Environmental sustainability, complex systems, and the disruptive imagination

Abstract

It is now widely recognized that a scientific approach to the environment requires recognition that all things environmental are embedded in complex systems. While it is theoretically possible to study elements of environmental systems in isolation, the knowledge derived will be of limited value for informing real decisions made in messy, context-rich situations. In environmental systems, it is not typical that all other variables remain equal. Thus, managers, decision-makers, and policy-makers are constrained from adjusting just one variable at a time. The interconnected, interdependent character of real-world environmental systems ensures that simplification risks masking the unintended consequences of decisions. Subsequently, new problems will emerge that require more study, increasingly ambitious interventions, and create further unintended consequences. An antidote to a spiral of ever-increasing effort yielding increasingly frustrating outcomes might be systems thinking. One goal of systems theory and methodology is to understand interconnectedness and interdependencies at an appropriately holistic scale, remaining sensitive to the limits of knowledge and willing to adapt present and future decisions to new information. Nonetheless, there is no single template that constitutes a systems approach. The degree to which systems analysts, decision-makers, and scientists can combine reductionist perspectives with holistic perspectives depends on the skill and experience with which methods or tools are employed. The paragraphs below describe several of these, in order from most narrow and quantitative to most broad and necessarily qualitative. Logical problem-solving requires analysts to pose wellformulated problems that typically yield one best right answer. For example, in systems optimization, the goal to identify a set of design variables that result in minimization or maximization of an objective (or merit) function that represents the values of decision-makers. Famous problems in transportation and economics are amenable to systems optimization, such as planning a bus route that picks up and delivers all passengers while driving the fewest possible miles. Taken collectively, these problems make up a field of study called operations research. When a systems optimization problem is not reducible to a single objective, it may be amenable to multi-criteria decision analysis (MCDA). Nonetheless, the optimization approach typically makes strong assumptions about the definition of boundaries and state of knowledge of the system, including the relationships among the state variables of the system. Typically, systems optimization is good for managing complicated systems from which reliable data and operational models can be extracted (such as air traffic control, supply chains, or manufacturing); however, it is not often appropriate for complex systems. The latter includes feedback loops that may operate on time scales that render it not useful to conceive of optimality. Some systems can be both simple and complex, in the T. P. Seager (&) Arizona State University, Glendale, AZ, USA e-mail: Thomas.Seager@asu.edu