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1
Introduction
Human economic activity now influences ecological systems not only at the local but also at the global level. Problems like climate change, induced by our carbon emissions, are encroaching on the natural environment to the extent that it is now impossible to find pristine systems. Ecosystem services can be lost owing to human activities, like agricultural expansion, that depend on these same services (Costanza et al. 1997; Daily 1997). Humans are a part of the ecological system. But we are currently capable of irreversibly damaging it.
Our current environmental problems are due in part to our inability to make many small, consistent decisions that lead to a broad, sustainable outcome. The world is becoming less forgiving of our mistakes as it becomes relatively ″full″ with human activity (Daly and Cobb 1989). In a full world, the interlinkages between human and natural systems are stronger, more pervasive, and more complex. In the past, when the world was relatively ″empty,″ we could address these environmental problems in a linear, compartmentalized manner. In today’s full world this approach is no longer adequate.
The problems of the environment are particularly difficult because their complexity, interrelatedness, and dynamic behavior are beyond the cognitive capacity of most humans to fully understand and manage. Humans respond to a strong signal that something is wrong but have more trouble stopping a negative trend that evolves with a slow pace and which involves many interlinked variables that are hard to track. The result is that we are gradually destroying the environment through our shortsighted, ill-coordinated management of it. Some refer to this phenomenon as ″death by a thousand cuts.″
Policymakers usually serve relatively short terms compared with the time scale on which structural environmental changes may occur. The short-term political orientation may continue to exist because of the lack of a coherent systems perspective in which actively involved stakeholders hold policymakers and politicians accountable. Trade-offs between short-term, usually economic, interests and long-term benefits—as well as the risk of long-term unintended and undesirable effects—of a policy are difficult to make and involve many stakeholders.
People occupied with environmental issues invariably face very challenging problems concerning the complex interactions between human systems and ecosystems (Costanza and Jorgensen 2002). For example, tourism, fisheries, and salt making are important economic activities in the Ria Formosa, a coastal wetland in the south of Portugal. These economic activities are affecting the ecosystem they are also depending on for their prosperity. In addition, these economic activities are interdependent. Tourists appreciate fish, and they enjoy the atmosphere a traditional fisheries fleet creates in the harbor of a town. However, the pollution caused by tourism to some degree affects the health of the fish stock. The cultivation of fish farms competes with traditional fisheries on the market and in addition competes for space with the local salt industry.
There is no single, simple answer available or a single discipline capable of adequately addressing such problems. Equilibrium-centered, command-and-control strategies are not necessarily well suited for the challenges posed by today’s full world. Equilibrium-centered strategies are based on the assumption that a steady state for a system exists and that all we have to do is to guide a system there with appropriate policies, while command-and-control strategies assume that a system will actually respond on command as predicted.
Many environmental professionals, industry leaders, and academics have to deal with existing or anticipated conflicts over the alternative use of resources, their economic implications, and the distribution of social impacts over the medium and long term. Economic prosperity is just as much, if not more, a part of many people’s preferred visions of the future as is the sustenance of natural systems.
All of these factors make the environmental manager’s job more difficult, to say the least. In recent years, complexity has become more readily accepted as an intrinsic part of environmental management and policymaking. For example, dealing with complexity means that an increasing number of actors are recognized as being involved in the problem. It suddenly appears as if everyone is affected by a complex problem. Pursued to the extreme, this realization can paralyze efforts to improve a situation. In practice, environmental practitioners try to strike a balance between the philosophical ideal of encompassing complexity and what is currently feasible to improve a situation. Therefore, dealing with complexity in environmental management and policymaking manifests itself in a variety of ways reflecting different levels of comfort with complexity.
Finally, in our complex world, uncertainty is an unavoidable and often uncomfortable companion (van Asselt 2000). Uncertainty triggers the sense that something may go wrong because not all the aspects of a problem are under control or the outcome surpasses a limited time span. Uncertainty and associated risk also depend on the perspective of stakeholders (consider the perspective of a real estate developer versus that of an environmental activist in the Upper Fox case study in Chapter 5) and their personal attitudes (risk-adverse or risk-seeking).
The purpose of this book is to introduce professionals (and those who are training to become professionals) to a new tool that can help society to cope with this complexity and make sound decisions about the environment. It involves modeling, or simulation, which may seem daunting to some readers but which is in fact both easily understandable and necessary if we are to confront our environmental problems effectively. Models offer us the ability to expand our mental capacity in ways that enable us to better understand ecosystems and the implications of our many small management and policy decisions as they relate to ecosystem and human health.
Building a simulation model helps us to perceive interconnections and to connect past, present, and future. The simulation models come from a field known as system dynamics, which is concerned with the understanding of how systems change over time. The behavior of systems is studied through identifying a minimum of building blocks that can explain the bulk of the behavior. Feedback loops and time lags characterize the relationships among the building blocks of a system dynamics model. Building system dynamic computer models helps us to try to understand systematically these time delays, nonlinearities, and feedbacks.
Mediated modeling is based on system dynamics thinking but emphasizes the interactive involvement of affected stakeholders in the learning process about the complex system they are in. It allows a group of stakeholders to understand how seemingly small decisions may spiral a system onto an undesirable course. Such understanding provides opportunities to jointly design strategies to abate the negative spiral or to curb a trend into a more positive one.
Mediated modeling can also allow policymakers and other stakeholders to see the consequences of their actions over longer time scales. Not only are the time and space scales on which many environmental changes occur not in sync with institutional structures and political cycles but the incentives are lacking to close this gap (Costanza et al. 2001). The lack of appropriately structured institutions is not surprising considering the way the human mind works.
Bounded Rationality
Extensive research shows that human rationality is limited, or bounded, and this limitation can create persistent judgmental biases and systematic errors (Simon 1948). ″Bounded rationality″ means we are limited by a lack of full information and cannot process all that we have. We therefore end up with suboptimal solutions (Simon 1948, 1979, 1985; Kahnemann and Tversky 1974; Kahnemann et al. 1982; Hogarth 1987; Ehrlich 2000). The human mind works in a rather short-term manner and favors linear relationships over a dynamic systems perspective (Weiner 1985; Shoham 1990; Ehrlich 2000, 2001). Personal positions are often static and defended on the basis of convictions and perceptions, and people select information that reinforces their initial position (Bakken et al. 1994).
A linear relationship can be pictured as a situation in which A leads to B, which leads directly to C. A complex system–based relationship can be pictured as a situation in which A, B, and C are interrelated to the extent that C, in turn, may have an impact on A (see Figure 1.1). Such an interrelationship implies that there are dynamic or time-related issues at work. For example, A may influence B relatively quickly, but in return, B may influence A with a time lag. In addition, a feedback of a third element, C, may cause the pattern to change again. Humans have trouble taking time lags and feedback loops into account in a dynamic manner.
In practice, decision making and the resulting outcomes often lag in time and space. The consequences of decision making are only weakly connected to current reality. Impacts of decision making are seldom satisfactorily monitored. Negative impacts are rarely acknowledged. All these factors prevent effective learning and reinforce reliance on (politically motivated) beliefs rather than facts. The lack of understanding of the dynamics within and among systems and the participation of affected stakeholders cause many policy decisions to have unintended, potentially disastrous consequences.
Figure 1.1.
Linear versus system dynamic thinking
Decision Making
As an artifact of our limitations in dealing with dynamic systems, decision making is often compartmentalized and fragmented into economic, environmental, and sociocultural spheres. The bigger, integrated picture gets lost, and trade-offs may not be obvious. Even though the following key elements to achieve sustainable development may not be sufficient, they are necessary to include in the decision-making process:
- Integration of insights from ecology, economics, and social sciences (Costanza 1993)
- Integration of impacts at different scales
- Effective stakeholder participation at the appropriate scale (Chambers 1997)
- A linked understanding of past, present, and future relationships (Senge 1990)
These key elements for sustainable development are often not deliberately addressed, and as a consequence, policymaking (and the underlying studies and decisions leading up to policymaking) is often accomplished through compartmentalization. The values assigned to environmental and economic activities may have different conceptual and empirical bases. The weighing of one against the other occurs at stages in the decision process at which a competitive win-lose situation results. It may be beneficial for some aspects of policymaking not to separate the questions of interest based on ecology and economics as different disciplines. By integrating ecology and economics (and any other relevant aspects) from the start of a decision-making process, the system can be thought of as a more multidimensional, dynamic, interactive one. Making the overall goals clear and understanding the trade-offs can harmonize the value basis up front.
If ecological and economic aspects of a system under study are included in a quantitative model structure, different units have to be converted for these aspects to relate to each other in a meaningful way. For example, while we are used to expressing economic values in monetary terms, the ecological values often lose out because it is more difficult to express them in monetary terms. Costanza, d’Arge, et al. (1997) argue that values of ecological services can be made more tangible and in some cases could help to get the ecological services on the monetary map.
Mediated modeling provides a structured process based on dynamic systems thinking to include the most important aspects of a problem in a coherent and simple but elegant simulation model.
Knowledge Accumulation by Experts
Policymaking is not the only field in which fragmentation and compartmentalization exist. Academia has traditionally embraced a logical positivist perspective based on the Greek ideal for understanding the world. These ideas still strongly influence the modern course of science. The logical positivist philosophy asserts that one ″right″ answer exists. In this view, the ″right″ answer will emerge if one keeps looking in increasing detail. Scientists are supposed to be the objective unbiased searchers for this answer. Two significant outcomes occur owing to this philosophy. First, it has led to the compartmentalization of science, which has produced many disciplines and well-guarded, hard-to-cross disciplinary boundaries. Second, in today’s educational system people are trained predominantly in analysis—the art of taking problems apart to study the parts in ever-increasing detail. Very little attention is given to synthesis—the art of putting the pieces back together into a well-functioning apparatus.
As Connie Ozawa elegantly describes in Recasting Science (1991), scientists are often used by different parties to drum up support or discredit claims when conflicts have made it to the courthouse. Mediated modeling sees a role for scientists as one of the stakeholders. During a mediated modeling process scientists learn from other stakeholders what the practical questions are. Scientists can provide pieces of informat...
