Integrated planning support in Resource Management Part I: On shifting paradigms and the changing role of science
Thorsten Arnold, Zentrum für Entwicklungsforschung (ZEF), [email protected]
(Abstract submitted in separate file)
CONTENT
1 INTRODUCTION ...... 2 1.1 The rise of holistic management 2 1.2 What is complexity? 6 2 MANAGING COMPLEXITY – ON THE MINDSETS OF RESEARCHERS ...... 8 2.1 Accepting complexity 8 2.2 Cooperation as key principle of evolution – in biology and economy 9 2.3 Understanding self-organization and path dependency 11 2.4 From linear research to innovation as a complex process 13 2.5 Re-defining the role of research and researchers 15 2.6 Understanding Adaptive Co-Management as an emergent property 16 3 RESEARCH NEEDS AND KNOWLEDGE REQUIREMENTS...... 18 3.1 From dictating solutions towards integrating multiple views 19 3.2 From predicting the future towards multiple scenarios and options 21 3.3 From generalization towards typical patterns 22 3.4 From single projects towards coordination of multiple actors 23 4 CONCLUSION ...... 25 5 LITERATURE...... 26
Thorsten Arnold ZEF Term paper
1 Introduction
I often say that when you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot measure it, when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind. (Lord Kelvin)
I confess that I prefer true but imperfect knowledge, even if it leaves much undetermined and unpredictable, to pretence of exact knowledge that is likely to be false.
(Friedrich A. von Hayek, Nobel price lecture, in criticism against dogmatic and over-simplistic contemporaneous economic mainstream beliefs)
Until now, many development institutions pursue a quest for ready-made solutions, to give advice from disciplinary perspectives. Following the Transfer of Technology-model (Douthwaite 2002, Chambers 1993), scientists made use of well-accepted international institutions to recommend innovations1 to national research- and policy systems, to be implemented on a local basis. Such distance to local realities and the lack of recursive elements makes adaptive, multiple-goal management impossible in a changing world – and ignores procedural principles of sustainable development.
1.1 The rise of holistic management With the rise of science in 17th -18th century Europe, all-round talents like Leibnitz, Alexander v. Humboldt or
J. W. Goethe contributed major discoveries. Such personalities were trained in humanities, philosophy, physics and biology and often enough were artists in their spare time.
Nowadays, science produces new insights with such a speed and in such a quantity that high levels of specialization are required for a scientific career. Highly differentiated scientific disciplines and institutions are responsible for knowledge production and knowledge management. Such a differentiating approach has shown remarkable successes in engineering science, e.g. in the development of computers, machinery, genetic engineering and biotechnology. The technical euphoria of the 1970s led to large-scale projects that changed the appearance of our earth - the green revolution, dams, river regulations, and land consolidation are a few
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examples. Institutions relied on top-down approaches based on the recommendations of northern experts.
During the 1980s, these approaches resulted in impressive successes, but also in enormous drawbacks.
Displacement of people, migration into mega-cities, soil erosion, loss of biodiversity, poverty and hunger are ubiquitous phenomena. Last but not least, the technical approaches are highly cost-intensive and threw countries into debt crises.
Consequently, the interconnectedness of development issues became apparent. It led to the call for multiple- goal approaches that enable some balance between incommensurable dimensions. The term sustainable development became widely used and accepted as a leitbild (overall goal). Instead of focusing on single economic objectives, it calls for a holistic, long-term perspective on societal, environmental, economic and institutional goals and principles2 – and fair procedures should balance the multitude of trade-offs. This leitbild is founded on change, and thus requires permanent feedback from on-the-ground observations into decision- making and constant adaptation of rules and institutions to a changing context.
Participation of civil society and recursive policy making are means to achieve such aim. ‘Interdisciplinary’ or
‘transdisciplinary’ research are becoming widespread buzzwords. So far, no convincing ways to successfully operationalize the leitbild SD on a large scale was found. It hasn't been developed under the pure market ideology, or under hierarchical institutions or central planning.
The implementation of holistic concepts appears as a challenge. Hierarchical institutions are rigid. Bureaucrats in power were often trained in times of technical euphoria, and their career was built in the era of disciplinary successes. It is all too human for them to advocate to others what was successful for them. Challenging these professions, the development expert Robert Chambers summarizes: “We do not know much on general principles of development. We only know: Development is change” (Chambers, speech in FAO, 2003). He stresses the need to find new institutional arrangements fit for such change - and suggests an effective mix
1 Innovation is used in this paper in a general sense, comprising technical, social and institutional change. Innovation can actively be spread (i.e. by extension or as a scaling-up), or diffuse without external effort. 2 The Indigenous People, as a major group of civil society, also stress the cultural dimension of sustainability, see IITC 2003.
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between ‘conventional, top-down institutions’ and new forms of bottom-up linkage.
Meanwhile, the development community is faced with the rise of market institutions – often intentioned as substitute for state institutions. Governmental institutions de-legitimized themselves for many reasons: institutional rigidity, orientation towards out-dated rules and regulations, corruption, reliance on top-down approaches. Many lagged behind reality and were unable to react to challenges in a globalizing world. After the collision of the soviet block, such inefficiency gave rise to a paradigm based on a pure market ideology: the Washington consensus, the retreat of state influence, privatization and trade liberalization. The chief economist at the World Bank, Joseph Stiglitz, points out that such politics did benefit some states, but - as one- fits-all approach – have caused severe distress for other countries. In recent years, efforts were made to meet the challenge of balance between market- and state institutions.
Ravi Kanbur3, former director of the World Development Report 2000/2001 - Attacking Poverty, described a deep conceptual divide during the scientific preparatory meetings for that report. He classified them into two groups: the first insisted that poverty is diminishing, while the other insisted that it is rising. Kanbur labeled these groups as 'financial ministers' and 'civil society' (Kanbur 2001). After his resignation, a famous compromise was found, based on population growth: (a) poverty rises, in absolute terms, and (b) poverty diminishes, in relative terms. Both groups can therefore continue their policies as usual – but maintain incoherency.
Daniel Kahnemann (Nobel price lecture for economics 2002:483) uses psychology to explain how the human mind reacts if our rational reasoning mismatches observed reality. We adapt our ideas based on intuitive judgment build on past experience (‘anchoring’). Thus, our thinking is likely to remain unchanged in its structure. It is the most difficult intellectual task to identify structurally new solutions once intuition has anchored our thoughts in old structures. Or, as Tony Allan rephrased Einstein: “You can’t solve a problem with the mindsets that caused that problem” (Conference in ZEF, Bonn 2005).
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The structure of research funding, the organization of knowledge management and the prevailing research paradigms reflect the confinement of the disciplines. Attempts to mainstream more interdisciplinary research is often antagonized by the structure of research institutions and the pressure to publish in highly specialized journals. Such organizational set-up has lead to huge successes in engineering-type problems, but also lead to failure with more complex tasks like NRM, poverty and environmental degradation.
Such analysis seems pessimistic, and does not give adequate weight to the manifold of small- and medium scale initiatives for true integration in science, and implement sustainable resource management that succeeds to balance ecology, economy, societal issues and institutions (see e.g. the compendium from Pretty 2002).
I argue that a new approach that integrated the paradigms of both groups described by Kanbur is necessary, to manage systems with methods appropriate to system complexity. Instead of aiming to rule out one or the other paradigm, efforts should be strengthened to find and mainstream criteria how and when to use each method.
3 http://web.worldbank.org/WBSITE/EXTERNAL/TOPICS/EXTPOVERTY/0,,contentMDK:20196832~pagePK:148956~piPK: 216618~theSitePK:336992,00.html
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1.2 What is complexity? A classification of systems – using knowledge & predictability Instead of choosing a management approach according to one's own beliefs and paradigm, I argue to base such decision on an assessment of the nature of a system and on our state of knowledge to foresee that system's development in time. In this chapter, I conceptually distinguish three sorts of systems, delineated by our capacity to predict. In later chapters, co-management is suggested as a means to bring both management forms to their fullest usefulness.
For didactical reasons, Ruitenbeek (2001) distinguishes simple systems, complex systems and – as intermediate – complicated systems. Such ad-hoc categorization may not be fully stringent in the conceptual sense, but it serves well to match management approaches with underlying system behavior4. Without going into details, the following classification is used:
• Simple systems are deterministic systems with fully known starting conditions in a stable external
environment5. Interaction terms are fully known, and full information over time can be assumed - on the
interaction of subsystems, and on behavior. They have clear and fully known cause-effect relations and
can be described fully in a formal way (i.e. Newton’s mechanics). Scientific knowledge is complete, and
no uncertainty remains. Such systems are deterministic in nature, and the quality of prediction depends
only on the knowledge of the current situation. Management can be fully standardized, using linear cause-
effect relations. Top-down approaches are feasible since unexpected behavior can be ruled out.
• In complex systems, changes in the external framework are regularly shifting the importance of internal
processes, so subsystems also change their interaction terms. Research into subsystems therefore does not
significantly improve predictability, since subsystems cannot be bounded in a meaningful way. This limits
our knowledge about their internal dynamic interaction. Systems with irreducible complexity are defined
4 I find this approach more useful then the conventional “linear / non-linear” division, since all real systems are non-linear. Rather, management approaches may be based on linear cause-effect-chains or on more connected views of the world. Good point 5 Deterministic chaos may occur with only three interacting variables, so long-term prediction is possible only on the basis of mathematically complete and perfect knowledge of the status quo. The butterfly effect, where even very small uncertainty in initial conditions may lead to large uncertainty after a period of time, can already be observed for very simple systems (Three-Body-Problem in physical mechanics).
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here as systems we have incomplete knowledge about, and further research into any single subsystem only
leads to a negligible decrease of our uncertainty, thus hardly any increase in overall predictability. Such
complex systems are characterized by unexpected structural change and not by stability. In the aftertime of
such change, systems remain far from equilibrium. Management needs to be adaptive to unexpected
changes. Recursivity and permanent monitoring is a pre-requisite for any success. If one accepts the view
that humans can learn, autonomously change their behavior and self-organize, systems involving human
institutions become complex.
• Complicated systems with reducible complexity are intermediate between the former two, and many
definitions co-exist. I define them as systems in a stable external environment where research into
particular subsystems may significantly improve predictability. They incorporate a level of uncertainty
caused by our incomplete knowledge. Since modularization into subsystems is feasible, disciplinary
research can reduce this uncertainty in a meaningful way. Such a system is structurally stable over time or
structural changes can at least be anticipated. As testable property, research can significantly enhance the
quality of predictions. Management should then draw on the knowledge of experts. Also, their
management methods can be standardized in a linear sense. For decision-makers, management becomes a
question of addressing uncertainty adequately. As advisors, scientists need to develop and adopt
appropriate methodologies which are flexible enough, but also make use of predictive tools.
A key challenge for policy- and decision making then becomes to make use of the appropriate management tools adequate for the complexity of a problem at hand. Resource managers on one hand, and scientific advisors on the other, need to address predictable subsystems such as the ‘simple’ chemo-physical realm, its interaction with the biological world – without disregarding feedbacks with market forces and with human institutions, which are less predictable. Thus, the success of resource management will be closely tied to how they make use of and integrate appropriate planning tools for each complexity level, and the quality of scientific advice under its distinct paradigm.
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2 Managing complexity – On the mindsets of researchers
Thought likes solutions, wisdom abhors them. C. West Churchman
For many years, mainstream agricultural science has been committed to concerns on agricultural productivity and the development of export agriculture – as if it was separated from natural resources, labor markets and ecosystem health. Agricultural policy was given in general terms, without taking too much notice of regional particularities. Many researchers (e. g. Anderson, 1998) still defend the view that research in local, context- specific resource management yields only minor benefits for the international community (in Ashby, 2001).
This chapter outlines recent insights from “hard” sciences on system behavior that extend such views. As a precondition to develop adequate management methods, development researchers are currently in the process of taking these insights up, and to accept new perspectives on change.
2.1 Accepting complexity As first and most important precondition for successful management of complexity, researches from many fields (e.g. Frederic Vester 2002, the GTZ development workers Burger & Mayer 2001, Pahl-Wostl 2004) stress the need that system managers accept system complexity as a quality that demands new approaches.
Positivist attempts have often remained in stiff, top-down control that caused unexpected side effects. The need for more flexible and more recursive approaches became apparent. In response to failures of such
‘conventional’ NRM, scientists from the field of complexity call for a paradigm shift that enables us to deal with complex systems (Janssen 2002).
F. von Hayek criticizes the prevailing believes in “exact” scientific statements and warns that “what looks superficially like the most scientific procedure is often the most unscientific, and that in [the field of complex phenomena] there are definite limits to what we can expect science to achieve” (Nobel price presentation
Hayek 1975, insertion from sentence before). He argues that the sudden leaps in physics have spread the belief that we could mould societal development towards our liking. Instead of trying to predict the future, he opts to
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focus on the monitoring, analysis and communication of “emerging patterns” as a second best, but truly scientific option.
If we accept systems as complex, we realize that our knowledge is constrained by irreducible uncertainty. We understand that our opinions and views reflect multiple aspects of reality - and bound to be simplistic and only partial. Then, new demands for new strategies and tools arise to manage under uncertainty. These should effectively combine all our knowledge and skills, including the “soft” and “hard” ones. Many current developments take this direction – deliberate or not: the trend towards participatory and integrated NRM, the involvement of stakeholder in research. The outsourcing of management decisions to private sector companies, devolution of political responsibilities and decentralization are other process that can bring decision-making closer to reality.
2.2 Cooperation as key principle of evolution – in biology and economy System philosophy is currently being challenged in various “hard” sciences to accept the role of cooperation and symbiosis - building on work from the biologists Lynn Margulis, James Lovelock Vladimir Vernadsky; physicists like Fritjof Capra; and economists like Elinor Ostrom or Herbert Gintis.
When Lynn Margulis (1981) published her hypothesis on endosymbiosis6, she proved that the energy supply of all higher life on earth (plants, animals and fungi) is a product of the mergence of two organisms into one fitter being, not of undirected mutation as Darwinists proposed. With Whittaker (1969), she later re-wrote the evolutionary ‘tree’, described the fifth kingdom of ‘protoctists’ (fig. 2). Her new work focuses on microbial evolution, based on ‘acquiring genomes’: horizontal gene transfer as driving mechanism of evolution, which adds considerable complexity to the former linear tree of evolution (fig. 3).
6 The symbiotic origin of mitochondria and chloroplasts is now part of every biological school book. They have there own DNA and reproduce autonomously inside of cells.
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Until today, Margulis successfully defends her – quiet provocative7 - hypothesis that “no species has ever
originated without the fusion of two organisms or the effect of horizontal gene transfer” as the driving power
of evolution (Margulis & Sagan, 2002). Her aim is to extend Darwin’s theory, to explain shortcomings and to
overcome the dogma of linear, egoistic Darwinism. Her ideas go back to the Russian Vernadsky biologist
(1928) and the evolution biologist Kropotkin (1902), but also draw on her own work on Gaia-hypothesis,
together with the ecologist James Lovelock (1987).
Figure 1: A linear view on evolution Figure 2: The evolutionary tree Figure 3: The symbiotic tree of evolution, after the fifth kingdom including drawing on new insights from molecular protoctists – linearity was saved! biology (Margulis, 2002)
(GRAPHICS WITHOUT PERMISSION YET)
In his book "The Turning Point", the physicist Fritjof Capra (1982:288) realized:
“An organism that thinks only in its own survival will invariably destroy its environment and, as we are
learning from bitter experience, will thus destroy itself. From the systems point of view the unit of survival
is not at entity at all, but rather a pattern of organization adopted by an organism in its interactions with
its environment; or, as neurologist Robert Livingston has expressed it, the evolutionary selection process
acts on the basis of behavior.”
7 Margulis accepts mutation as important evolutionary factor and selection as key pressure. She sees her theory as extension rather than as alternative to Darwin’s theory. She draws a sharp line between Darwin’s scientific theory and Darwinism as an ideology derived from it.
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Even though many economists today refer to evolution as the “nature’s proof”, Charles Darwin (*1809 –
†1882) developed his theory during the heydays of Adam Smith’s works (*1723 – †1790) – which undoublty influenced his thinking. In modern economics, game theory allows to address system behavior not addressed by conventional equilibrium theory, like path dependency and heterogeneity. But also, the concept of rationality was widened considerably: work from Elinor Ostrom or Nancy McCarthy demonstrates how the outcomes of collective property management may be as good or better then state or private property regimes – depending on the specific context. Their work focus on factors that enable effective institutions for collective management, and factors required for such institutions to emerge – while remaining within the rational actor’s paradigm. Small group size, social and cultural homogeneity, problem severity, high existing social capital, consistent impacts, low discount rates, and low transaction costs are among the factors most widely seen as conducive for collective action (Ostrom 2001, Balland or Platteau 1996), while the effect of economic heterogeneity (e.g., wealth and asset distribution) seems context-dependent (Engel 2004).
Gintis (2001) even observed “strong reciprocity”: Actors are willing to punish others, which defect commonly agreed on rules. Their effort to enforce such rules far exceeds the expected direct benefit. Such behavior cannot be explained with rational actor-theorems alone. Frey (2001) stresses other psychological factors such as social preferences, intrinsic motivation and identity.
2.3 Understanding self-organization and path dependency
Aside from such broadening of the rational actors paradigm, research became aware of self-organization and path dependency. Referring to self-organization, Probst (1987:16f) distinguishes three phases: a) Adam Smith (1796) introduced the “Invisible Hand” in the same time when prices emerged as central
market mechanism – not as the result of central planning or the directives of aristocrat rulers, but
“spontaneously”, “dialectical” or from the “invisible hands”.
He influenced Darwin, and also sociologists who developed along the idea of “fitness” as a property of
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individuals, species and groups inside of a larger system. Drawing on contemporary system thinking and
on social, biological and economic theories, Carl Menger referred to markets and other economic
institutions as “evolving social organisms”. F. von Hayek stated “order can emerge, neither independent
from human action, nor as the intended outcome of such action, but as an unpredicted result of combined
behavior” (in Probst 1987, translation T. Arnold). b) The phase of “conservative self-organization” (1920 - 1960) is attributed to the emergence of system
theory, e.g. with biologists like Paul Weiss, Ludwig von Bartalanffy and Jan Smuts, and with physicists
like Norbert Wiener and Walter Cannon who worked on coupled systems, feedback loops and control. The
notion of control gave way to the idea of homeostasis, the management imperative to hold key system
variables in a certain range - of systems deviating from equilibrium and appropriate counteractions in
order to stabilize it. Ecology supposed the evolutionary “climax”. c) The phase of “innovative self-organization” (1960 - 1985) or, as labeled by Maruyama (1963), of
“cybernetics II”, was initiated with the description of self-enforcing feedback mechanisms that amplify
little deviations from an equilibrium - contemporary with Lorenz’s first description of deterministic chaos
and chaotic attractors. A need became apparent to balance stability against flexibility, conservation against
renewal, and conservative forces against forces of change and development. Maturana & Varela described
autopoiesis of biological systems, where biological systems are no longer independent from their
framework, but as self-referential unity with their context. The paradigm of “climax -” or “equilibrium
ecology” is not compatible with chaotic attractors, which allow the continuous amplification of any
deviations from equilibrium. Internal interconnectedness and redundancy was now interpreted as
safeguard for stability and resilience.
It is important to distinguish between “conservative, adaptive” and “evolutionary, creative” self-organization.
The first assumes that a changing context triggers a system to adapt towards a new equilibrium, while the latter describes innovative processes in a system in imbalance and disequilibrium (Probst 1987. p. 18). The first is path-independent, since all developments have the same outcome. In contrast, evolutionary self-
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organization is fully path-dependent, so initial conditions and the context determine the outcome.
2.4 From linear research to innovation as a complex process
In terms of impact, the Green Revolution changed the face of the earth - it was the biggest success of agricultural research and extension. Looking back, it did not succeed to solve many related issues - rural poverty and hunger remains unacceptably high, the inequality in land ownership has increased dramatically and caused out-migration into the slums of Mega cities, and environmental degradation is severely risking long-term sustainability of food production. Agricultural markets have contracted to an alarming extent, and are now dominated by a few companies (FAO 2005, IIED 2005, Vorley 2004).
Robert Chambers criticizes the Transfer of Technology-approach to extension for its bias towards the interest of researchers (patents, publications and professorship), for the stiff requirements imposed by donors, for the focus on quick profits by the private sector, and for the lack of recursivity (Chambers 1993, Chambers 1997).
As Jürgen Hagmann (2002) puts it, the linearity of the research-extension-farmer approach to diffuse innovation leaves the researcher as an outsider to the local reality of the farmer, thus supporting single-goal- oriented solutions. While disciplinary scientists can justify their research and recommendations by means of their intellectual authority (Clark 2001), negative side effects often remain ignored. The distribution of benefits and detriments is unequal, and technologies are criticized for not meeting the requirements of local contexts.
Norman E. Borlaug, Nobel price winner and inventor of crossbred seeds, takes another view and calls for a
“Doubly Green Revolution” in the 21st Century. He calls to “stand up against anti-science zealots” (Borlaug
2003:7) that resist technological change, especially in GMO technology. His argument is based on the belief that Green Revolution technologies should be improved to embrace environmental objectives. Social issues like unemployment and migration should be tackled through off-farm employment, which is the responsibility of other institutional branches and not of the agricultural sector. Thus, the social costs of unemployment or
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market access lay out of the reach of agricultural development, and the overall welfare effect depends largely on the capacity of the industrial sector to absorb the displaced. The development of sound criteria should be an absolute priority to overcome such polarized and emotional argument in an equitable way. Research into technologies that are not supportive to the needs of farmers as the bulk portion of rural poor is not in line with the poverty policies from many donors.
Ruitenbeek warns that the current fixation on prices and competition as main market mechanisms is far too simplistic. To describe altruistic behavior as emergent property, he uses the image of an “invisible wand” in analogy to Adam Smith. The role of governance then is restricted to “removing barriers to the full emergence of common goods”: prices and social cooperation. Such includes the prevention of monopolistic behavior, uncompensated environmental damage, unfair labor practices etc. How this shall be achieved remains unclear
- either through top-down control, or by giving bottom-up initiatives space to organize themselves.
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2.5 Re-defining the role of research and researchers
Hagmann stresses the need to draw a line between the roles of natural resource managers as insiders of an innovation process, and scientists as political outsiders, supporters or facilitators. Research & development interventions should accept a constructionist epistemology, where the validity of any perception depends on the observer. Interventions then need to shift from fixed, externally defined and single goals (normally defined with view towards donors) towards process-orientation, where goals are negotiated and revised with local actors and target groups.
If innovation diffusion is seen as a flow of knowledge and understanding from a holistic view, then scientists can identify and eliminate the bottlenecks of such flows – since any flow is limited by its weakest element.
The capability to learn and build new competencies will depend on how well all parts fit together and on the strength of these connections. However, it is then necessary to make a distinction between ‘information’ that is accessible to specialists and ‘knowledge’ that results from the process of learning and gives ‘meaning’ to information (a review methodology was suggested by Smith 2003 for Sub-Saharan Africa, DFID).
Since positivist scientific advice, especially if built on models, is inherently apt to take one or the other side of a discourse, it was often perceived as being political – strengthening one opinion with its hidden assumptions, rather than neutrally providing better knowledge to everyone. New modeling approaches aim to structure and inform a discourse, while remaining neutral (e.g. Barreteau et al, 2003, d’Aquino et al, 2003).
In such framework, the role of positive research shifts significantly. Since the behavior of a complex system cannot be accurately predicted through external analysis, Hagmann calls for a shift from system analysis to
“exploring the system from within”. Researchers thus need to view themselves as part of an iterative learning cycle. Their outputs should be fed back into the policy cycle in a comprehensible form, targeting the needs and perspectives of all participants (Hagmann 2000, CGIAR workshop).
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2.6 Understanding Adaptive Co-Management as an emergent property
As one method to holistic, process-oriented innovation, Adaptive Co-Management (ACM) is discussed here.
Drawing on publications from the mid-nineties, it was tested and evaluated for different NRM projects. First success factors emerged, but a comprehensive evaluation of when and how to apply such method is under development8.
Ruitenbeek (2001) bases ACM on two pillars: (1) the rights and responsibilities of stakeholders are shared and coordinated, and (2) stakeholders need to learn through actions in one period so that they may modify actions in the future. The time horizon at which ACM operates must account for long timeframes, reflecting the long time-scale of cycles in bio-economic systems. Conscious participation should then lead to conscious adaptation to long- and short-term change (p.7f).
An enabling framework includes agreement on the measure of success and the will to develop a common vision as basis for cooperation. A balance between the exploitation of existing ideas and strategies and exploration of new ideas avoids (homeostatic) stagnation of system development and lock-in behavior.
Networks of reciprocal interaction should foster trust and cooperation. The role of scientists is to explore strategies to enlighten and discuss how consequences of potential actions might spread. Valued criteria in addition to prices need to be developed, to assess the progress towards short-term as well as long-term goals.
The final point made is that, in reaction to reap small efficiencies, more failures are commonly sowed - especially if system redundancy is removed which makes it less resilient towards stress (Ruitenbeek 2001).
In disagreement to other authors, Ruitenbeek et al. elaborates that ACM has proved most successful and sustainable if it naturally emerged. It seems that attempts to impose ACM from central planning organizations have generally failed, caused resource capture and further negative (and unexpected) outcomes. Ruitenbeek warns that “premature introduction of ACM as a policy intervention could lead to system failure. This may occur because the introduction of such process disrupts existing evolutionary processes within the system” (p.
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17). External organizations may facilitate such process, and serve as a facilitator. The author surmises that the most important criticism (the inaptness of ACM in situations with strong political interests, low trust and rent- seeking) can directly be linked to such natural emergence.
Borrini-Feyerabend et. al. (2000) stress that adaptive or participatory co-management should not be regarded as blueprint solution for resource management: some pre-conditions should hold. These include active commitment active commitment of several important stakeholders, a real need for access to the resources for some of them, a need for to make controversial decisions even though different values exist, sufficient time for negotiations.
It may instead not be advisable to enter into a NRM partnership when a set of questions regarding the political, legal, institutional, economic and socio-cultural framework does not hold; specifically when
• as a result, the local communities would be renouncing a customary status of unique rights with no
expectable comparable advantage in exchange;
• he political environment does not secure the safety of all negotiating parties.
• there is no time to take decisions through negotiation (i.e. in an emergency situation)
The ACM approach seems compatible with the view of other authors on sustainable development in agriculture: the German Commission for Sustainable Development (Deutscher Bundestag 1998) recommends an “open process of search for operational goals and objectives, based on a common leitbild. IUCN states,
“Sustainable Development is a journey, not a harbor” (IUCN/IIED 1994). Burger (2001) from GTZ recommends that such co-governance is a venture that starts in our minds, one that requires courage for transparency, for responsibility and for cooperation.
In order to avoid vertical incoherence between planning and implementing institutions, Burger suggests examining the subsidiarity principle closely: decision-making shall be decentralized to the lowest effective
8 A holistic evaluation of success factors that incorporate impact on production, but also on societal development is also missing for even older attempts, like green revolution technologies: Under which circumstances do communities and environment benefit, when
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level9, while governance structures shall support the self-development of lower, decentralized entities and foster an enabling environment. The subsidiary principle was also recommended by large German NGOs
(BfdW, EED, MISEREOR, DWHH) and the German Ministry of economic Cooperation (BMZ).
3 Research needs and knowledge requirements
Even if a system perspective to planning support is accepted by researchers, our procedural knowledge how to operationalize such support remains partial. This chapter identifies knowledge needs in the face of a changing role of science. It is argued that positivist, somewhat prescriptive, and contextualist or process-oriented methods may be combined pragmatically for a development oriented towards multiple goals.
Hagmann distinguishes two main directions of development research:
• “Research on the process of integration in resource management”, which often accepts a constructivist view, is interdisciplinary, and may draw on group-learning, facilitated round tables or participatory action research.
• “Planning-support research” on technological advice, mainly conventional predictive, data-driven science or modeling.
A third element is added by the author:
• Meta-knowledge on development interventions ,stating when and how to coordinate the former two. Often enough, both are conflictive and hardly linked due to their different approaches. In Resource Management, efforts are undertaken to make use of predicative modeling in group learning processes as planning support (see FIRMA 2003 or D’Aquino et al, 2002). Other topics include a systematized review on how to intervene: context-specific success factors for projects, for coordinated scaling-up and on autonomous diffusion of innovation.
should traditional techniques be maintained since benefits are diverged to transnational corporations or local elites? 9 Since some redundancy in decision structures is tolerated and even welcomed to foster resilience of the system, some tension was observed with the “efficient approach” of some economists who recommend the “optimal” level, often hierarchically above the subsidiarity level. Other economists claim that the valuation and realisation of „optimal“ mainly depends on the value system applied, including the valuation of properties like “system resilience”.
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3.1 From dictating solutions towards integrating multiple views
The need to bridge and integrate disciplinary knowledge in NRM: classical science, management-oriented research and decision-making. Engineering approaches of resource use often built on positivist scientific advice, in the belief that reality (natural and social) can be described in accurate terms, and controlled then. A typical result is the definition of a single management objective and clear-cut goals, which are then passed to
“implementers” through top-down planning. Technical cooperation during the 70s and 80s often build on such model – many projects are either abandoned by now, or benefits did not reach the target groups. Today, the necessity is seen to embrace the perspectives of stakeholders and the objectives of user communities (Babin
1999), but remains methodologically and procedurally intricate: different morals and values collide, diverse and sometimes conflicting management objectives become apparent. Power structures remain and interfere with stakeholder communication (Röling 1996).
The idea of stakeholder platforms for collective action in multiple-use common property resources is spreading considerably after the prevailing top-down ‘transfer-of-technology’ approach used in agricultural extension proved inadequate in many settings (see Chambers 1997). Social learning has become a centre of scientific attention, as “a process that can be encouraged by lifting barriers to communications and by encouraging interaction between the parties involved in policy issues. The core idea is that parties can learn from each other by more open and responsive communication” (Steins 1998).
After a systematic evaluation of experiences, Steins summarizes central issues and limitations:
• the relation of the platform to the existing institutional setting
• their relation to other scales, since expected and unexpected impacts might occur on seemingly unrelated levels
• the question of representation or: Who has legitimacy and sufficient capacity to represent a user group in the platform
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• the heterogeneity between user groups as well as within user groups; with colliding beliefs, morals and values.
• prevailing power structures and their interactions with other levels of decision-making
In such setting of multiple actors, scientific advice can easily be drawn towards one party or the other, especially if parties are involved in funding or support fashionable themes which are easily published or taken up by mass media: due to his linkage to concurrent political interest groups as well as dominant printing in the media, the ideological ideas of the Lysenko prevailed Russian agricultural science for decades, until finally disproved by reality (Graham 1998).
If scientists seek a position as neutral facilitator in resource management for development goals, they must first acknowledge the limits of their tools to deal with complex issues. Then, they can identify adequate planning-support tools for well-defined purposes with transparent assumptions. Whether assumptions are appropriate or not in a specific context should be the decision of a group process, since they imply value judgment.
The key challenge is to keep advice transparent and maintain a position of trust and neutrality even from opposing parties. This does not only require sound foundation on well-proofed theory, but also communicative skills, political awareness and a degree of humbleness towards the unknown.
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3.2 From predicting the future towards multiple scenarios and options When the positivist paradigm still dominated scientific thinking, models were often aimed at predictions - they suggested the expected future. Self-critical relativisation that pointed at uncertainties and discussed likelihood was the exception rather than the norm. Examples include dams that displaced and marginalized people while benefits went to elites; they had adverse effects on downstream dwellers and were financially not sustainable due to sediment clogging. Thus, prediction requires the correct representation of all relevant mechanisms in a model. In cases where human decision-making and institutions are relevant, prediction-based advice is little reliable: if a modeler describes a system of irreducible complexity, new and unexpected processes are likely to emerge and limit any model-based predictions.
Still, scientific disciplines like soil science, hydrology, agricultural engineering and economics can predict the consequences of well-described intervention scenarios to a certain degree, can show effects on various scales not apparent to local actors, and demonstrate feedback mechanisms. Often, scientists can clarify and reconcile misunderstandings. Scientific learning tools were proven as very effective to support farmer experimentation and the adoption of technologies, and can be used to build local capacities. Mathematical models are used to aggregate knowledge from various scientific disciplines into a visual representation.
Careful attention needs to be paid to the normative force of scientific recommendation and political dynamics triggered by it: intervention scenarios are assumed as external to these dynamics – ignoring the human ability to learn, adapt and self-organize. Yet, predictive science can serve to communicate boundary conditions to
INRM interventions (also called guardrails10 to a tolerable window of development; see Toth et al. 1997 and
Petschel-Held et al. 1999), and serve to develop capacities.
Nowadays, the effect of our choices is often represented by agreeing on “feasible & consistent” future scenarios - these are development paths that a system might take as consequences of our future action. Such efforts may be model-based (IPCC 1995), or based upon logic reasoning and discussion (e.g. Pimbert 2001).
10 German: „Leitplanken“
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The use of scenarios may have different purposes. IPCC uses scenarios to facilitate communication between scientists and policy makers, and to offer a common platform for data sharing. The scenarios developed for the citizen’s jury in Andra Pradesh (Pimbert 2001) had a different purpose: Three consistent futures of the agricultural production and trade system were presented to an audience, building on three different development paradigms. The jury was then asked to judge and prioritize the impact that these scenarios might have on their families and friends.
Integrated scenarios have proved to be very powerful tools to stimulate discussion and focus research, but also to reflect the uncertainties inherent to complex systems. Nevertheless, these examples are disputed: while
IPCC-scenarios are criticized for oversimplification, the after-process of the jury highlighted the challenge to manage such process neutrally. Scenario results need to endure heavy criticism from political opponents – which stresses the need for saliency in science.
3.3 From generalization towards typical patterns
Maybe the most intriguing task is to confer lessons made in one setting to other cases, without falling into traps of simplification. Some attempts to systemize success factors were undertaken recently, by theoretic reasoning, and also by systematic review of case studies. Hancock (2003) addresses the tension between contextualists who stress the importance of path-dependency for any particular case, and universalists which strive for general, unifying principles. Learning from lessons, a present focus of many development organizations, requires to find a middle course: general enough to allow conference, but specific enough to avoid the failures of blue-prints.
To my knowledge, a comprehensive comparison of case studies that allow deriving success factors for different classes of contexts does not yet exist – apart of the vast literature on single cases. Case study comparison is limited by (a) a sound and readily accepted methodology, and (b) case studies which are sufficiently comparable.
As one possibility, Petschel (2001) suggests a pre-classification of contexts into “few typical patterns”, based
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on the Syndrome approach (Schellnhuber 1997). For such clusters, a certain degree of generalization seems feasible. The argument builds on the belief that, for a general and universalist explanation of development, too many processes interact. Any such attempts are doomed to “everything might cause anything” – conclusions
(see Geist & Lambin 2003 on causes of deforestation). If it is feasible to compare case studies from similar contexts, one could derive guiding, yet context-specific rules.
3.4 From single projects towards coordination of multiple actors
Summarizing literature, Engel (2004) outlines meaningful roles for a range of actors in co-management. She lists a range of responsibilities that should remain with state or local government institutions, while others should be taken from the international community, private sector, donor agencies or local NGOs. Instead of perfect but isolated projects, the few large-scale success cases of development aid tell stories of good coordination, communication skills and adaptive planning between the key actors: local organizations, local authorities, international development organizations, donors and the private sectors.
In the following, key success factors are discussed for the process of scaling-up small successes in community-driven development to a larger scale. They represent a summary from three recent publications, where authors drew on each other: Binswanger 2003, Hancock 2003 and Gillespie 2004. All three are senior consultants for World Bank, FAO and IFPRI.
Five key problems that make scaling-up difficult were identified:
• Total external and/or fiscal cost may be too high • The institutional setting may be hostile • „Co-production“ of scaling-up services may not be mastered o different values and experiences of diverse actors / co-producers (community workers, local NGOs, higher-level sector specialists, ...) o No clear distribution of information and assignment of functions to co-implementers /co-producers. o Incompatible incentives of co-implementers / co-producers Actors that can gain direct rewards from co-implementing are more likely to produce the anticipated
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outputs. In some cases, the transmission of power/budgets involved with decentralizations even acted as negative incentive, and communities were not willing to finance efforts since impacts had only medium- term pay-offs.
• Lack of adaptation to local context of practices that were successful elsewhere.
• Lack of scaling-up logistics / institutions
Key reasons that lead to successful scaling-up of good / best practices were identified:
• Strong political commitment was vital, helping overcome resistance to change and facilitating the transfer of funds and technology to communities. Strong NGOs and a lively civil society helped greatly.
• All successful scale-ups created sophisticated, context-specific procedures, incorporated in manuals with simple transparent messages. These manuals / procedures were, however, living documents that were constantly adapted in the light of new experiences and contexts.
• All successful cases had detailed planning from the micro to macro dimension. They benefited from a realistic assessment of financial resources, needs, and institutional realities.
• Successful cases had good systems for sharing and spreading knowledge to implement planning, including well-adapted guidebooks. These helped ensure that different stakeholders knew precisely what their role was, and helped provide incentives compatible with roles. No-till farming and micro credit spread fast by person-to-person and community-to-community contacts.
• Appropriate incentives for different stakeholders proved important. Managerial incentives were aimed at getting the right outcomes rather than rapid disbursement. Establishing the right processes took time and effort. Once the process were well established, disbursement picked up
• Some projects succeeded because they build on many years of past experience and utilized institutions already created, in part or full. The Indo-German watershed Development Project in India built on decades of previous experiences. ASA in Bangladesh built on micro credit models pioneered by Gramee Bank. Best practices from earlier experiences provided a useful starting point, but required adaptation to each context.
• Scaling up, as a long-haul process, need political commitment and patience over long periods. Flexibility in sequencing needs to accommodate this
These findings are still indicative. Yet, they show a combination of planning-oriented factors that are implemented centrally, and process-oriented, decentral methods that integrate the diversity of views, which were fuelled by constructivist science. For co-management, the cases clearly show the importance of process and people and also suggest areas for scientific planning-support.
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4 Conclusion
This paper attempts to outline the evolution of our ability to handle complexity in real systems, summarize lessons from development research and apply them to science-based (positivist) planning support. Such insights may renew the role of model application in Natural Resource management.
I argued that disciplinary research as advisors of specialized and hierarchical management institutions is susceptible to give single-goal approaches, which are highly probable to be incoherent due to system complexity and feedbacks. A paradigm shift to match institutional and decision structures with the complexity of underlying problems is therefore a pre-requisite to achieve SD, and scientists can then provide adequate planning-support tools for specific problems. Such shift is currently taking place, but has not yet fully embraced the mindsets.
In scientific fields like physics, mathematics, biology, economics, philosophy and sociology, complexity is an accepted quality. Yet, humans still have problems to open their mind sufficiently to fully understand the role of context, of path dependent development. Adaptivity, flexibility, resilience and recursivity seem key requirements for new forms of management, and require new views on policy- and decision-making, and ultimately a clearer definition of the role of science.
I argue that the clash of paradigms, as described by Ravi Kanbur during the preparation of the WDR 2001, is neither helpful nor necessary. Research needs to develop criteria and indicators to make fullest use of all its methods in its appropriate settings. These include efficient top-down management, if a system has appropriate properties and we have sufficient knowledge and objectives are clear. New methods for adaptive co- management need to be developed along much deeper – thus giving the management of complex resource systems a firmer scientific background.
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