child Safe, Forever.” CYPS redesigned its entire system to fulfill that purpose and ensure it had a meaningful impact (OECD, 2017).

• (c) Regulating Public transportation in Toronto, Canada.

Disruptive technological change and the emergence of the ride-sharing economy are at the core of this case study. In Canada, digitalization impacts all levels of government—city, province, and federal. Policies connected to emerging fields of the economy (e.g., housing and transportation, insurance, taxation, etc.) are regulated at different levels of government. This creates a problem—who has ownership over a governance issue? In 2014, the transportation network company Uber started to operate in Toronto without specific regulatory oversight. To tackle the regulatory challenge and simultaneously preserve the beneficial aspects of a sharing economy, an independent arbiter using systems methods proposed a sharing economy strategy for Toronto (and by extension cities across Ontario). They also helped develop new legislation that enables the city and its citizens to both regulate and benefit from new entrants that disrupt old businesses (OECD, 2017).

• (d) Deciding how to share the Nile (Ethiopia).

The continuing conflict in the Nile River Basin between Egypt, Sudan, and Ethiopia over the filling of Ethiopia’s newly built Grand Ethiopian Renaissance Dam is perhaps one of the best examples of an international ‘wicked’ water management problem. So far, after a considerable number of modeling studies by just about every academic, consulting firm, NGO, and agency or research institution that models water, including modeling studies designed to check up on the results of other modeling studies, no acceptable solution is apparent. This is in spite of negotiations that continue to take place at the highest government, and even international, levels. Downstream Egypt and Sudan do not want any increased risk of not having the water they consider they are entitled to, and upstream Ethiopia wants to fill the dam so as to maximize hydropower production to help meet the considerable demand for electrical energy in their country and in the surrounding region. Water stored in a reservoir or that evaporates from the reservoir is not then available downstream and that scientific fact for Egypt and Sudan is unacceptable. All water allocation issues can turn into wicked ones that have no solutions when there is an unwillingness to compromise or think outside the box in order to enlarge the options for achieving an acceptable water management policy (El-Fekki & James, 2021).

• (e) Modeling ecosystems in the Great Lakes (Canada and US).

A joint Canadian-US five-year 20-million-dollar systems study to identify improved operating policies for controlling the lake levels and river flows of the lower Great Lakes basin began over two decades ago. The study was undertaken by the International Joint Commission that oversees the management and operation of all boundary waters between the two countries. The Great Lakes serve multiple purposes and users. These purposes include hydropower production, shipping, commercial fishing, recreational boating, shoreline protection, and ecosystem enhancement. Ecosystem enhancement is often in conflict with other goals, especially shoreline preservation. Floodplain ecosystems benefit from some variation in water levels and flows, whereas shoreline owners would prefer low constant levels that cause less erosion. Higher and more constant lake levels are preferred for other purposes if they are below flood stage. Furthermore, benefits derived from all the purposes but ecosystem enhancement can be expressed in monetary terms. But the main motivation for this study was to find operating policies that better protected, and in fact restored, wildlife habitat along the shores of the lakes and downstream river. At one point during this study, the US co-chair of the IJC requested a benefit–cost analysis that included all the purposes served by the Lower Great Lakes system, including ecological habitat restoration. He specifically wanted to know the dollar value of a muskrat since the main conflict was between what shoreline owners wanted and what ecologists assumed muskrats (representing wetland habitats) wanted. Without being able to justify a specific dollar value for ecosystem enhancement, the study ended after 5 years without that benefit–cost analysis and thus without a decision. The commissioner claimed later that not getting that analysis was one of the reasons no decision on a revised operating policy was made—until nine more years of further analyses and political debates (IJC, 2006).

• (f) Needing an interested client (Ghana).

A few years ago, the African Development Bank funded a project exploring the possible reoperation of the Akosombo Dam on the Volta River. This hydroelectric dam in southeastern Ghana is operated by the Volta River Authority. Since the beginning of its operation in 1965, the dam’s discharges have degraded the downstream ecosystem of the river and its floodplains. This in turn has adversely impacted those living downstream of the dam. The aim of the project was to find an alternative operating policy that would restore the downstream ecosystems while still meeting electrical energy demands. The institution overseeing the project was the power authority. It had the authority to alter the dam’s operating policy, but producing power and generating electricity were their main missions and objectives. Here come these foreign scientists and modelers on relatively short visits to work with the authority and to help them obtain the data and develop the necessary models needed for establishing a reoperation policy and estimating its impacts. While spending considerable time with many of the impacted stakeholders as well as with the staff of the power authority during those visits to Ghana, the authority made it clear during each visit that ecosystem restoration was not their mission or interest. It might not have made any difference, but not being able to work closely and continuously with all involved in the project surely contributed to the failure of the modelers to gain the level of trust and understanding needed to enable a successful reservoir reoperation result (Opgrand et al., 2019).

• (g) Modeling the Great Man-made River (Libya).

The Great Man-made River in Libya is a system of wells, pumps, pipes, and reservoirs designed to bring water from aquifers in the Sahara Desert to where water is needed along the Mediterranean Sea coast where most Libyans live and irrigate crops. Optimization models were used to identify cost-effective designs and operating and capacity expansion policies and to compare their costs to the costs of other alternatives for satisfying Libya’s water demands. Getting the data to enable that modeling proved to be a challenge. Individual government agencies considered the data they had gave them power and were not willing to give that up. Only until some degree of trust was developed (on the squash courts) between the foreign modelers and agency personnel did it become possible to obtain the needed data.

As a footnote, during the planning and construction of the Great Man-made River, several engineers convinced the New York Times, a major and trusted newspaper in the US, that instead of being a water distribution system the project was really intended for transporting troops and tanks in trucks and trains to where Libya could invade Libya’s neighboring countries without being seen by satellites. This ‘news’ was published on the front page of the New York Times, whose motto is ‘all the news that is fit to print,’ on December 2, 1997. Indeed, it supported the popular notion that Libya’s government was not to be trusted (Bonner, 1997).

• (h) Water and qat security (Yemen).

Sana’a, the capital of Yemen, depends on an aquifer for its water. Years ago a groundwater modeling study showed that this aquifer would be depleted in a decade or two due to excessive withdrawals. Most of the groundwater withdrawals were being used for growing qat, a green-leaved plant that has been chewed by Yemenis for centuries for its stimulant effect. Asking Yemenis to restrict their chewing of qat would be similar to asking coffee drinkers to restrict their drinking of coffee. Finding a socially as well as economically acceptable solution to this water management problem proved to be difficult. When suggesting to policymakers that perhaps this issue should be discussed in public in hopes of enlisting their help and support in identifying a suitable solution, they, the policymakers, rejected the suggestion. “Why should we worry about this potential crisis? When it happens, we may not even be alive.”

• (i) Restoring the Florida Everglades (United States).

An example of having to adapt to unforeseen consequences involves the long-term project to restore the ecological health of the vast Everglades wetlands in the state of Florida in the US. Begun two decades ago, this project is arguably the most ambitious ecosystem recovery effort anywhere. It is in some sense in response to past management decisions that focused on development and did not consider preserving this unique environment. The project is essentially a vast re-plumbing scheme aimed at replicating as nearly as possible the historical freshwater flows over the flat wetlands of the Everglades—often called the River of Grass—that once made South Florida a biological wonderland. These flows were diverted when in the late 1940s the US Army Corps of Engineers initiated a flood control project aimed at protecting land for urban and agricultural development. Over a half-million acres were drained by a network of levees, canals, and pumping stations. While making Florida’s eastern coast and midlands safer for development, it also destroyed much of the Everglades ecosystem including its wildlife. Now people care more about this unique ecosystem and the environment than they did when the decision was made to ‘drain the swamp’. The ongoing restoration project involves taking out much of that drainage and diversion infrastructure and restoring the overland flows to their original patterns to the extent possible to maintain what remains of this unique environment and ecosystem. The project is being informed by numerous simulation models and modelers from multiple federal and state agencies, each responsible for addressing a range of issues. The hope is that this unique ecosystem will continue to motivate people to visit (and spend their time and money in) Florida (Grunwald, 2006).

• (j) More water management modeling (Africa, Asia, Europe, and US).

Successful examples of effective ongoing use of the systems approach to inform those managing water include the Mekong River Commission’s Decision Support Framework (Mekong DSF), the Nile Basin Initiative’s Decision Support System (NB DSS), and the flood forecasting model, FloRiAn, of the International Commission for the Protection of the Rhine (ICPR), the Corps’ Water Management System (CWMS) used by the U.S. Army Corps of Engineers to support its regulation of river flows through reservoirs, locks, and other water control structures located throughout the US. Other water allocation models are being used to inform managers of the Senegal and Zambezi Rivers in Africa and the Euphrates and Tigris Rivers in the Middle East, the South-North water diversion project in China, and in the operational management of Lake Como in Italy (FAO, 2021; Stakhiv et al., 2020; Todini, 2014).

• (k) Educating young modelers (US).

When in the 1970s the Clean Water Act and its Amendments were passed in the US, they required all point sources of wastewater to be treated using ‘best management practices’ (that generally meant secondary treatment that removes about 80% of oxygen-demanding pollutants from wastewaters) before discharging them into receiving surface water bodies. The CWA policy became an expensive national public works program. Model studies showed that considerable money could be saved by adopting cost-effective policies, policies that met surface water quality standards at a minimum cost. In terms of infrastructure construction and operation costs, the CWA policy was expensive, but politically it was cheap. To enforce the CWA policy required monitoring only the quality of wastewater treatment plant influents and effluents, an easier task than monitoring the quality of wastewater influents and effluents and receiving surface water bodies. Modelers who could identify more cost-effective wastewater treatment policies for particular watersheds and river basins did not have to defend their models, along with their assumed model parameter values, in court. Every potential polluter was treated equally. Investigations into which polluter upstream contributed to a water quality standard violation downstream, and by how much, were not necessary. Politically, the CWA policy was a much easier and less costly policy to implement. So much for the education of those advocating cost-effectiveness.

• (l) Food security (Algeria).

To become more self-sufficient in feeding its people, the government of Algeria initiated a study (in the 1970s) aimed at identifying the sites, design capacities, and operating policies of infrastructure needed to capture, store, and deliver irrigation water to parts of the Sahara Desert for growing crops. The system performance measures the government wanted considered were infrastructure instillation and operating costs and the amount and reliability of water delivered. The task of the modelers was to identify alternatives that represented efficient tradeoffs among these three conflicting objectives. Upon presenting some results for one region of the country the government chose an inferior solution, one that cost more, was less reliable and produced less water than many other possible solutions. When asked why that plan was chosen, the answer was that their chosen plan satisfied other objectives better. This is an example of the fact that the set of project objectives and their relative importance can change during a modeling, planning, and policy-making process, especially as all involved learn more from the modeling and other sources about what is possible and hence what can be achieved.

• (m) Asking the right questions (Cambodia).

In the Mekong basin, as in many other river basins in this world, hydroelectric dam builders are busy practicing their trade to meet increasing demands for energy. In one recent study, the question being addressed was where to site and how to design and operate a series of dams to produce hydroelectric power. Framing the question in this manner leads one to identify dam sites and hydropower plant capacities and reservoir operating policies needed to meet specified energy targets. Framing the question to be how to produce more energy leads to a broader range of options including the consideration of solar panels on existing reservoirs. In the Lower Mekong, solar power was shown to be a much less expensive option than building and operating more dams and less damaging to the ecosystems and biodiversity of the river. This information had an impact on a decision not to build a particular dam that was planned. For how long that decision will apply, who knows (Ratcliffe, 2020; Thomas et al., 2017).

• (n) Achieving cleaner air (Europe and India).

Where several different goals compete, modeling can help to find a balance. A highly successful example is the Regional Air Pollution Information and Simulation Model (RAINS). In the 1990s, RAINS helped to guide Europe’s policy on six air pollutants, including particulates and sulfur dioxide (the chief cause of acid rain), calculating costs and health effects of various policies. RAINS results in Europe and India have shown the power of cooperative action on air pollution, which is much more effective than efforts by any single state and, therefore, more politically attractive. Now extended to include greenhouse gases, the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) Model reveals how clean-air policies can have co-benefits, improving the health of people and ecosystems while also curbing climate change (Battersby, 2021).

2.1.2 Lessons from These Case Studies

The application of systems studies of public policies is often triggered by a perceived crisis or opportunity. This may take the form of an actual crisis or a perception that the current performance of a public system could be better. All the case studies highlight that someone needs to have a vision and take direct ownership of the problem. All the case studies outlined above exhibited either some level of urgency or obvious opportunity to serve the public better that motivated the systems analyses. This in turn created a window of opportunity. Would the domestic violence project have developed if Iceland had not experienced a social or fiscal crisis? Would the modeling projects in the Nile, in Libya, and in Florida have taken place without some sense of urgency? Probably not. In short, the acknowledgment of cumulative severe effects can lead to a sense of urgency or crisis. However, the case studies from the Netherlands and Algeria and Yemen indicate that it is difficult to implement changes during truly chaotic moments in organizations, as some level of stability must be reached to initiate a broader systems study. The stakeholders involved in such situations need to retain a sense of urgency, even in a stable environment. Maintaining the political will is an essential part of implementing change in more static conditions. Those at the highest level of an agency need to acknowledge that change is needed in the services they provide. While achieving an agreement that there is a problem or opportunity is the first step, it is not enough. There has to be an agreement that something should be done to address the problem or take advantage of an opportunity. This agreement has to become actionable involving people and place.

Once organizations recognize the need to change, they must invest the time to understand and articulate both the problems to be addressed and the objectives to be achieved. In the case of the Netherlands, this meant long internal discussions and the identification of a new mission: “Every Child Safe, Forever.” The organization understood that they needed to focus on children’s safety and to start treating adults as parents first and individuals second. In the case of Iceland, broader community discussions with the police, social services, child protection, the church, and so on were initiated. These reaffirmed the notion that domestic violence is a public health issue and not a private matter, thus prioritizing the social effects of violence over privacy. In Canada, the value debate made it clear that a more flexible, affordable transportation system was preferred over other concerns. In the case of Ghana, the responsible organization never considered a change to be in their interest, as indeed it might degrade the service they were currently providing.

When implementing change, stakeholders may suggest many objectives or goals to be achieved. Some goals may conflict with others. This was the case in the Great Lakes, Algeria, Nile, Ghana, and Florida Everglades studies. In these cases, systems modeling was able to identify the tradeoffs among conflicting objectives or performance measures. Chapter 16 in this book is devoted specifically to how this can be done.

Meaningful measurement, modeling, and monitoring are key to addressing and finding acceptable solutions to complex problems. Without them, causality and the effects of interventions are often difficult to assess. In the Netherlands, a specific measure was used to evaluate child safety—‘acute child safety’. In Iceland, a new risk framework was adopted. In the Canadian case study, the whole process was initiated to produce a legitimate evaluation of the impetus for change. Consequently, modeling served as a communication tool used to justify the process of systems change and the use of systems approaches themselves. The evaluation carried out by the Institute for Gender, Equality, and Difference at the University of Iceland, regarding the domestic violence project, helped to keep the process going. In Toronto, the facilitator’s evaluation, alongside additional federal and non-governmental reports, paved the way for the city to advance the sharing agenda. Agencies involved in the restoration of the Florida Everglades are typically spending over $50 million annually for modeling and monitoring and data management. They clearly believe if you cannot measure and monitor, you cannot manage needed change.

A number of other factors emerged from these case studies. First, contextual factors impact systems change. Timing is important and supporting elements must come together to create a ‘window of opportunity’. Second, different resources are needed for systems change - , time, finances, capabilities, and legitimacy, all of which require leadership and sustained political support.

However, leadership alone is not sufficient. Based on the case studies, it is difficult to say which factors were the most influential, but it is clear that different elements have to be in place to make change possible. Moreover, systems change is a continuous process and it is essential to ensure feedback with regard to unintended consequences and unforeseen conditions during the implementation phase and beyond. Monitoring is critical to being able to decide if and when to adapt and make further changes.

Modeling, as objective and value-free as it tries to be, cannot insulate itself from value judgments and decisions. Values enter the modeling process even in the framing of questions to address and objects of study, in decisions about what gets funded, in the selection of data to be collected, and in the analytical methods to be used and the scope of the analyses. Values also play a role in deciding what scientific evidence, including modeling results, are deemed appropriate to be communicated, and how they are to be presented. Just how effective modeling studies are in informing stakeholders and policymakers depends on just how much trust exists between them. Trust in modeling increases if modelers are engaged and open with the people they want to inform and influence.

Exercises

• 1. Under what conditions might it be appropriate to apply systems modeling methods to public sector issues?

• 2. What is the purpose of developing and using modeling methods?

• 3. What is a measure of modeling success?