Ecological economists are open to learning and connecting material that is typically looked at separately within particular disciplines. Indeed, ecological economists need to be open to learning across divisions within both economics and ecology, for within each there are different schools of thought with different methods for understanding systems. This entry addresses critical issues with respect to developing and accessing knowledge across disciplines and schools.
Epistemology is the branch of philosophy concerned with how we know. It identifies key issues such as the problem of induction, the question of whether we can ever induce from a series of similar observations that the next one will be the same. Epistemological problems have no perfect solutions, but problems can be dealt with more constructively. Simply realizing that all of our ways of knowing have weaknesses can reduce unproductive arguments.
Unity of Science
Our understanding of transdisciplinary knowledge is dominated by our belief in the unity of science. Since scholars in the disciplines are studying different aspects of the same reality, as they advance, accumulating truth and weeding out falsehoods, their understanding must merge into a unified understanding of the whole. Ecological economics is an effort to hasten a particular merger by pushing ecological understanding towards economics, and economic understanding towards ecology. There are, however, at least five serious problems with this unity-of-science view of ecological economics.
First, perhaps the multiple schools within as well as the disciplines of ecology and economics themselves are already unified, or at least not incongruent, but their congruence is hidden by the particular languages used by the different schools of thought. At first, this seems like a problem that could be easily solved by unifying the languages, and several scholars at the beginning of the 20th century argued for this for science as a whole. Yet anyone who has lived in different cultures knows that translations are never as precise as we require science to be. So we need to learn multiple languages, yet there are far more languages than any one of us can possibly learn. One might become fairly adept at the languages of economics and ecology, but still be a rank amateur in ethics and the physical sciences. Even if someone claimed to have learned all of the languages, how would the rest of us know he or she had learned them correctly? For this reason, ecological economics is a group effort entailing shared learning, cross checking and correction, and yet still considerable trust. And even if ecologists and economists are successful at learning each other’s languages and cultures, other scientists and society as a whole will more likely trust our collective knowledge if they too are participating in a shared learning system.
Second, there are good reasons to believe that ecology and economics do have major gaps between them so that it is not simply a matter of learning languages but of filling in key spaces. We know, for example, that economists have not made linkages to ecological systems, nor vice versa, and there is little reason to believe that the systems models of each discipline can simply be joined. Efforts to date have made one or two linkages at most, and this may be the best we can do until more of the gaps are filled.
Third, we also know that economics has evolved around the assumption, for example, that there are no long-run resource limits, an assumption that contradicts ecological understanding. Ecologists, on the other hand, have mostly tried to study systems apart from people from which they try to deduce how systems with people should be designed, the naturalistic fallacy. This means that what passes for common sense in the realm of each discipline is nonsense or only true under particular conditions in the other. Unity is certainly not possible until what appears from each of the disciplinary perspectives to be misinformation in the other discipline based on false or limited assumptions is cleared up.
Fourth, there are good reasons to believe that the sciences will never unify. If different models are truly different, mechanistic and evolutionary models, for example, then they are simply different ways of looking at systems that yield different insights. Unity between them is not possible. We see mechanistic and evolutionary approaches emphasized within different schools across multiple disciplines. This suggests multiple partial mergers are possible.
Fifth, there are serious limits to our understanding of objectivity that intertwine with the disciplines and models used. Seeing systems as mechanistic leads one to stress predictability and efficiency. Seeing systems as evolutionary leads one to stress emergence and diversity. Different models favor different visions for our future, and these visions favor particular human values. The field of conservation biology is not simply the science of biological diversity but a collection of scientists arguing that our future will be better if we learn to live with nature in a manner that sustains diversity. Much of the knowledge among conservation biologists spans into areas shared, and too often exclusively claimed, by ethnobiologists, development economists, and environmental ethicists, for example. Similarly, highway, nuclear, and genetic engineers have visions of our future that favor different values. These engineering visions and values are inherently favored by the approaches of the sciences behind them. Learning across the disciplines necessarily entails acknowledging and working with the complementarities and contradictions of these alternative visions and values.
In spite of these five problems, belief in the unity of science prevails. Our environmental agencies, divided along disciplinary and professional lines, are expected to coherently manage the whole environmental system. When environmental problems arise that fall between disciplines, research funds are allocated to fill the gap. In fact, however, we also learn across the disciplines in spite of the disunity of the sciences. There are three additional methods by which transdisciplinary knowledge arises: integrated assessment, heuristic models, and distributed learning networks. The five problems associated with our hope for the unity of the sciences reappear in somewhat different forms in each of the other approaches.
The approach of integrated assessment (IA) entails the development of a new professional field of experts trained to integrate the models of specific disciplines into a more comprehensive model. The premise of IA is that larger systems can be understood as being made up of subsystems that can be taken from the disciplines as closed, independent modules and then linked at their boundaries. Furthermore, the linkages between the models result in feedbacks on the models from one time period to the next. The original subsystem models, because they are largely coming from the disciplines, have been developed pretty much independently of each other and may run at different spatial and temporal scales. Both to get the modules to work together and because of data limitations, IA experts may have to modify the original models, build simpler models that incorporate critical elements, or provide an interlinking model that aggregates output across space and time. Integration requires that one or more critical outputs of each module must be inputs to one or more other modules. The flows between the modules must necessarily be quantitative in nature.
Integrated assessment faces the five challenges identified for the unity of science and is criticized for not addressing them adequately. Those trained in integrated assessment are accused of being insensitive to the cultural meanings of disciplinary concepts, ignoring gaps between the disciplines or filling them in on their own, ignoring the contradictions between knowledges, assembling mechanistic models while not paying sufficient attention to other models, etc. No doubt, all of these charges are true, but they are also inherent epistemological problems without clear solutions. IA practitioners are in an awkward position of taking material from and speaking for multiple disciplines while not really being inside of any of them. Those who object to IA, however, are typically blind to how they think knowledge can be assembled across the disciplines. The five challenges, however, can be better acknowledged and worked with more explicitly by IA practitioners. IA, on the other hand, crossbred with and evolved from cost-benefit analysis and environmental assessment. Thus, IA practitioners are typically closer to the policy process where clear, simple answers, unladen by philosophical sophistication, are being demanded.
We tend to think of whole systems models as building up from models of subsystems. Ecologists have tried to construct ecosystem models, for example, by building up from simple, two species, predator-prey models. We forget, however, that predator-prey models do not contain the knowledge of organismal biologists, microbiologists, chemists, and physicists. Similarly, models of interacting ecological and economic systems do not have to build up from all of the details known to economists and ecologists. Rather, those interrelations that are critical to the question being asked can be built into a simple, exploratory (heuristic) model. The Nordhaus model of climate change, for example, combines critical interactions between the economic, atmospheric, and marine systems in a mere 13 equations. This model has been both very effective at influencing policy and the subject of much criticism because it is very easy to use and very easy to see why it is a little too simple. It has, however, helped focus attention on the choice of interest rate and how benefits and costs are distributed, details that are far more difficult to see and untangle in most integrated assessment models of climate change.
Distributed Learning Networks
Scientists have coped with the disunity of science from the beginning. We argue in this section that a key approach to coping has been through a process of distributed learning among scientists using different approaches working in different fields. In some sense, this is a “flip side” of why the unity of science has failed us. Yet, if we can rationalize and improve upon the social processes we have used to cope with disunity, science may be able to better approach transdisciplinary understanding. An example is illustrative:
The Intergovernmental Panel on Climate Change (IPCC) is a large group of scientists in close communication. The big computer models of global climate do not link the knowledge of the atmospheric physicist working at the scale of milliseconds and microns to the knowledge of the evolutionary biologist working at the scale of millennia and continents. As an interactive group of researchers, however, what one learns does affect how others design their subsequent research projects. Think of them as a network of interactive researchers, or a distributed learning system. Among them they have systematic knowledge that is probably improving as they learn with each other. They design their research in the context of being a member of a distributed learning system, yet no one of them understands everything. They have included mergers between parts of the disciplines, integrated assessment, and heuristic modeling in their overall effort, so this is the most inclusive of the approaches. Collectively, they are the best-informed people we have on the subject, but they do not have a coherent model that predicts specific futures associated with specific policies. What they know is affected by the social process, the formal and informal communication channels set up by the IPCC, half a dozen other key institutions, as well as the scientific journals through which they most formally communicate.
While our systemic understanding has long incorporated distributed learning network processes, there has been relatively little work on how different social structures, communication rules, and judgment processes hinder or help scientists reach improved collective understanding. Distributed learning networks address many of the five challenges to the unity of science, but do so in a collective process that ultimately substitutes judgment for definitive answers. A key problem of this approach is that the dominant expectation of how policy follows from science does not rest on collective judgment. The dominant expectation is that there is a single coherent model, or perhaps multiple complementary models, that predict alternative futures reasonably accurately.
Distributed learning networks and collective judgments seem too subjective to be scientific processes. Some suggest that alternative approaches to designing distributed learning networks and drawing on their collective judgment should be explored and assessed to see which work better under different circumstances.
There appear to be four general approaches to learning across ecology and economics: merging based in the unity of science, integrated assessment, heuristic modeling, and distributed learning networks. Five challenges are identified in the context of why the unity-of-science approach is not meeting our expectations. These five challenges take somewhat different forms for each of the four approaches. Epistemological problems do not have solutions; they can only be acknowledged and worked with constructively. Knowing that they exist and cannot be avoided, we can reduce unfounded claims and expectations.
Unity of Science: The problems of complexity and understanding of whole systems will diminish as the disciplines expand their understanding and eventually merge into a coherent explanation of reality.
Integrated Assessment: We can construct systematic ways and professional standards for integrating models and information developed in the disciplines into larger models that generate information about larger complex systems over appropriate time periods. The problems of complexity will go away as we develop and put our trust in professionals trained to undertake integrated assessment modeling.
Heuristic Models: All models ignore great complexity at smaller/lower/shorter scales. All problems can be modeled fairly simply on the scales at which we need to understand them. The problems of complexity and understanding whole systems will be reduced as a new breed of scientists arises that develops and uses models, and develops data sets, at appropriate scales.
Distributed Learning Networks: While methodological pluralism is accepted, different scientists using different models and languages focusing on different parts of the system can communicate their findings to each other, affect each other’s research design and their interpretation of results, and make collective assessments of the whole system. The problems of complexity will be reduced by deliberately designing distributed learning networks to make them work better and learning how to draw upon, and trust, collective assessments from the networks.
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- Galison, Peter and David J. Stump (eds). 1996. The Disunity of Science: Boundaries, Context, and Power. Stanford University Press, Stanford.
- Kitcher, Philip. 2001. Science, Truth, and Democracy. Oxford University Press, Oxford.
- Norgaard, Richard B., 1989. The Case for Methodological Pluralism. Ecological Economics, 1:37-57.
- Norgaard, Richard B., 2001. The Improvisation of Discordant Knowledges. In: Cutler J. Cleveland, Robert Costanza and David I. Stern (Editors). The Nature of Economics and the Economics of Nature. Edward Elgar, Cheltenham, U.K.