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Managingcomplexadaptivesystems—Acoevolutionaryperspectiveonnaturalresource
management
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E CO L O G I CA L E CO N O MI CS 63 ( 20 0 7 ) 9–2 1
a v a i l a b l e a t w w w. s c i e n c e d i r e c t . c o m
w w w. e l s e v i e r. c o m / l o c a t e / e c o l e c o n
METHODS
Managing complex adaptive systems — A co-evolutionary
perspective on natural resource management
Christian Rammela,⁎, Sigrid Staglb , Harald Wilfinga
a
Department for Anthropology, Human Ecology Research Group, University of Vienna, 1090 Vienna, Althanstraβe 14, Austria
SPRU — Science and Technology Policy Research, University of Sussex, Brighton BN1 9QE, UK
b
AR TIC LE I N FO
ABS TR ACT
Article history:
The overexploitation of natural resources and the increasing number of social conflicts
Received 26 October 2005
following from their unsustainable use point to a wide gap between the objectives of
Received in revised form
sustainability and current resource management practices. One of the reasons for the
14 December 2006
difficulties to close this gap is that for evolving complex systems like natural and socio-
Accepted 22 December 2006
economic systems, sustainability cannot be a static objective. Instead sustainable
Available online 17 April 2007
development is an open evolutionary process of improving the management of social–
ecological systems, through better understanding and knowledge. Therefore, natural resource
Keywords:
management systems need to be able to deal with different temporal, spatial and social scales,
Co-evolution
nested hierarchies, irreducible uncertainty, multidimensional interactions and emergent
Complex adaptive systems
properties. The co-evolutionary perspective outlined in this paper serves as heuristic device to
Social institutions
map the interactions settled in the networks between the resource base, social institutions and
Natural resource management
the behaviour of individual actors. For this purpose we draw on ideas from complex adaptive
Evolutionary theory
systems theory, evolutionary theory and evolutionary economics. Finally, we outline a
Sustainable development
research agenda for a co-evolutionary approach for natural resource management systems.
© 2007 Published by Elsevier B.V.
1.
Introduction
Natural resource management systems are core to sustainable
development. Characterised by a high level of complexity, and
shaped by unpredictable external and internal changes, these
management systems aim to address sustainability conflicts,
which we face from global to local scales. These conflicts
reflect the urgent need to change our current modes of
production, consumption patterns and technological choices
to balance human well being with ecological and social
resilience. Overexploitation of natural resources, devastation
of environmental services and an increasing number of social
conflicts following the unsustainable use of natural resources
demonstrate the wide gap between the objectives of sustainability and current resource management practices.
⁎ Corresponding author.
E-mail address: [email protected] (C. Rammel).
0921-8009/$ - see front matter © 2007 Published by Elsevier B.V.
doi:10.1016/j.ecolecon.2006.12.014
On the one hand, this gap results from the shortfalls of
static approaches based on standard economic models like
the maximum sustainable yield (Carpenter et al., 2002),
short-term optimisation (Becker and Ostrom, 1995) and the
related limitations of mono-disciplinarity (Berkes et al.,
2003). In particular, neo-classical resource economics
extends this gap as it deals with ecological and environmental systems by analysing the threats arising from
scarcity constraints by reference to a “mechanical corset”
based on closed systems, reductive science, reversibility and
an a-historic worldview (Nicolis and Prigogine, 1977; RamosMartin, 2003; Rammel and van den Bergh, 2003). Driven by
neo-classical equilibrium models that are characterised by
their theoretical “elegance and aesthetics” (Nelson, 1995)
rather than by their potential to understand the complexity
10
E CO L O G I CA L E CO N O MI CS 63 ( 20 0 7 ) 9–2 1
of evolving systems, conventional resource management
systems often focus exclusively on myopic optimisation and
gains of efficiency rather than on the capacity to foster
social–ecological resilience in the long-run.
On the other hand, sustainable management of complex
evolving systems (Allen, 1990, 2001; Giampietro, 2004) is
challenged by different temporal, spatial and social scales,
nested hierarchies, inevitable uncertainty, multidimensional
interactions and emergent properties (Berkes et al., 2003;
Gunderson and Holling, 2002; Mayumi and Giampietro, 2006).
Consequently, sustainable resource management must be an
integrated and interdisciplinary process aiming at the interdependencies between institutions, environmental dynamics,
economic processes, applied technologies and dominant
cultures in managing and administrating natural resources.
But how to understand and how to model the complexity of
natural resource management systems?
Sustainability is “not a fixed ideal, but an evolutionary
process of improving the management of systems, through
improved understanding and knowledge” (Cary, 1998:12). A
growing body of literature points to the potential of evolutionary thinking in economics in general and in resource
management in particular (Hodgson, 1993; Nelson, 1995;
Heino et al., 2000; Allen and McGlade, 1987; Jeffrey and
McIntosh, 2002; MacGlade, 2002; Rammel and van den Bergh,
2003; Henrich, 2004). Ramos-Martin (2003: 390) points out, that
ecological economics is “an evolutionary science” and as such
“deals with complex adaptive systems” (Holland, 1995; Levin,
1999). In the following we argue that a co-evolutionary
approach is necessary to understand natural resource management systems and to enhance sustainability in the long
run. In this paper, we aim to develop an interdisciplinary
framework for mapping the co-evolutionary interactions
settled in natural resource management systems, which we
perceive as complex adaptive systems. For this purpose we
focus on the interactions between the natural resource base,
social institutions and the behaviour of individual actors and
draw on co-evolutionary theories from different disciplines
that are relevant for natural resource management systems.
The structure of this paper is as follows: Section 2 briefly
introduces complex adaptive system (CAS) theory as theoretical basis for analysing the dynamics of social–ecological
systems. Section 3 presents an overview about the use of the
concept of co-evolution in different disciplines. In search for
theories to underpin natural resource management, special
attention is given to the understanding of co-evolutionary
dynamics in biology, technology studies and economics.
Section 4 presents a co-evolutionary framework of natural
resource management systems. Section 5 gives an outlook of a
future research agenda on co-evolution and natural resource
management systems. We conclude in Section 6.
express large macroscopic patterns which emerge out of
local, small-scale interactions. In general, CAS are based on
“complex behaviour that emerges as a result of interactions
among system components (or agents) and among system
components (or agents) and the environment. Through
interacting with and learning from its environment, a complex
adaptive system modifies its behaviour to adapt to changes in
its environment“ (Potgieter and Bishop, 2001: 1).
In CAS, nested hierarchies, multiplicity of cross-scale
interactions and feedback loops between different hierarchical levels imply a high degree of complexity and non-linear
behaviour that predictive equilibrium models fail to calculate
(Van den Bergh and Gowdy, 2003). Analysing CAS means to
incorporate variability, adaptations, uncertainty and nonlinearity while heading for improved understanding of how
co-evolutionary processes and dynamic patterns emerge and
interact across hierarchical levels and across different spatial,
temporal and social scales (Hartvigsen et al., 1998). As CAS
theory deals with evolving, self-organising systems, it is also
concerned with resilience, path dependence and system
memory offering a conceptual framework for applying the
insights and data from small-scale analysis to understand
larger-scale patterns and processes (Cross et al., 2003).
Addressing complex interactions across various levels such
as ecosystem dynamics and institutional change, CAS theory
aims at enhancing the understanding of co-evolving social–
ecological systems1 (Berkes et al., 2003) in general, and natural
resource management systems in particular (Abel et al., 2000;
Levin, 1999). For natural resource management systems, a CAS
approach emphasises that their rules, behaviour and structures vary over time as they adapt to changing external
environments (e.g. climate effects or resource prices), just as
the particular sub-systems adapt to micro-level emergences
(e.g. new management routines or changing local administrations). In these systems, due to their evolutionary and
adaptive behaviours, two important systemic properties
emerge, which oppose any static, predictive approach: Firstly,
the emergence of novelty, which is the creative foundation of
sustainable development (Holling, 1994). Secondly, non-linear
behaviour that emphasises systems far from equilibrium and
the irrelevance to calculate any form of unique equilibrium.
The results are hierarchical aggregations, dispersed crossscale interactions and an ongoing process of creating novelty,
selection and adaptation, and in particular the existence of
inevitable uncertainty (Funtowicz and Ravetz, 1990; Stirling
and Mayer, 2001).
Far from exhibiting static equilibria, natural resource
management systems have been disrupted and changed in
the past and they express a dynamic interplay between
transitions and maintained structural configurations. This
interplay inhibits the dynamics of network evolution, and
reveals a particular optimisation problem: on the one hand,
2.
1
Social–ecological systems include social and ecological subsystems. They incorporate an integrated focus on the various
linkages between both systems. In natural resource management,
applying the concept of social–ecological systems emphasises the
objective ”to relate management practices based on ecological
understanding, to the social mechanisms behind these practices,
in a variety of geographical settings, cultures, ecosystems”
(Berkes et al., 2003: 4).
Complex adaptive systems
There is an increasing awareness in natural and social
sciences that ecological, physical as well as socio-economic
systems share the characteristics of CAS (Arthur et al., 1997;
Levin, 1998; Janssen, 1998; Ramos-Martin, 2003). Characterised
by self-organisation and co-evolutionary dynamics, they
E CO L O G I CA L E CO N O MI CS 63 ( 20 0 7 ) 9–2 1
due to their different attributes and interrelations, subsystems or agents depend on particular co-operative and
mutual interactions which drive various co-evolutionary
developments and optimisation processes. On the other
hand, long enduring evolutionary network must contain cooperative links that lead to an overall systemic performance
that is adaptive and successful in its surrounding environment (Allen, 2002). Between micro-level interactions and
macro-level adaptivity, sustainability arises, if each subsystem fits successfully in the network, and if the network
successfully fits into the wider environment. In contrast,
adaptations of particular agents or sub-systems initiate
cascadic change across the particular hierarchies and could
cause qualitative change of the behaviour or structures of the
overall CAS which may prove unsustainable and fail to cope
with its external environment. In short, any adaptation which
enhances a specific optimisation process of an individual subsystem could fail to enhance the resilience of the whole
system. This illustrates the importance of hierarchical feedback loops across scales and the capacity of CAS (such as
natural resource management systems) to create and benefit
from innovative variability of different evolutionary trajectories2 starting at small system scales while constraining
those innovations that destabilise the overall system because
of their nature or excessive exuberance (Holling, 2001;
Gunderson and Holling, 2002). For natural resource management systems this tension between adaptation, innovations
and feedback loops emphasises the need for maintaining
diversity and enhancing participatory stakeholder processes
through greater transparency and a shared contextual understanding. Both will be described in more detail in Section 5.
Traditionally, neo-classical resource economics (and even
ecology) tend to neglect the dynamics of CAS and replace nonlinearities, complexity and uncertainty through the “clarity” of
precise numbers and interventions heading for the optimisation of specific key variables (Cross et al., 2003). However, in
CAS like natural resource management systems, disturbances
and selection processes are winnowing system components,
leading to adaptations and to system reorganisation to
accommodate change. Throughout time the evolutionary
trajectories of CAS are the result of these adaptations and
are the reflection of a permanently changing and co-evolving
world. Consequently, major problems of unsustainable resource management are linked with human influence on
these evolutionary trajectories by attempting to stabilise and
control pre-selected key variables. As these attempts are
fundamentally static and unevolutionary they have generally
failed and led to rigid institutions, which were unable to
manage ecosystems sustainably (Holling et al., 2002).
In contrast, acknowledging the dynamics of CAS means
also to refer to a “co-evolutionary potential” which reflects the
ability to perceive and respond to feedback in terms of
establishing mutual and dynamic interactions between the
particular sub-systems or evolving elements. In natural
resource management systems, this potential enhances
reciprocal adaptations between and within socio-economic
2
In human systems, evolutionary trajectories include technological innovations, behavioural preferences as well as institutional arrangements.
11
systems and natural systems, with adaptations often driven
by crisis, conflicts, learning and redesign (Rammel and
Staudinger, 2004). Therefore, in CAS, sustainable management
comes close to initiating a “co-evolutionary dialogue” where a
continuous learning process is driven by the mutual and
reciprocal interactions among the interlinked sub-systems
and agents. Alongside this “dialogue”, the ability to form new
relations and new emerging properties enhances the chances
of adaptive change and social–ecological resilience.
Blann et al. (2003) illustrate this point very well by reference
to the control strategies and management skills that had
developed in the Minnesota Department of Natural Resources;
by use of case studies of forestry, water management and
agriculture their research studies “new” practitioners in the
Minnesota Department of Natural Resources who focus on
learning and change by creating open and respectful platforms for dialogue, learning, relationship building and
experimentation.
3.
The interdisciplinary use of co-evolution
The term co-evolution was coined by Ehrlich and Raven (1964) to
describe genetic change of one species in response to the
evolution of a second species. Since then several kinds of coevolutionary interactions between species or genes, promotional and inhibitory, were described by scholars of biology ( Janzen,
1980; Futuyama and Slatkin, 1983; Thompson, 1994). Starting
from strict gene-for-gene-co-evolution, over a more general
definition as reciprocal evolutionary change up to recent
approaches of gene-culture-co-evolution – which stretches the
term to include the reciprocal effects of genetic and cultural
change within the human species – the metaphorical use was
successively developed and extended (Van Valen, 1973; Durham, 1991; Lalland et al., 1999). This made a transfer of coevolutionary theory into other research areas such as technology studies and socio-economic analysis possible.
Subsequently, over the last 20 years, there has been
growing awareness that technology is co-evolving with its
socio-economic and bio-physical environment. This emphasises the inevitable interdependencies between technologies,
institutions, values and the bio-physical environment (Nye,
1992; Vicenti, 1994; Mokyr, 1990; Basalla, 1988; Nelson and
Winter, 1982). These studies focus on the analysis of path
dependence, adaptive technologies and lock-in situations
(Arthur et al., 1997). In economics the co-evolutionary
metaphor is probably most widely used for the interrelations
between technologies with industrial structures (Dosi and
Kogut, 1993; Nelson, 1994a,b; Antonelli and Marchionatti,
1998; Nelson and Sampat, 2001; von Tunzelmann, 2003).
Norgaard (1994), Kemp (1997), Minsch (1997) and Rennings
(2000) highlight the importance of co-evolutionary interactions between technology and the related institutional setting
which is of particular importance in the case of ecoinnovation, sustainable management systems and “green
technologies”. Focussing on the policy arena, Kemp and
Rotmans (2001) developed a co-evolutionary framework for
sustainable technology development, which was adopted by
Dutch environmental policy makers who aim to orientate
private and public actors toward sustainable transition goals.
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At another level the co-evolutionary metaphor is applied
for conceptualising interrelationships between human behaviours and institutions. Actors influence each other directly or
indirectly via social institutions. “In a co-evolutionary process,
[...], the adaptive landscape of one actor heaves and deforms
as the other actors make their own adaptive moves” (Kauffman, 1993: 238). However, the co-evolutionary behaviour is
not “limited to attaining point attractors which are local
optima, nor is it clear that co-evolving systems must be
optimising anything whatsoever” [ibid]. For recent reviews in
this area see Van den Bergh and Stagl (2003), and Gintis (2003).
In environmental management co-evolutionary concepts
are adopted to highlight the role of individual and social
learning for adaptive management (Norgaard, 1988; Hadfield
and Seaton, 1999; Janssen et al., 2000a,b; Keen et al., 2005;
Stagl, in press). Anderies et al. (2004) developed a coevolutionary framework for identifying parts of a social–
ecological system which are potentially vulnerable to internal
disturbances and to identify design principles for institutions
which lead to robust social–ecological systems. After reviewing a large number of historical cases they conclude that
whether or not the multilevel and multi-scale aspects of a
social–ecological system provide benefits or incur costs
depends delicately on the institutional arrangements and
that a diversity of adaptive institutions will be necessary for
fostering sustainable development (Ostrom, 2005).
4.
A co-evolutionary framework of natural
resource management systems as complex
adaptive systems
4.1.
The conceptual base
Natural resource management systems as complex adaptive
systems (CAS) are characterised by their dynamic interdependencies across various scales and are driven by mutual
interactions between institutional, ecological, technological
and socio-economic domains. Hence, we argue that sustainable
management requires interdisciplinary analysis and improved
understanding of multi-dimensional feedbacks and, more
generally, of the dynamics of the interrelations between the
particular interacting subsystems. To achieve such an understanding and to broaden our analytical scope, we consider a coevolutionary perspective on natural resource management as
an operational focus that shall provide an appropriate conceptual umbrella to tackle key aspects of sustainability. However,
using a “co-evolutionary umbrella” does not necessarily mean
the application of a new methodology; for a start it emphasises
the need for a novel and interdisciplinary combination of
existing tools (such as agent-based system modelling, institutional analysis, multi-criteria appraisal and / or dynamic system
modelling) to capture co-evolutionary interactions and complex
dynamics inherent in natural resource management.
Certain tools are, however, based on specific paradigms and
show a great dependency on the mental models of actual tool
users. Some methods and tools providing a “co-evolutionary
umbrella” may prove useless in the hands of those trained in
normal (reductive) science. Facing these constraints of reductive
approaches to natural resource management, researchers are
increasingly calling for a new dynamic paradigm; such a new
paradigm is meant to, firstly, help understand how these
systems interact across different scales, and secondly, how to
manage them (Holling, 1995; Costanza et al., 1997; Cumberland
et al., 1997; Lubchenco, 1998; Patterson and Williams, 1998;
Allison and Hobbs, 2006). Consequently, this new paradigm
which emerges from the quest for a better understanding of the
complex interaction across hierarchies and domains of natural
resource management systems requires a range of new tools as
well as better use of exisiting ones; both approaches need
researchers and natural resource managers with new mental
models of systemic approach and skills to apply them.
Clearly, a co-evolutionary approach to natural resource
management systems is neither deterministic nor will it
encourage any predictive policy to control or “correct” resource
management problems. Even though a co-evolutionary perspective focuses more on understanding the past (ex-post analysis),
this does not limit its potential. To understand how today's
conditions and problems were created in the past, analysing
notions such as emergences, path-dependencies or co-evolutionary developments increases our ability to maintain our
options for sustainable futures and enhances adaptive management in order to contribute to social–ecological resilience.
At a general level, we conceive co-evolution as dynamic
interactions between two or more interdependent systems,
which account mutually for each other's development. In
detail, co-evolution can be seen as the evolutionary process
among two or more elements/sub-systems/systems driven by
reciprocal selective pressures and adaptations between these
elements/sub-systems/systems. Thus, a co-evolutionary system can be defined by the totality of all the interacting
elements/sub-systems (see also Jeffrey and McIntosh, 2002).
Moreover, co-evolutionary dynamics reflect different temporal, spatial and social scales, nested hierarchies, inevitable
uncertainties, multidimensional interactions and contain
emergent properties (Janssen et al., 2000a,b; Gowdy, 1994;
Stokes, 1994).
In applying a co-evolutionary perspective to natural
resource management, we perceive natural resource management systems as hierarchically arranged mosaics of coevolving social, technological and environmental processes
or elements. As social–ecological systems they express an
essential part of human-environment interactions in which
both sides modify one another continuously by mutual
feedback creating a dynamic process shaped by qualitative
change, error making, ignorance, learning and adaptation
(Allen, 1990; Norgaard, 1994; Berkes et al., 2003).
There is a common understanding that research on coevolutionary dynamics implicates a system–theoretic approach which allows coping with dynamic interrelations,
uncertainty and qualitative change (Norgaard, 1994; Jeffrey
and McIntosh, 2002; McClade, 2002). Referring to CAS, these
dynamic interactions take place across hierarchical overlapping levels (Holland, 1995), which can express a great
diversity of possible sub-systems. From a heuristic point of
view, these levels can be aggregated into (1) natural resources,
(2) institutions and social organisations (including technological systems) and (3) individual behaviour. The classical
dichotomy between exogenous and endogenous factors of
resource management systems can be overcome as these
E CO L O G I CA L E CO N O MI CS 63 ( 20 0 7 ) 9–2 1
factors as well as their feedback can be found across all three
hierarchical levels.
Drawing on the work of Simon (1974), we use the term
”hierarchies“ not in the meaning of top-down sequences of
control and power, hierarchies are rather seen as semiautonomous levels that are created by interactions among
variables that share similar spatial and temporal attributes.
These hierarchies are by no means static structures, their
interconnected levels could instead be understood as “transitory structures“ (Holling et al., 2002) that are conserved by
interactions of changing processes and structures across
various scales. These transitory structures can consist of
particular entities of ecosystems as well as cover elements in
technological systems, individual corporations, industrial
sectors or socio-ecological systems.
Across these three hierarchical levels, today's natural
resource management faces very complex, challenging problems but also opportunities. We perceive them as “natural
elements” of co-evolutionary processes and, in this context,
natural resource management as a regulation system that
links (at diverse scales) two sub-systems: human societies on
the one hand and bio-physical systems on the other. We call
the whole co-evolving system “human–nature-system” which
expresses the characteristics of social–ecological systems
(Berkes et al., 2003). In general, co-evolving “human–naturesystems” contain a high level of inevitable complexity in
terms of dynamic, cross-scale and interdependent interactions between particular sub-systems of natural resource
management, which must be considered simultaneously
when aiming for sustainable development. Additionally, as
CAS, “human–nature-systems” are characterised by unpredictable change affecting the structure, quality, rules and
behaviour of the particular natural resource management as
well as its external conditions. Consequently, management
for sustainability must tackle the key notion of adaptive
capacity to deal with change in a socially, economically and
ecologically sound way. In short, adaptive capacity refers to
the design and potential of natural resource management
(expressed in institutions, knowledge, policies and technologies) to change and adapt in response to altered conditions,
crises, emergences and unpredictable effects of (co)evolutionary dynamics. Hence, adaptive capacity is the capacity to
perceive stimuli and to send signals for adaptive change, i.e. to
respond to balancing feedback. Facing the challenges of
complex and co-evolving natural resource management
systems, such as unpredictable qualitative change and
uncertainty, it is the diversity3 of opportunities and systemic
properties that provides the capacity to enhance adaptivity in
terms of buffering and reorganising after disturbance and
change (Stirling, in press; Folke et al., 2002; Elmqvist et al.,
2003; Rammel, 2005). The importance of maintaining diversity
for a future research agenda on natural resource management
systems will be outlined in Section 5.
3
That diversity is a crucial property not only for the resilience
and sustainability of natural resource management systems, but
is also an important and very special feature of sustainable socioeconomic development was shown in this journal by several
authors across various disciplines (e.g. Schütz, 1999; Matutinoviæ,
2001; Rammel and van den Bergh, 2003).
4.2.
13
Environment and the co-evolution of institutions
Heading for an evolutionary understanding of the interactions
between social institutions and the environment, we face the
risk entering in the controversy about “nature or nurture”.
Since the rise of Darwinism, there has been “a long and bloody
Hundred Years War among anthropologists” (Fracchia and
Lewontin, 1999) over whether human culture (and its inherent
institutional features) is evolving through differential survival
and reproduction of cultural elements or whether human
culture is a self-perpetuating and autonomous human product explicable in terms of unfolding and intrinsic directionality
(Biersack, 1999; Dunbar, 1999).
Without exploring this debate here fully it is obvious that
human ability to build institutions is a fundamental base for
the societal efforts to utilise and manage ecological niches and
natural resources. In principle, institutions evolve over time
either by deliberate design or spontaneously, constrained by
both context and path dependencies. This means that their
structures, rules and objectives reflect past conditions and
reveal the process of adaptation over time (Hodgson, 1993).
Shaped and selected by environmental as well as cultural
factors they enfold a multidimensional process (Van den
Bergh and Stagl, 2003), which specifies the way and intensity
of exploitation of natural resources and their further utilisation and distribution within the socio-economic system
(Matutinoviæ, 2006).
Since the advent of the very first agricultural settlements
institutional features covering issues of governing, distributing and accessing resources became crucial elements in the
human struggle for survival. Due to environmental changes or
increasing population, institutional arrangements played a
major role to find adaptive answers to new challenges in the
form of intensification of natural resource usage, sociopolitical reorganisations or access rights, to list only a few
(Boserup, 1981; Stone and Downum, 1999). Consequently,
these efforts of cultural adaptations influenced the evolutionary paths of bio-physical systems. Hence, co-evolution theory
stresses that bio-physical settings and institutional features
change together; the evolution of each is reflected in the
evolution of the other (Norgaard, 1994). Institutions such as
community-based management systems have co-evolved
with their natural resource base and developed adaptive
knowledge to live with environmental change and surprise
(Berkes et al., 2003). Referring to the mutual interrelations
within “human–nature-systems”, we understand this situation as “co-evolutionary dialogue”, which is expressed by
different hierarchical levels with different spatial, temporal
and social scales. Within this dialogue, changes in the
environmental setting will partly be related to institutional
adjustments and adaptations that emerge within the socioeconomic systems and vice versa.
As integrative part of cultural evolution, norms, social
conventions, social and economic rules co-evolved with
environmental conditions as well as with technological
trajectories and economic prosperity (Dalton and Coats,
2000). Consequently, institutional arrangements are constrained and shaped by the multidimensionality of society's
history. Thus, neither can the “right” institutional form be
chosen without limitations nor is the intended outcome of
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institutional change autonomously achieved (Ostrom et al.,
1994). Moreover, institutions are following a continuous
process of trial-and-error learning about the selective pressure
of environmental settings as a stimulus of improvement, the
operational rules and the transformation costs of changing
these rules. Following evolutionary dynamics, these learning
processes (based on adequate information about the environmental impact) will by no means reach adaptive success
automatically as is shown by numerous cases, for example in
the area of water management systems (Hauchler et al., 1999;
Shiva, 2002). On the one hand, the timeliness and quality of
the selective feedback that institutions obtain vary according
to how natural resources are used and affected by the
anthropogenic intervention and they vary across different
types of environments (e.g. using “invisible” groundwater or
using “visible” surface water). Additionally irreducible uncertainty is present when dealing with CAS and with the nonlinear dynamics of social–ecological systems. On the other
hand, culture and institutions create their own social reality
where the criteria of success and selection are orientated on
“non-environmental” issues such as social coherence, wellbeing, power or psychological satisfaction (Stuart-Fox, 1999;
Rulye, 1973). For the case of water management, the perception of the situation (Do people believe that there is a water
shortage?), the perception of the change of the situation (Did
water demand rise quickly or steadily over a long period?) and
perceived responsibilities (Should water companies repair the
leaks and forego a share of their profit? Or should businesses
and households limit their water use?) are only a few
examples which help shape specific social realities. The
more complex and powerful institutional arrangements
become, the more societies are faced with the dilemma of
two dimensions of reality: one social and one ecological,
expressing two interdependent issues of individual survival
and well-being (Rammel and Staudinger, 2002). Thus, much of
institutional preoccupation is focused at achieving goals
whose underlying values emerge apart from their environmental relevance.
The selectivity of ecological settings affects the co-evolution of institutions, therefore the environment encountered by
norms, rules and social conventions does not just act as static
“template” to which institutions adapt. Given the potential of
institutions to manage processes of “ecosystem engineering”
(Jones et al., 1997) they are highly able to construct or modify
their relevant niches (for the evolutionary importance of niche
constructing see also Odling-Smee et al., 1996; Lalland et al.,
1999). Hence, constructing niches refers to an active process of
altering the environmental setting, which is followed by a
sometimes highly significant (positive or negative) selective
feedback in terms of an “ecological inheritance” (Odling-Smee,
1988). However, institutions that manage and use natural
resources do not need to face the consequences of the
“ecological inheritance” immediately. The more niche construction is advanced in terms of technological and institutional power, the more it temporally damps negative
environmental feedback expressing some kind of co-evolutionary time-lag.
The benefits of institutions modifying or constructing
ecological niches are obvious. Facing greater independence
and different time scales in terms of slow environmental
changes enhances steady accumulation of adaptive knowledge and a more efficient control over natural resources and
ecological functions. Additionally it supports institutional
stability and cohesiveness as it pays each institutional
member to invest in mutually beneficial niche constructing
(Lalland et al., 1999). Furthermore, shielding the immediate
selective pressure gives room for creativity accompanied by an
increased variety of alternative innovations. This variety
could enable rapid adaptive changes, if the niche construction
breaks down. Nevertheless, co-evolutionary time-lags do not
avoid the necessity of adaptive institutional change and the
high importance of continuous learning by interacting, which
is primarily based on the process of perception. Physical
changes and the “ecological inheritance” do not generate the
significant societal response until they are recognised as
problems. In general, the driving force behind institutional
change and learning is the perceptual system which emerges
out of a co-evolutionary process between environmental
triggers, accumulated knowledge, political objectives, and
the controversial and mutual communication between individual and aggregate appreciative systems (Hadfield and
Seaton, 1999).
Thus, to orientate the “co-evolutionary dialogue” along the
lines of a smooth steady state of change, successful longenduring institutions must invest in comprehensive systems
of perception and monitoring as the foundation of adaptive
management (Walters, 1986; Walker et al., 2002). As shown in
the literature on common property resources, institutions coevolving with the environment in an adaptive and sustainable
way express the ability to modify their operational rules over
time in light of past experience and new perceptual emergence (Ostrom et al., 1994).
4.3.
Institutions and co-evolving behaviour
The previous section showed that institutions are shaped by
their environment and that they shape the environment.
Social institutions and organisations impact the environment
via management activities. Additionally they shape people's
behaviour by limiting behavioural options and at the same
time enabling the pursuit of (possibly new) behaviours.
As outlined above, co-evolutionary interactions between
individual behaviours and institutions are mainly channelled
via knowledge, norms and values which bridge the collective
outcomes of far from equilibrium interactions and heterogeneous learning at the behavioural level with the established
mechanisms at the institutional level that shape, constraint
and initiate low-level behaviour. From an evolutionary point
of view, institutions and the cognitive structures enabling
individual behaviour to perform similar functions, which
provide the capacity to simplify complexity, classify phenomena, dissolve uncertainty and collect information to orientate
and constrain our choices (Morand and Stagl, 2001). However,
individual behaviour is embedded in and actively forms the
institutional context meaning that knowledge, norms and
rules start to emerge as individually generated patterns based
on individual learning and personal experience. In a bottomup process these behavioural patterns and conventions shape
and influence institutions as higher level entities, when
people make use of other's experience and benefit from
E CO L O G I CA L E CO N O MI CS 63 ( 20 0 7 ) 9–2 1
reducing complexity and uncertainty through adapting conventions in order to govern given aspects of social life.
The co-evolutionary dynamics between emergences,
embeddedness and mutual feedback shape a process of social
selection, which, as an essential factor driving cultural
evolution, is characterised by hierarchically structured processes of eliminating or supporting alternatives (Rammel and
Staudinger, 2002). From individual cognitive selectivity based
on personal life trajectories, over already learned knowledge
which biases subsequent learning, to personal practice where
actions are differentially expressed in relation to their
selective environment, to actions of social groups and
institutions, this social selective process transforms culturally
dependent models into social reality (Campbell, 1991; Gellner,
1993; Stuart-Fox, 1999). Consequently, social reality can
neither reflect “the true nature” of things nor can it describe
the complexity of ecosystems and natural resources. As
institutions regulate the relationships and interactions
among individuals as well as between ecological and socioeconomic systems (Gatzweiler and Hagedorn, 2002), they filter
and generate the reality of natural resource management
systems, define management problems (and ignore others)
and finally direct possible paths of solutions.
As discussed briefly in Section 3 the relationship between
social actors and institutions has recently received much
attention within the socio-economic research community by
way of developing novel analytical frameworks as well as
conducting laboratory experiments. Notably, it was shown,
that the sustainable use of natural resources often requires
cooperation and institutions fostering cooperative behaviour
(Axelrod, 1997) and the development of pro-social norms
(Bowles and Gintis, 1998). The emergence of different outcomes in the context of common pool resources and
cooperative behaviour was analysed by Deadman et al.
(2000), Jager et al. (2000) and Ostrom et al. (2002). In fact,
some of the most sophisticated and successful co-evolving
natural resource management systems are common pool
institutions that enhanced behavioural responses towards
long-term sustainability. Examples in this area include Swiss
grazing commons (Ostrom et al., 1994), or the Spanish
irrigation systems called “huertas” (Glick, 1970).
Recently, agent based modelling (ABM) has proven to be a
remarkably versatile tool for understanding behaviour in
social–ecological systems (Gilbert and Conte, 1995; Berger,
2001; van der Veen and Otter, 2001; Walker and Janssen, 2002;
Parker et al., 2003; Sengupta and Bennett, 2003; Hodgson and
Knudsen, 2004; Happe et al., 2006). It enables us to address the
fundamental issues of the agency-structure debate, which
highlighted that systems consist of both agents and structures, each of which has an impact on the other (Giddens,
1979). In particular, ABM can capture the co-evolutionary
interactions at the micro level within natural resource
management systems and their institutional environments4.
This approach gives special attention to the co-evolutionary
dynamics of the organisational levels (of resource consumers
and providers), which interact through rules and agents. As it
was shown by Janssen et al. (2000a, b) for the case of resource
4
For an up-to-date list of references on ABM in land use
research see:http://www.complexityscience.org/NoE/ablum.htm.
15
management in complex rangeland systems, the mutual
interactions between individual actors, institutional regimes
and different policies in CAS can be analysed by adaptive
agent models which emphasise that “behavioural rules at the
level of individual agents lead to emergent properties at the
macro level. Instead of traditional deterministic equilibrium
seeking models, adaptive agent models evolve, leading to
irreversible structural change. External and internal disturbances prevent the system from reaching equilibrium”
(Janssen et al., 2000a,b: 250).
An area of natural resource management, to which ABM
was frequently applied, is participatory water management
(Tillman et al., 1999; Tillman et al., 2001; Pahl-Wostl, 2002;
Barreteau et al., 2003; Becu et al., 2003); it contributes to the
development of an interface of analytical modelling and
participatory stakeholder approaches. This approach also
offers a potential resolution of the duality between individual
and collective levels of a system is offered. Especially, the
potential to support participatory resource management
through linking modelling with the quality of the process of
decision-making, rather than to aim at final “objective results”
is a fundamental issue of a co-evolutionary approach and will
be crucial to a future research agenda on natural resource
management.
5.
A future research agenda on natural
resource management
Recalling the recent developments in natural resource management, we perceive a lack of dynamic approaches based on
CAS and co-evolutionary theory. However, the multi-dimensional nature of natural resource management systems calls
for interdisciplinary bridges and communication about general phenomena such as complexity and cross-scale interactions. In the following we suggest two complementary issues,
which, in our opinion, represent promising areas within a
future research agenda on natural resource management.
5.1.
Enhancing participative stakeholder processes
through greater transparency: modelling co-evolutionary
dynamics
Due to the very nature of CAS, in natural resource management our knowledge is always incomplete and surprise
inevitable. In fact, we face complex co-evolving systems
which act as “moving targets”, continuously evolving at
multiple spatial, temporal and social scales. At best we can
hope to understand the evolution of natural resource management systems after the fact, but any attempt to control or
predict is arbitrary and indeed bears the risk to erode the
resilience of the overall social–ecological systems (Holling and
Meffe, 1996; Gunderson et al., 1995).
If fundamental uncertainty and potential costs (stakes) are
high, “traditional” science is “inadequate” and ethical judgement is “ubiquitous” (Funtowicz and Ravetz, 1990). As part of
post-normal science, Funtowicz and Ravetz suggest participatory procedures with all relevant stakeholders for decision
making (see also Renn et al., 1995; O'Connor, 2000; Kasemir
et al., 2003; Smith, 2003). For natural resource management
16
E CO L O G I CA L E CO N O MI CS 63 ( 20 0 7 ) 9–2 1
such participatory processes are key to sustainability as it is a
crucial means for addressing existing conflicts and achieving
the “co-evolution of stakeholder preferences” heading for
shared goals and a compromise decision (Sherwill and Rogers,
2001). Therefore, a central idea of a co-evolutionary approach
must be to enhance a shared contextual understanding about
natural resource management systems in such a way, that
researchers provide the stakeholders involved in the decisionmaking process with “integrative information” about the
system in question, but letting them their own way to reach
compromise solutions.
By “integrative information” we mean offering a greater
degree of transparency of CAS. Obviously, sustainable use of
natural resources is unlikely without improved understanding
of cross-scale interactions, lagged responses and non-linear
behaviour pointing at the necessity to expand from knowledge
about structures to knowledge about co-evolutionary processes that enhance the capacity of natural resource management
systems to address change and surprise (Berkes et al., 2003;
Gunderson and Holling, 2002). To understand how such
systems respond to and trigger change, to understand how
particular sub-systems (including particular stakeholder
groups) of natural resource management systems interact
across different spatial, temporal and social scales is central
for improving the quality of decision-making processes and
for enhancing participation.
Greater transparency and improved understanding of the
dynamics of natural resource management systems points at
systems modelling which help to unravel complexity in CAS.
Recently, systems modelling has made crucial advances to
map co-evolutionary dynamics in CAS proving information
about qualitative change, emergences and non-linear behaviour (Janssen et al., 2000a,b; Bonabeau, 2002; Parker et al.,
2003). It was shown, that models incorporating the coevolutionary dynamics of CAS are viable exploratory tools
for the formalisation process of coupled, co-evolving “human–
nature systems”, as well as the exploration of trajectories of
possible scenarios (Goldstein, in press).
However, modelling CAS must be interdisciplinary, integrative, focussing at appropriate scales, stakeholder oriented and
must incorporate change and uncertainty, rather than present
“truth“ through images of closed and mechanic systems which
fail to reflect real life phenomena. Modelling of natural resource
management systems can be highly valuable to participatory
resource management, as long as we remember that even the
knowledge that we can obtain from any model of CAS is always
context dependent (Clark et al., 1995). For example, participatory
multicriteria appraisal tries to present and organise multidimensional and varied information about complex issues (e.g.
water systems of a region) in such a way that it encourages
stakeholders to explore options and deliberate about them
before making a decision (Munda, 2000).
In short, modelling CAS must be along the lines of science,
which heads for integrating parts, rather than along the
traditional science of parts (Abel, 1998). This emphasises the
involvement of relevant stakeholders at the earliest stage of
modelling (choosing relevant indicators, identification and
ranking problems, revealing hidden conflicts, etc.) as well as
providing user-friendly models and visual interfaces that have
the potential to communicate dynamic interactions across
scales and support social learning processes. Improved
knowledge is neither effective in isolation nor will it contribute to sustainability if not “translated” to local stakeholders.
As stressed by Haag and Kaupenjohann (2001:46) “[m]odelling
for decision-making may have to take into account requests
for transparency and participation and the validity of model
products will be judged according to their capacity of
providing context-sensitive knowledge for specific decision
problems”. After all, modelling co-evolutionary dynamics in
resource management must provide an interface between
science, users and management (Rogers, 1998; Videira et al.,
2003) in order to overcome the split of academic science and
managerially useful and applicable science.
5.2.
Maintaining diversity
Facing the challenges of co-evolving natural resource management systems, which express high levels of complexity
and irreducible uncertainty, we have to learn to include
periods of change, surprise, disturbance and crises, followed
by times of renewal and reorganisation in our management. In
fact, sustainable resource management should be able to
understand and enhance the mechanisms that maintain and
conserve the ability to adapt to changing conditions, respectively include the notion of adaptive capacity as a fundamental hallmark of sustainability (Abel et al., 2000; Jeffrey and
McIntosh, 2002; Blann and Light, 2003; Folke et al., 2002;
Gallopin, 2006). As increasing body of literature and empirical
research shows, in CAS, and especially in natural resource
management systems, it is diversity as a fundamental system
property that provides the potential to enhance adaptivity in
terms of buffering and reorganising after disturbance, crisis
and change (Folke et al., 2002; Elmqvist et al., 2003; Stirling,
2005). Thus, the challenge for natural resource management is
to learn to live with change and surprise and to nurture
diversity for renewal and reorganisation (Berkes et al., 2003).
From an evolutionary point of view, diversity is related to
the “co-evolutionary potential” as the capacity of systems
(sub-systems or agents) to establish new evolutionary interactions which might initiate future development trajectories
(Rammel and Staudinger, 2004). It is also the potential to
perceive and respond to feedback in terms of establishing
mutual and dynamic interactions between the particular
systems or evolving elements. In natural resource management systems, “co-evolutionary potential” supports mutual
relations between and within socio-economic entities and
natural systems, with adaptations often driven by crisis,
emergences, conflicts, learning and redesign. Following from
the diversity of alternative futures, learning and redesign is a
co-adaptive process which is driven by permanently adjusting
the rules that underpin natural resource management in order
to cope with uncertainties and fundamental change (Folke
et al., 2002).
Notably, as neo-classical resource economics advances
“optimal management” based on the short term success of
increasing yield in homogenised environments and stabilised
ecosystem outputs (Gunderson and Pritchard, 2002), the coevolutionary potential as well as continuous learning and
redesign is not sufficiently reflected in conventional resource
management. However, “evolution does not lead to individuals
E CO L O G I CA L E CO N O MI CS 63 ( 20 0 7 ) 9–2 1
with optimal behaviour, but to diverse populations with the
resulting ability to learn. The real world is not only about
efficient performance but also the capacity to adapt. What is
found is that variability at the microscopic level, individual
diversity, is part of evolutionary strategy, and this is precisely
what mechanistic systems representations do not include. In
other words, in the shifting landscape of a world of continuous
evolution, the ability to climb is perhaps what counts, and what
we see as a result of evolution are not populations with ‘optimal
behaviour’ at each instant, but rather actors that can learn”
(Allen, 1990: 563).
Putting the objective of maintaining diversity at the very
core of sustainable natural resource management, a broad field
of future research opens up. Referring to the question how to
nurture diversity in social–ecological systems to cope with
disturbance uncertainty and crisis, major advances were made
through the pioneer work of authors such as Berkes et al.
(2003), Berkes and Folke (1998) and Gunderson and Holling
(2002). Among others, these studies showed expressively, that
notions such as sustaining and enhancing the social–ecological memory, creating opportunities for self-organisation or
combining different types of learning and knowledge are
crucial in preserving diversity and adaptive change (see also
McIntosh et al., 2000). Obviously, these notions bridge to
current research fields in ecological economics concerned
with multi-level governance, institutional learning, behavioural analysis and vulnerability analysis to name only a few.
“The real world is not only about efficient performance but
also the capacity to adapt” (Allen, 1990: 563). Even as we
strongly emphasise the essential role of diversity, and deplore
the lack of adequate integration of diversity in standard
economic approaches, efficient performance must be high on
the agenda of natural resource management. However, as in
every evolving complex system, natural resource management faces an implicit trade-off between short-term local
optima, to achieve specific criteria of efficiency, and maintaining the co-evolutionary potential, to achieve adaptability
and resilience in the long-run. Investing in adaptivity lowers
the efficiency gains of today and investing in efficiency
reduces the chances to cope with tomorrows change. Two
ecological economists, Giampietro (1997) and Mayumi and
Giampietro (2001), addressed this trade-off between efficiency
and diversity, by emphasising that redundant diversity of
obsolete activities expresses a maintained repertoire of
alternative meanings of efficiency which increases the
capacity to adapt to changing conditions, emergent properties
or disturbances. The distinction between the roles of efficiency, effectiveness and resilience goals are also well addressed
in the organisation analysis (Quinn and Rohrbaugh, 1983) and
political science literature (Sabatier and Mazmanian, 1980;
Sabatier and Jenkins-Smith, 1993).
As shown by Holling and Meffe (1996) and Gunderson and
Holling (2002), natural resource management systems5 express
a global tendency to increase efficiency and standardised
routines, while losing their co-evolutionary potential and their
5
For a general perspective on this phenomena in CAS see also
the notion of adaptive cycles expressing the tension between
exploitation and renewal, which is essential for preserving
resilience at the overall system level (Holling, 2001).
17
ability to change adaptively and tackle emerging change and
conflicts. This tendency reflects a gradual shift from management where “weak” institutions are mainly concerned with
responding passively to external variability towards management where “strong” institutions control external variability
through advanced technologies and intensified internal connectivity and standardisation. Evolving systems are easily
locked into their own success and selection criteria, which
were built in the past and constrain the future through selective
perception and path dependency. Hence, short term success of
rising yield in increasingly controlled environments and
evolving routines towards optimisation of current management
objectives create a lock-in situation of more and more inflexible
management patterns. Paradoxically, this represents a situation
where initial success of resource management can lead to
unsustainability and vulnerability in the long run.
Sustainable management of natural resources implies
striving for a sound balance between efficiency and adaptability while keeping an institutional design which incorporates adaptive change, learning and renewal. A striking
example for this trade-off can be found in energy policy. For
strategic reasons a variety of energy technologies are supported under R&D and other non-market schemes; they
produce not only technologies that enable us to generate
energy services more efficiently, but also the adaptive capacity
of our economies to respond to various disturbances (Stirling,
1994). For example, Brazil benefitted from its capacity to
generate biofuels once world oil prices increased; for years this
technology had been considered as secondary. The more
drastic the disturbances that we face, the further away from
current technologies future solutions may be found. Energy
policy based on CAS would treasure the diversity of technologies more than current policies do.
For future research it means to support processes that
increase efficiency in natural resource management, obtained
by reinforcing the highest performing activities without the
complete elimination of the obsolete ones. However, it means
also to shift the focus from current research from a short-term
to a long-term horizon, where co-evolutionary potentials and
innovative diversity can be linked to sustainability.
6.
Conclusions
Natural resource management systems can be described as
CAS. Embedded in a range of hierarchical levels with different
spatial and temporal scales, the particular elements are
shaped by a mutual yet non-deterministic “co-evolutionary
dialogue”. In this dialogue, environmental changes will partly
be related to adjustments and adaptations that emerge within
the socio-economic systems in terms of altered institutions,
technologies, policies, perceptions and behaviours. However,
co-evolution does neither mean progress nor being well on the
way to sustainable resource management. Future sustainability is not preordained; it emerges from different co-evolutionary interactions and alternative beliefs about it. Although a coevolutionary approach has the ability to support the objectives
of sustainable natural resource management, further research
is necessary to develop stronger linkages between co-evolutionary theory and natural resource management.
18
E CO L O G I CA L E CO N O MI CS 63 ( 20 0 7 ) 9–2 1
To improve our understanding of sustainable resource
management systems a co-evolutionary framework seems to
be useful to focus future research. We argued that there is a
strong need to model co-evolutionary dynamics in natural
resource management systems to enhance stakeholder participation. Modelling co-evolutionary interactions across
scales helps to unravel complexity in natural resource
management systems and supports a shared contextual
understanding among stakeholders as base for dialogueoriented methodologies. Additionally, explorative and evidence-based research on the linkages between sustainability
and diversity and the related trade-off between efficiency and
adaptivity is needed.
Adopting a co-evolutionary perspective enables us to
capture dynamic patterns of human-environment interactions in resource management systems and supports interdisciplinary learning. Hence, the co-evolutionary approach
improves our understanding of the “co-evolutionary dialogue”
among the interlinked sub-systems and agents, but also
facilitates an iterative process of knowledge exchange between disciplines. This provides stakeholders with multidimensional, process-oriented and contextual knowledge about
the natural resource systems. The co-evolutionary approach
addresses complexity by (i) capturing qualitative change and
cross-scale interactions, (ii) refering to the whole spectrum of
uncertainty, and (iii) focusing on learning processes.
Acknowledgements
We thank Jeroen van den Bergh for inspiring discussions on
the paper's subject and John Gowdy for his comments on an
earlier version of this paper. We are also grateful to two
anonymous reviewers and their helpful comments.
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