Complex Systems

Collectively seeing complex systems

Economists use multiple patterns of thinking to understand economies: market models, Marxist arguments, Keynesian and monetary theories, institutional analysis, and other forms. There is no more general model that unites the more specific models. Similarly, ecologists understand ecosystems through models emphasizing food webs, material and energy flows, population dynamics and species interactions, evolutionary processes, and spatial patterns. Divisions in scientific understanding also arise through the different spatial and temporal bounds scientists put on their analyses as well as different assumptions they make about how the parts of reality they are studying relate to the whole. For example, most economists, if they consider the environment at all, make very simple assumptions about ecosystems, while most ecologists, if they consider the economy at all, make very simple assumptions about economies and human behavior. Thus scientific understanding of the interactions between economic and ecological systems is very fractured and disconnected. This is why students cannot simply take a few courses in economics and a few courses in ecology and then see the full complexities of reality clearly.

Seeing the full complexity of economic and ecological interactions was, however, the goal of the Millennium Ecosystem Assessment (2005). About 1400 scientists from multiple environmental and social science disciplines from around the world worked together over a period of five years. They agreed on a general model and then assessed the available scientific literature and data to see how they inform a more systemic understanding of the changes taking place around the globe. What they discovered, of course, was that the studies undertaken using specific models did not fit into their general model. Ecologists had not included linkages to the economy in their studies; economists had not included linkages to ecosystems. Some studies were conducted at one scale, others at another. The same words were used, but with different meanings in the different studies they assessed. The quality of environmental data from monitoring varied tremendously between heavily populated and unpopulated regions as well as between rich and poor countries.

Nevertheless, a full picture was developed through discussions among ever changing combinations of scientists participating in the Millennium Ecosystem Assessment. The discussions drew on both the scientific and experiential knowledge of the scientists. Scientists participating in a group working on one part of the assessment had to connect with scientists from other groups to make sure the linkages they were beginning to understand were consistent across groups. Judgment calls were repeatedly made, contested by other participants, and fine-tuned again. In short, an understanding of the complex interactions between people and nature, how ecosystem degradation occurred, and what this meant for future peoples was constructed through a long, complex discursive process that took place in some fifty meetings over the five years, numerous email exchanges, and international conference calls as well, all in the context of writing the text of the assessment.

We understand complex system dynamics through a collective discursive learning process. Ecological economists were among the most effective participants in the arduous process of the Millennium Ecosystem Assessment because they were already accustomed to working with other scientists across the models of economics, ecology, and other disciplines. While there are still possibilities for solitary thinkers to make breakthroughs in ecological economics, some of the most effective ecological economics is accomplished through a discursive learning process with others.

This Informational Box is an excerpt from An Introduction to Ecological Economics by Robert Costanza, John H Cumberland, Herman Daly, Robert Goodland, Richard B Norgaard. ISBN: 1884015727

Glossary

Citation

Norgaard, R. (2013). Collectively seeing complex systems. Retrieved from http://www.eoearth.org/view/article/51cbed4b7896bb431f6911f1