Whatever we do is based in some way on an underlying set of beliefs or assumptions about the world around us. Often these assumptions are inter- nalised and implicit in the way we think and act and may unconsciously introduce biases into our research. The ability to think about potential alter- native paradigms means being conscious and critical about the fundamental assumptions and philosophies that shape the way we approach problems.
Therefore, one way to show explicitly the paradigm that shapes our actions is to identify the assumptions that underpin it. Table 5.2 identifies the assump- tions and features of two paradigms. The first is that commonly displayed in the twentieth century, as was described in Chapter 4, and the second is an alternative post-normal science paradigm proposed for the twenty-first century (Checkland, 1984; Milbrath, 1989; Jayaratna, 1994; Doreet al., 2000;
Sterman, 2000; Gunderson and Holling, 2002).
Key factors that shape the alternative paradigm are the requirement for a fundamental understanding of the ‘problem situation’ within its context and recognition that knowledge of the future is made difficult because of the emergent system properties that produce unpredictable and uncertain future dynamics.
Table 5.2.Contrasting paradigms between the normal science dominant in the biophysical sciences of the twentieth century and an alternative
paradigm emerging in the twenty-first century A commonly displayed twentieth
century paradigm
An alternative paradigm for the twenty-first century
Problem solving and goal seeking orientation
Learning orientation Priority on economic growth and
development
Focus on sustainability and the long-term
Focus on short-term or immediate prosperity
Focus on long-term or future prosperity
Assumed predictability and certainty Unpredictability and uncertainty
Control Adaptive management
Single linear causality Recognition of need for holistic/integrative thinking One ‘truth’ or best answer Does not produce final answers Context not very relevant Context is important
Observer status objective Observer status is constructed or interpreted
Focusses on parts Focus on holism and integration
Analysis/reduction Synthesis
Structural constancy Structure changes affect function
Reversibility Recognition of hysteresis and
irreversibility Asymptotic stability Multiple stable states
Reliance on simple cause and effect Recognition of emergent properties of systems
Assumes systems models to be models of the world (ontologies)
Assumes systems models to be intellectual constructs (epistemologies) Science and technology have the
answers
Scepticism and critical evaluation of science and technology
Talks the language of problems and solutions
Talks the language of ‘situation of concern’ and accommodations Sources:Checkland (1984), Milbrath (1989), Jayaratna (1994), Doreet al. (2000), Sterman (2000), Gunderson and Holling (2002).
Post-normal science owes its origins to the research of Gregory Bateson (Bateson, 1979) who was originally an anthropologist and ethnographer in the discipline of second-order cybernetics (that is to say, the science of communication and complex control processes through which self-organising biological and social systems regulate themselves and maintain homeostasis or
stability within a given environment). This area of research developed into the theory of knowledge and in 1958 was identified as a new kind of science for which there was no satisfactory name (Bateson, 1979). Through this process Bateson developed a number of principles to underpin this new science which later became known as post-normal science (Funtowicz and Ravetz, 1990).
There were two major principles that underpinned Bateson’s approach. The first principle was an emphasis on the need for a process that assists the inclu- sion of diverse perspectives, that is one that facilitated an understanding of relationships among different aspects of a problem. The second principle was the need for social learning that included an adaptive approach to valuation.
This social learning approach enquired into the process by which values are constructed, thus incorporating a reflexive approach to decision making.
Normal science is unable to deal effectively with either the need to accom- modate diverse perspectives (and values) or the uncertainty of future system behaviour. Under conditions of uncertainty, standard decision-making tools that rely on quantifiable and objective facts often fail. Uncertainty arises from complex, value-laden and subjective situations that do not conform to set assessment criteria. This deficiency led to debate on new and adequate ways of dealing with and managing uncertainty. Expectations of certainty and stability about the future are being replaced with expectations of uncertainty and surprise.
Therefore, the management of uncertainty is central to the management of messy or complex problems. Uncertainty has been organised by either category or level. For example, it has been proposed that uncertainty can be ascribed to five categories as shown in Table 5.3 (Fletcher and Davis, unpublished).
Alternatively, rather than defining uncertainty into separate categories, attempts have been made to identify different levels of uncertainty in relation to decision making in which some types of uncertainty are considered more seriously than others. Four levels of increasing uncertainty have been reported as risk, lack of understanding, ignorance and indetermination (Wynne, 1992;
Yearly, 2000; Robertson and Hull, 2003). The first level of risk is involved with issues of statistical accuracy, precision and reliability. Level two uncertainty is identified in relation to level of knowledge or ignorance of the system, only the key factors of which are known. The third level of uncertainty is identified by increasing ignorance about the parameters in the system; that is, we don’t know what we don’t know. At the extreme end of uncertainty there is indetermination in which future system behaviour cannot be known because of the emergent properties of the system that arise through social action within the system.
At the most extreme level of uncertainty, Funtowicz and Ravetz (1992) identified situations in which facts are uncertain, values are in dispute, stakes are high and decisions are urgent. They postulated that such circumstances
Table 5.3.Categories of uncertainty Category Description
Randomness Lack of a specific pattern of data Vagueness Imprecision of definition
Conflict Equivocation, ambiguity, anomaly or inconsistence in the combination of data or evidence
Incompleteness That which we do not know, know we do not know, and do not know we do not know. Includes what is too complicated and/or what is too expensive to model Relevance Issues and information that may or may not impact on
the proposition being addressed Source:Fletcher and Davis (unpublished)
required very different practices from normal science, proposing ideas of post-normal science as a worthy alternative. The difference between normal and post-normal science is conceptualised in Figure 5.4 in a typology of approaches to science. As system uncertainties and decision stakes rise, the need increases for a new or post-normal science. Proponents of a post-normal science challenged that value-free science cannot exist, argued that values matter, that these must be stated explicitly and consequently that the mental constructs of those involved are important (Funtowicz and Ravetz, 1992;
Vennix, 1999; Hull et al., 2002). This is opposed to normal science that takes the position of an objective science. Although Funtowicz and Ravetz (1992) identified important differences between normal science and post- normal science, they also stressed that the two sciences were complementary.
The type of science applied is necessarily dependent upon the type of problem to be addressed (as shown in the Hawkesbury Hierarchy of approaches to problem solving and situation improvement (Table 4.3)).
Traditional disciplinary science that underpinned natural resource policy has given the expectation that there is certainty in decisions and that deci- sion makers can control and manage changes in the environment. It is now acknowledged that the science of ecology, one of the keystone sciences on which natural resource management is based, is an uncertain science and was not well handled by policy or management (Dovers, 2000b). Until recently, a major gulf has existed between so-called pure research and applied research.
Theory was developed with little reference to ‘real’ systems, and much ecolog- ical research has had little apparent relevance to environmental problems (Hobbs, 1998) and hence may have been a contributing factor to natural
Post-normal science
Professional consultancy
Normal science
Type and level of system uncertainty
Decision stakes
High
High Low
Low
Technical Methodological Epistemological
Fig. 5.4. A typology of approaches to science.Source:Funtowicz and Ravetz (1992); Ravetz and Funtowicz (1999)
resource degradation. Not only is ecological theory changing but also many assumptions of traditional disciplinary science are being challenged in the way they relate to natural and social systems. Complexity and uncertainty are key components of contemporary environmental theory and therefore are redefining the sciences of nature and human society, and changing the role of science in decision making (Robertson and Hull, 2003) and improving the public’s trust in environmental politics (Wynne, 1992; ESRC Global Envi- ronmental Change Programme, 2000).