Lesson 12 Notes.pdf

Post Normal Science 1. The Post-Normal Science I. Introduction - Post-Normal Science (PNS) is a new conception of the management of complex science-related issues. - It focuses on aspects of problem-solving that tend to be neglected in...

Post Normal Science 1. The Post-Normal Science I. Introduction - Post-Normal Science (PNS) is a new conception of the management of complex science-related issues. - It focuses on aspects of problem-solving that tend to be neglected in traditional accounts of scientific practice: uncertainty, value loading, & a plurality of legitimate perspectives. - PNS considers these elements as integral to science. - By their inclusion in the framing of complex issues, PNS can provide a coherent framework for extended participation in decision-making, based on the new quality assurance tasks & relies on open dialogues between all those involved. - Ecological Economics provided the initial intellectual & personal setting in which PNS evolved. - The first conference in Ecological Economics was the first major appearance of the new problem-solving framework. - The original ambition of Ecological Economics was to develop a scientifically informed movement to face the epistemological & governance challenges presented by sustainability. - Very often, the work done under the mantle of Ecological Economics is reduced to being a minor branch of mainstream economics, with all its pathologies of reductionism & pseudo-quantification. - II. New Tools for New Problems - In the sorts of issue-driven science relating to the protection of health & the environment, typically facts are uncertain, values in dispute, stakes high, & decisions urgent. - The traditional distinction between ‘hard’, objective scientific facts & ‘soft’, subjective value judgments is now inverted. - All too often, we must make hard policy decisions where our only scientific inputs are irremediably soft. - The “sound science” requirements frequently invoked as necessary for rational policy decisions may affectively conceal value-loadings that determine research conclusions & policy recommendations. - In these new circumstances, invoking ‘truth’ as the goal of science is a distraction or even a diversion from real tasks. - A more relevant & robust guiding principle is quality, a contextual property of scientific information. - A picture of reality that reduces complex phenomena to their simple, atomic elements can effectively use a scientific methodology designed for controlled experimentation, abstract theory building & full quantification. - The traditional ‘normal’ scientific mindset fosters expectations of regularity, simplicity & certainty in the phenomena & in our interventions. - But these can inhibit the growth of our understanding of the new problems & of appropriate methods for their solution. - This situation is a novel one for policymakers: In one issues of environment & sustainability are in the domain of science: the phenomena of concern are located in the world of nature. Yet the tasks are different from those traditionally conceived for Western science. For that, it was a matter of conquest & control of Nature; now we must manage, accommodate & adjust. We know that we are no longer, & never were, the “masters & possesses of Nature” that Descartes imagined for our role in the world. - These new problems are characteristic of ‘complex systems’. These are not necessarily complicated; They involve interrelated subsystems at a variety of scale levels & a variety of kinds. Thus we now know that every technology is embedded in its societal & natural contexts, & that ‘nature’ itself is shaped by its interactions with humanity. In such complex systems, there can be no single privileged point of view for measurement, analysis & evaluation. In such contexts, there is generally no ‘hidden hand’ whereby selfish individual actions automatically benefit the wider societal & natural communities. There is no substitute for morality in the good conduct of our affairs. - The phenomenon of life, society, & now the environment, can’t be captured, nor their life problems managed, by sciences assuming that the relevant systems are simple. In terms of such paradigms, they will always present anomalies & surprises. - PNS has been developed as the appropriate methodology for integrating with complex natural & social systems. - The difference between old & new conditions can be shown by the present difficulties of the classical economies approach to environmental policy; Traditionally, economics attempted to show how social goals could be best achieved through mechanisms operating automatically, in an essentially simple system. The “hidden hand” metaphor of Adam Smith conveyed the idea that a conscious interface in the workings of the economic system would do no good & much harm. However, for the achievement of sustainability, automatic mechanisms are insufficient. Even when pricing rather than control is used for the implementation of economic policies, the prices must be set, consciously, by some agency; & this is then a highly visible controlling hand. When externalities are uncertain & irreversible, then it is possible to set “ecologically correct prices” to be utilized in actual or fictitious markets. - The issue is not whether it is only the marketplace that can determine economic value, for economists have long debated other means of valuation. - The post-normal perspective challenges the assumption that in any dialogue, all valuations or ‘numeraries’ should be reduced to a single, one-dimensional standard. - Contrary to the impression that the textbooks convey, in practice, most problems have more than one plausible answer, & many have no well-defined scientific answer at all. - In the artificial world studied in academic science courses, it is strictly inconceivable that science-related problems could be tackled & solved except by deploying accredited expertise. - Practical techniques that cannot be explained in principle by accepted science are commonly dismissed as the products of dogmatic tradition or blind chance. - When persons with no formal qualifications attempt to participate in the process of innovation, evaluation, or decision-making, their efforts have tended to be viewed with suspicion or scorn. - PNS provides a means for correcting this sort of mindset, which has now become quite counterproductive, both for the legitimacy & for the quality of science-related policy processes. III. Science for the Post-Normal Age - As a theory, PNS links epistemology & governance, for its origins lie in the relations between those two domains. Its authors were concerned that the sciences devoted to solving health & environmental problems are radically different from those that are instrumental in creating them. In comparison to those traditional sciences, the policy-relevant sciences have enjoyed less prestige & funding, are less mature scientifically, & are more subject to external influences & constraints. By the criteria of the traditional philosophy of science, their results fail to attain the status of ‘sound science’. PNS provides a response to these crises of science & philosophy, by bringing ‘facts’ & ‘values’ into a unified conception of problem-solving in these areas, & by replacing ‘truth’ by ‘quality’ as its core evaluative concept. Its principle of the plurality of legitimate perspectives on any problem leads to a focus on dialogue, & on mutual respect & learning, wherever possible. - PNS comprises those inquiries that occur at the interfaces of science & policy where uncertainties & value-loadings are critical: It can be analyzed as a ‘policy cycle’ including policies, priorities, persons, procedures, products, & post-normal assessments; it also extends to the ‘downstream’ phases of implementation & monitoring. Depending on the context, the task may be more like policy-related research, science-related decision, or creating technical-social innovation. The distinctions are never absolute, as the whole policy process is a complex system with interrelated natural, technical & societal elements. - PNS can be located concerning the more traditional problem-solving strategies through a diagram. On it, we see two axes, ‘system uncertainties’ & ‘decision stakes’. When both aspects are small, we are in the realm of ‘normal’, safe Applied Science, where expertise is fully effective. When either is medium, then the application of routine techniques is not enough; skill, judgment, & courage are required. This is Professional Consultancy, for example, the surgeon or the senior engineer. In such cases, the creative element is more an exercise in design than the discovery of facts. In recent years, we have learned that even the skills of professionals are not always adequate for the solution of science-related policy issues. When risks can’t be quantified, or when possible damage is irreversible, then we are out of the range of competence of traditional sorts of expertise & traditional problem-solving methodologies. This situation is represented on the diagram as the outer band, that of PNS. - The term ‘post-normal’ provides a contrast to two sorts of ‘normality’. One is the picture of research science as ‘normally’ consisting of puzzle solving within the framework of an unquestioned & unquestionable ‘paradigm’, in the theory of Kuhn (1962). Another is the assumption that the policy context is still ‘normal’, in that such routine puzzle-solving by experts provides an adequate knowledge base for decision-making. - The management of systems uncertainties through the involvement of decision stakes occurs even in routine science. Whatever the statistical test, there will always be errors: no test can completely avoid being either too selective (rejecting genuine correlations) or too sensitive (accepting spurious ones). A balance must therefore be struck between the error-costs of excess selectivity and those of excess sensitivity and the balance depends on the policy framework of the test. A very selective test designed around avoiding ‘false positives’ could exclude potentially important information, which could then remain permanently unknown. The well-known ‘confidence level’ expresses this value-driven choice. It is ‘normally’ not assigned by researchers; rather they automatically apply the level that is standard for their field. - All these considerations have been articulated in statistical theory, in terms of the ‘null hypothesis’ around which tests are designed, and the errors of its rejection when true (Type I), or acceptance when false (Type II). These correspond to errors of excess sensitivity, & of excess selectivity, respectively. These are the stuff or routine work in ‘normal science’. Statistical theory tends to undervalue another sort of error, ironically called Type III, when the whole artificial exercise has no relation to the real issue at stake. Type III errors are a characteristic pitfall when the ‘normal science’ approach is deployed in post-normal situations. Modeling exercises are prone to this error, as the gap between the available data & a manageable model on one hand, & the real policy situation on the other, can’t be bridged. All conventional economics outside of the most narrowly empirical sort if particularly prone to Type III error. - With the post-normal perspective, we can see how uncertain data & inconclusive arguments can easily yield vacuous results. But with awareness & management of uncertainties & value-loadings, economic analysis can be a strong & indispensable tool in policy dialogues.  - When a problem is recognized as post-normal, even the routine research exercises take on a new character. The value-loadings & uncertainties are no longer managed automatically or unselfconsciously. As they may be critical to the quality of the product in the policy context, they are the object of critical scrutiny by researchers themselves as well as by their peers, ordinary, & extended. Thus, ‘normal science’ itself becomes ‘post-normal’, & is thereby liberated from the fetters of its traditional unreflective, dogmatic style. IV. Extensions of the Peer Communities - The contribution of all the stakeholders in cases of PNS is not merely a matter of broader democratic participation. These new problems are in many ways different from those of research science, professional practice, or industrial development. Each of those has established its own means of quality assurance for the products of the work. But for these new problems, the maintenance of 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. - Since this context of science is one involving policy, we might see this extension of peer communities as analogous to earlier extensions of the franchise in other fields, such as women’s suffrage & trade union rights. - With PNS we can guide the extension of the accountability of governments to include the institutions involved in the governance of science & technology. - Extended peer communities are made when the authorities can’t see a way forward or when they know that without a broad base of consensus, no policy can succeed. - They are called ‘citizens’ juries’, ‘focus groups’, ‘consensus conferences’, or any other one of a great variety of other names. - Forms & powers are varied but they all assess the quality of policy proposals, including a scientific element. - Their verdicts all have some degree of moral force & hence political influence.  - These extended peer communities will not necessarily be passive recipients of the materials provided by experts. They will also possess, or create, their own ‘extended facts’. These may include craft wisdom & community knowledge of places & their histories, as well as anecdotal evidence, neighborhood surveys, investigative journalism, & leaked documents. This activity is most important in the phases of policy formation, implementation & monitoring of policies. Participants can enhance the quality of the problem-solving processes themselves. - At the local level, people not only care about their own environment but can also be quite ingenious & creative in finding practical means for its improvement, integrating the social & technological aspects. - Local people can imagine solutions & reformulate problems in ways that the experts, with the best will in the world, do not find ‘normal’. - PNS provides a rationale whereby traditional knowledge (i.e. agriculture & healing) is utilized, harmonized, enhanced, & validated anew. - A possible bridge between post-normal science & practical evaluation tools may be the concept of social multi-criteria evaluation. Social multi-criteria evaluation puts its emphasis on the transparency issue; The main idea is that the results of an evaluation exercise depend on the way a given policy problem is structured & thus the assumptions used, the ethical positions taken, & the interests & values considered have to be made clear. In this framework, mathematical models still play an important but less ambitious role than traditional optimization, which is one of guaranteeing consistency between assumptions used & results obtained. 2. Main Elements of Post-Normal Science - PNS is a problem-solving framework developed by Silvio Funtowicz & Jerome Ravetz. I. The Scientific Management of Uncertainty & of Quality - In the issue-driven research of PNS, the characteristic uncertainties are large, complex, & less well understood than in matured quantitative sciences. - The management of uncertainties should rely on explicit guidelines & credible set of procedures transparent to all actors involved in a policy process.  - The principle quality, understood as a contextual property of scientific information, is central to the management of uncertainty in PNS. - It allows for tackling the irreducible uncertainty & ethical complexity that are central to the resolution of complex issues. - PNS calls for the development of new norms of evidence & discourse, where knowledge is extended to peer communities for quality assurance purposes. - One of the basic principles is the inclusion of laypersons, i.e. citizens & non-experts in the assessment of quality. - PNS recognizes that all those with a desire & commitment to participate in the resolution of the relevant issues are expected to enrich the nature of policy debates involving science. II. The Multiplicity of Perspectives & Commitments - As policy processes become dialogue, knowledge is ‘democratized’, encompassing the diversity of legitimate perspectives & commitments. - The guiding principle in the dialogue on a PNS issue is quality rather than ‘truth’. - Most complex issues entail a plurality of actors & multiple dimensions of analysis that are difficult to condense in a single scale of measurement. - It is accepted that there is no sharp distinction between ‘expert’ & ‘lay’ constituencies. - Both types are needed to enrich the comprehension of the whole. - Extending decision processes requires the creation of conditions to identify, involve, & engage the relevant community, thus entering the realm of participatory processes. - The contribution of social actors is understood not merely as a matter of broadening participatory democracy but as a legitimate input to the co-production of knowledge. III. The Intellectual & Social Structures that reflect Problem-solving Activities - Unlike previous models of science, PNS does not attempt to define unifying conceptual foundations or to create closed boundaries in a field of research. - The unity in PNS is primarily derived from the ethical commitment to the resolution of an issue rather than from a shared knowledge base. - This commitment will take social actors through appropriate problem-solving activities & dialogues. 3. Post-Normal Science in Action - Utopian Rationalist Also known as the technocratic model of science. Entails that the policy-making process should assimilate scientific information to a maximum extent. May be appropriate in those rare situations of consensus on both knowledge & values that pertain to a policy problem. It reflects the notion of science speaking ‘value-free’ truth to political power that gained institutional currency in the 19th century. - Pragmatic Rationalist (PNS) Also known as a ‘democratic’ ideal of science. It accepts, within limits, the inevitability of political ingredients in science advice. Considers technocratic science advising to be mistaken for many policy problems. Uncertainty & value controversy ask for science to contribute to political debate by representing different legitimate perspectives on policy problems. Recognize & identify the controversies, uncertainties, & ambiguities to open up discussion & stimulate the process of deliberative decision-making. - The science-policy-society interface (PNS) is not static but instead seen as a highly dynamic process in which all kinds of linkages are continuously being formed & broken. - PNS is often misunderstood as something that replaces normal science. It should instead be seen as a societal problem-solving strategy that partly draws on normal science but emphasizes that for addressing complex policy problems more is needed to shape the science-policy-society interface. - Facts are still necessary but no longer sufficient. Normal Science must be modified to fit the post-normal principle to achieve the goal of quality. - Deficit View Uncertainty is considered a deficit of available knowledge. Uncertainty is seen as a temporary problem that will disappear if more research is done. Management of uncertainty equals a reduction of uncertainty. There is a strong belief that science can provide certainty. Uncertainty is treated as if it were a fact that needs to be discovered & correctly quantified. One tendency seen is the production of ever more complex & detailed models because precise calculations are key to truth.  A pitfall of this paradigm is that a false certainty is created because the numbers obtained from these models can suggest more knowledge than there is. - Evidence Evaluation View Considers uncertainty to be a problematic lack of equivocalness. The solution proposed is a comparative evaluation of individual research results, focused on building scientific consensus. The focus shifts from establishing certainty to evaluation of evidence to establish gradations of certainty. Focuses on generating robust conclusions & widely shared interpretations of the available limited knowledge. A pitfall of this paradigm can be that matters on which no consensus can be reached continue to receive too little attention, while, in fact, this dissension is often highly policy-relevant. Especially weak signals of early warning of new risks are likely to be overlooked or under-addressed here because it often is impossible to reach a consensus interpretation on such issues. - Post-Normal View In further contrast to the deficit view, it sees uncertainties as more than a number. It stresses that uncertainty also results from the new ways by which knowledge on complex policy issues is produced. This implies that if knowledge is produced that is conditioned on the unknown validity of assumptions, uncertainty is unavoidably coproduced. Acknowledges that not all uncertainties can be quantified & that in complex issues, unquantifiable uncertainties can well be more relevant & salient than the part of uncertainty for which we have enough knowledge to quantity it in some reliable way. It calls for an approach that openly deals with deeper dimensions of uncertainty. -