S. Funtowicz, Centre for the Study of the Sciences and the Humanities, University of Bergen; J. Ravetz, Oxford
In relation to policy, "the environment" is particularly challenging. It includes masses of detail concerning many particular issues, which require separate analysis and management. At the same time, there are broad strategic issues, which should guide regulatory work, such as those connected with "sustainability". Nothing can be managed in a convenient isolation; issues are mutually implicated; problems extend across many scale levels of space and time; and uncertainties and value-loadings of all sorts and all degrees of severity affect data and theories alike.
This situation is a new one for policy makers. In one sense the environment is in the domain of Science: the phenomena of concern are located in the world of nature. Yet the tasks are totally different from those traditionally conceived for Western science. For that, it was a matter of conquest and control of Nature; now we must manage, accommodate and adjust. We know that we are no longer, and never really were, the "masters and possessors of Nature" that Descartes imagined for our role in the world (Descartes 1638).
To engage in these new tasks we need new intellectual tools. A picture of reality designed for controlled experimentation and abstract theory building, can be very effective with complex phenomena reduced to their simple, atomic elements. But it is not best suited for the tasks of environmental policy today. The scientific mind-set fosters expectations of regularity, simplicity and certainty in the phenomena and in our interventions. But these can inhibit the growth of our understanding of the problems and of appropriate methods to their solution. Here we shall introduce and articulate several concepts, which can provide elements of a framework to understand environmental issues. They are all new, and still evolving. There is no orthodoxy concerning their content or the conditions of their application
The leading concept is "complexity". This relates to the structure and properties of the phenomena and the issues for environmental policy. Systems that are complex are not merely complicated; by their nature they involve deep uncertainties and a plurality of legitimate perspectives. Hence the methodologies of traditional laboratory-based science are of restricted effectiveness in this new context.
The most general methodology for managing complex science-related issues is "Post-Normal Science" (Funtowicz and Ravetz 1992, 1993, Futures 1999). This focuses on aspects of problem solving that tend to be neglected in traditional accounts of scientific practice: uncertainty and value loading. It provides a coherent explanation of the need for greater participation in science-policy processes, based on the new tasks of quality assurance in these problem-areas.
Anyone trying to comprehend the problems of "the environment" might well be bewildered by their number, variety and complication. There is a natural temptation to try to reduce them to simpler, more manageable elements, as with mathematical models and computer simulations. This, after all, has been the successful programme of Western science and technology up to now. But environmental problems have features which prevent reductionist approaches from having any, but the most limited useful effect. These are what we mean when we use the term "complexity".
Complexity is a property of certain sorts of systems; it distinguishes them from those which are simple, or merely complicated. Simple systems can be captured (in theory or in practice) by a deterministic, linear causal analysis. Such are the classic scientific explanations, notably those of high-prestige fields like mathematical physics. Sometimes such a system requires more variables for its explanation or control than can be neatly managed in its theory. Then the task is accomplished by other methods; and the system is "complicated". The distinction between science and engineering, the latter occurring when more than a half-dozen variables are in play, is a good example of the distinction between simple and complicated systems.
With true complexity, we are dealing with phenomena of a different sort. There are many definitions of complexity, all overlapping, deriving from the various areas of scientific practice with, for example, ecological systems, organisms, social institutions, or the "artificial" simulations of any of them. Here we adopt a more general approach to the concept. First, we think of a "system", a collection of elements and subsystems, defined by their relations within some sort of hierarchy or hierarchies. The hierarchy may be one of inclusion and scale, as in an ecosystem with (say) a pond, its stream, the watershed, and the region, at ascending levels. Or it may be a hierarchy of function, as in an organism and its separate organs. A species and its individual members form a system with hierarchies of both inclusion and function. Environmental systems may also include human and institutional sub-systems, which are themselves systems. These latter are a very special sort of system, which we call "reflexive". In those, the elements have purposes of their own, which they may attempt to achieve independently of, or even in opposition to, their assigned functions in the hierarchy (Funtowicz and Ravetz 1997b).
First, any "system" is itself an intellectual construct, that some humans have imposed on a set of phenomena and their explanations. Sometimes it is convenient to leave the observer out of the system; but in the cases of systems with human and institutional components, this is counterproductive. For environmental systems, then, the observer and analyst are there, as embedded in their own systems, variously social, geographical and cognitive. For policy purposes, a very basic property of observed and analysed complex systems might be called "feeling the elephant", after the Indian fable of the five blind men trying to guess the object they were touching by feeling a part of an elephant. Each conceived the object after his own partial imaging process (the leg indicated a tree, the side a wall, the trunk a snake, etc); it was left to an outsider observer to visualise the whole elephant. This parable reminds us that every observer and analyst of a complex system operates with certain criteria of selection of phenomena, at a certain scale-level, and with certain built-in values and commitments. The result of their separate observations and analyses are not at all "purely subjective" or arbitrary; but none of them singly can encompass the whole system. Looking at the process as a whole, we may ask whether an awareness of their own limitations is built into their personal systematic understanding, or whether it is excluded. In the absence of such awareness, we have old-fashioned technical expertise; when analysis is enriched by its presence, we have Post-Normal Science.
We can express the point in a somewhat more systematic fashion, in terms of two key properties of complex systems. One is the presence of significant and irreducible uncertainties of various sorts in any analysis; and the other is a multiplicity of legitimate perspectives on any problem. For the uncertainty, we have a sort of "Heisenberg effect", where the acts of observation and analysis become part of the activity of the system under study, and so influence it in various ways. This is well known in reflexive social systems, through the phenomena of "moral hazard", self-fulfilling prophecies and mass panic.
But there is another cause of uncertainty, more characteristic of complex systems. This derives from the fact that any analysis (and indeed any observation) must deal with an artificial, usually truncated system. The concepts in whose terms existing data is organised will only accidentally coincide with the boundaries and structures that are relevant to a given policy issue. Thus, social and environmental statistics are usually available (if at all) in aggregations created by governments with other problems in mind; they need interpreting or massaging to make them relevant to the problem at hand. Along with their obvious, technical uncertainties resulting from the operations of data collection and aggregation, the data will have deeper, structural uncertainties, not amenable to quantitative analysis, which may actually be decisive for the quality of the information being presented.
A similar analysis yields the conclusion that there is no unique, privileged perspective on the system. The criteria for selection of data, truncation of models, and formation of theoretical constructs are value-laden, and the values are those embodied in the societal or institutional system in which the science is being done. This is not a proclamation of "relativism" or anarchy. Rather, it is a reminder that the decision process on environmental policies must include dialogue among those who have an interest in the issue and a commitment to its solution. It also suggests that the process towards a decision may be as important as the details of the decision that is finally achieved.
For an example of this plurality of perspectives, we may imagine a group of people gazing at a hillside. One of them "sees" a particular sort of forest, another an archaeological site; another a potential suburb, yet another sees a planning problem. Each uses their training to evaluate what they see, in relation to their tasks. Their perceptions are conditioned by a variety of structures, cognitive and institutional, with both explicit and tacit elements. In a policy process, their separate visions may well come into conflict, and some stakeholders may even deny the legitimacy of the commitments and the validity of the perceptions of others. Each perceives his or her own elephant, as it were. The task of the facilitator is to see those partial systems from a broader perspective, and to find or create some overlap among them all, so that there can be agreement or at least acquiescence in a policy. For those who have this integrating task, it helps to understand that this diversity and possible conflict is not an unfortunate accident that could be eliminated by better natural or social science. It is inherent to the character of the complex system that is realised in that particular hillside.
These two key properties of complex systems, radical uncertainty and plurality of legitimate perspectives, help to define the programme. They show why environmental policy can not be shaped around the idealised linear path of the gathering and then the application of scientific knowledge. Rather, the formation of policy is itself embedded as a subsystem in the total complex system of which its environmental problem is another element.
3. Post-Normal Science as a bridge between complex systems and environmental policy
The idea of a science being somehow "post-normal" conveys an air of paradox and perhaps mystery. By "normality" we mean two things. One is the picture of research science as "normally" consisting of puzzle solving within an unquestioned and unquestionable "paradigm", in the theory of T.S. Kuhn (Kuhn 1962). Another is the assumption that the policy environment is still "normal", in that such routine puzzle solving by experts provides an adequate knowledge base for policy decisions. Of course researchers and experts must do routine work on small-scale problems; the question is how the framework is set, by whom, and with whose awareness of the process. In "normality", either science or policy, the process is managed largely implicitly, and is accepted unwittingly by all who wish to join in. The great lesson of recent years is that that assumption no longer holds. We may call it a "post-modern" "rejection of grand narratives", or a green, NIMBY (Not In My Back Yard) politics. Whatever its causes, we can no longer assume the presence of this sort of "normality" of the policy process, particularly in relation to the environment.
The insight leading to Post-Normal Science is that in the sorts of issue-driven science relating to environmental debates, typically facts are uncertain, values in dispute, stakes high, and decisions urgent. Some might say that such problems should not be called "science"; but the answer could be that such problems are everywhere, and when science is (as it must be) applied to them, the conditions are anything but "normal". For the previous distinction between "hard", objective scientific facts and "soft", subjective value-judgements is now inverted. All too often, we must make hard policy decisions where our only scientific inputs are irremediably soft.
The difference between old and new conditions can be shown by the present difficulties of the classical economics approach to environmental policy. Traditionally, economics attempted to show how social goals could be best achieved by means of mechanisms operating automatically, in an essentially simple system. The "hidden hand" metaphor of Adam Smith conveyed the idea that conscious interference in the workings of the economic system would do no good and much harm; and this view has persisted from then to now. But for the achievement of sustainability, automatic mechanisms are clearly insufficient. Even when pricing rather than control is used for implementation of economic policies, the prices must be set, consciously, by some agency; and this is then a highly visible controlling hand. When externalities are uncertain and irreversible, then no one can set "ecologically correct prices" practised in actual markets or in fictitious markets (through contingent valuation or other economic techniques). There might at best be "ecologically corrected prices", set by a decision-making system. The hypotheses, theories, visions and prejudices of the policy-setting agents are then in play, sometimes quite publicly so. And the public also sees contrasting and conflicting visions among those in the policy arena, all of which are plausible and none of which admits of refutation by any other. This is a social system, which, in the terms discussed above, is truly complex, indeed reflexively complex.
In such contexts of complexity, there is a new role for natural science. The facts that are taught from textbooks in institutions are still necessary, but are no longer sufficient. For these relate to a standardised version of the natural world, frequently to the artificially pure and stable conditions of a laboratory experiment. The world as we interact with it in working for sustainability, is quite different. Those who have become accredited experts through a course of academic study, have much valuable knowledge in relation to these practical problems. But they may also need to recover from the mindset they might absorb unconsciously from their instruction. Contrary to the impression conveyed by textbooks, most problems in practice have more than one plausible answer; and many have no answer at all.
Further, in the artificial world studied in academic courses, it is strictly inconceivable that problems could be tackled and solved except by deploying the accredited expertise. Systems of management of environmental problems that do not involve science, and which cannot be immediately explained on scientific principles, are commonly dismissed as the products of blind tradition or chance. And when persons with no formal qualifications attempt to participate in the processes of innovation, evaluation or decision, their efforts are viewed with scorn or suspicion. Such attitudes do not arise from malevolence; they are inevitable products of a scientific training which presupposes and then indoctrinates the assumption that all problems are simple and scientific, to be solved on the analogy of the textbook.
It is when the textbook analogy fails, that science in the policy context must become post-normal. When facts are uncertain, values in dispute, stakes high, and decisions urgent the traditional guiding principle of research science, the goal of achievement of truth or at least of factual knowledge, must be substantially modified. In post-normal conditions, such products may be a luxury, indeed an irrelevance. Here, the guiding principle is a more robust one, that of quality.
It could well be argued that quality has always been the effective principle in practical research science, but it was largely ignored by the dominant philosophy and ideology of science. For post-normal science, quality becomes crucial, and quality refers to process at least as much as to product. It is increasingly realised in policy circles that in complex environment issues, lacking neat solutions and requiring support from all stakeholders, the quality of the decision-making process is absolutely critical for the achievement of an effective product in the decision. This new understanding applies to the scientific aspect of decision-making as much as to any other.
Post-Normal Science can be located in relation to the more traditional complementary strategies, by means of a diagram (see Figure 1). On it, we see two axes, "systems uncertainties" and "decision stakes". When both are small, we are in the realm of "normal", safe science, where expertise is fully effective. When either is medium, then the application of routine techniques is not enough; skill, judgement, sometimes even courage are required. We call this "professional consultancy", with the examples of the surgeon or the senior engineer in mind. Our modern society has depended on armies of "applied scientists" pushing forward the frontiers of knowledge and technique, with the professionals performing an aristocratic role, either as innovators or as guardians.
Of course there have always been problems that science could not solve; indeed, the great achievement of our civilisation has been to tame nature in so many ways, so that for unprecedented numbers of people, life is more safe, convenient and comfortable than could ever have been imagined in earlier times. But now we are finding that the conquest of nature is not complete. As we confront nature in its reactive state, we find extreme uncertainties in our understanding of its complex systems, uncertainties that will not be resolved by mere growth in our data-bases or computing power. And since we are all involved with managing the natural world to our personal and sectional advantage, any policy for change is bound to affect our interests. Hence in any problem-solving strategy, the decision-stakes of the various stakeholders must also be reckoned with.
This is why the diagram has two dimensions; this is an innovation for descriptions of "science", which had traditionally been assumed to be "value-free". But in any real problem of environmental management, the two dimensions are inseparable. When conclusions are not completely determined by the scientific facts, inferences will (naturally and legitimately) be conditioned by the values held by the agent. This is a necessary part of ordinary research practice; all statistical tests have values built in through the choice of numerical "confidence limits", and the management of "outlier" data calls for judgements that can sometimes approach the post-normal in their complexity. If the stakes are very high (as when an institution is seriously threatened by a policy) then a defensive policy will involve challenging every step of a scientific argument, even if the systems uncertainties are actually small. Such tactics become wrong only when they are conducted covertly, as by scientists who present themselves as impartial judges when they are actually committed advocates. There are now many initiatives, increasing in number and significance all the time, for involving wider circles of people in decision-making and implementation on environmental issues.
The contribution of all the stakeholders in cases of Post-Normal Science is not merely a matter of broader democratic participation. For these new problems are in many ways different from those of research science, professional practice, or industrial development. Each of those has its means for quality assurance of the products of the work, be they peer review, professional associations, or the market. For these new problems, quality depends on open dialogue between all those affected. This we call an "extended peer community", consisting not merely of persons with some form or other of institutional accreditation, but rather of all those with a desire to participate in the resolution of the issue. Seen out of context, such a proposal might seem to involve a dilution of the authority of science, and its dragging into the arena of politics. But we are here not talking about the traditional areas of research and industrial development; but about those where issues of quality are crucial, and traditional mechanisms of quality assurance are patently inadequate. Since this context of science is one involving policy, we might see this extension of peer communities as analogous to earlier extensions of franchise in other fields, as allowing workers to form trade unions and women to vote. In all such cases, there were prophecies of doom, which were not realised.
For the formation of environmental policy under conditions of complexity, it is hard to imagine any viable alternative to extended peer communities. They are already being created, in increasing numbers, either when the authorities cannot see a way forward, or know that without a broad base of consensus, no policies can succeed. They are called "citizens' juries", "focus groups", or "consensus conferences", or any one of a great variety of names; and their forms and powers are correspondingly varied. But they all have one important element in common: they assess the quality of policy proposals, including a scientific element, on the basis of whatever science they can master during the preparation period. And their verdicts all have some degree of moral force and hence political influence.
Along with this regulatory, evaluative function of extended peer communities, another, more intimately involved in the policy process, is springing up. Particularly at the local level, the discovery is being made, again and again, that people not only care about their environment but also can become ingenious and creative in finding practical, partly technological, ways towards its improvement. Here the quality is not merely in the verification, but also in the creation; as local people can imagine solutions and reformulate problems in ways that the accredited experts, with the best will in the world, do not find "normal" within their professional paradigms.
None can claim that the restoration of quality through extended peer communities will occur easily, and without its own sorts of errors. But in the processes of extension of peer communities through the approach of Post-Normal Science, we can see a way forward, for science as much as for the complex problems of the environment.
A sort of manual for Post-Normal Science practice has recently been produced by the UK Royal Commission on Environmental Pollution. In its 21st Report, on Setting Environmental Standards, makes a number of observations and recommendations reflecting this new understanding. Thus, on uncertainty, we have:
9.49: No satisfactory way has been devised of measuring risk to the natural environment, even in principle, let alone defining what scale of risk should be regarded as tolerable;
9.74: When environmental standards are set or other judgements made about environmental issues, decisions must be informed by an understanding of peoples’ values. …;
and on extended peer communities:
9.74 (continued): Traditional forms of consultation, while they have provided useful insights, are not an adequate method of articulating values;
and on a plurality of legitimate perspectives:
9.76: A more rigorous and wide-ranging exploration of people’s values requires discussion and debate to allow a range of viewpoints and perspectives to be considered, and individual values developed.
(UK Royal Commission on Environmental Pollution1998) Chapter 9 - Conclusions].
The inadequacies of the traditional "normal science" approach have been revealed with dramatic clarity in the episode of "mad cow" disease. For years the accredited researchers and advisors assured the British government that the risk of transfer of the infective agent to humans was not significant. They did not stress the decision-stakes involved in the official policy, in which public alarm and government expense were the main perceived dangers. Then infection of humans was confirmed, and for a brief period the government admitted that an epidemic of degenerative disease was a "non-quantifiable risk". The situation went out of control, and the revulsion of consumers threatened not only British beef, but also perhaps the entire European meat industry. At this stage there had to be a "hard" decision to be taken, on the number of cattle to be destroyed, whose basis was a very "soft" estimate of how many cattle deaths would be needed to reassure the meat-eating public. At the same time, independent critics who had been dealt with quite harshly in the past were admitted into the dialogue. Without in any way desiring such an outcome, the British Ministry of Agriculture, Forests and Fisheries had created a situation of extreme systems uncertainty, vast decision stakes, and a legitimated extended peer community.
The Post-Normal Science approach needs not be interpreted as an attack on the accredited experts, but rather as assistance. The world of "normal science" in which they were trained has its place in any scientific study of the environment, but it needs to be supplemented by awareness of the "post-normal" nature of the problems we now confront. The management of complex natural systems as if they were simple scientific exercises has brought us to our present mixture of triumph and peril. We are now witnessing the emergence of a new approach to problem-solving strategies in which the role of science, still essential, is now appreciated in its full context of the uncertainties of natural systems and the relevance of human values.
- Descartes, 1638: Discours de la Methode, Part VI.
- Futures, 1999, Special Issue: Post-Normal Science, J. R. Ravetz (ed), 31:7.
- S. O. Funtowicz and J. R. Ravetz, 1992, Three Types of Risk Assessment and the Emergence of Post-Normal Science, in Krimsky S. and Golding D. (eds) Social Theories of Risk, Westport (CN), Praeger, pp. 251-273
- S. O. Funtowicz and J. R. Ravetz, 1990: Uncertainty and Quality in Science for Policy, Kluwer Academic Publishers, Dordrecht , NL, 1990.
- S. O. Funtowicz and J. R. Ravetz, 1993: Science for the post-normal age, Futures 25:7, 739-755.
- S. O. Funtowicz and J. R. Ravetz, 1994: The worth of a songbird: ecological economics as a post-normal science, Ecological Economics 10 (1994) 197-207
- S. O. Funtowicz and J. R. Ravetz, 1997b : The Poetry of Thermodynamics, Futures, 29:9, 791-810.
- T. S. Kuhn, 1962: The Structure of the Scientific Revolutions, University of Chicago Press, Chicago, IL.
- UK Royal Commission on Environmental Pollution. Setting Environmental Standards, 21st Report, Chapter 9 - Conclusions.