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Gray, S. A., S. Gray, J. L. De Kok, A. E. R. Helfgott, B. O'Dwyer, R. Jordan, and A. Nyaki. 2015. Using fuzzy cognitive mapping as a
participatory approach to analyze change, preferred states, and perceived resilience of social-ecological systems. Ecology and
Society 20(2): 11. http://dx.doi.org/10.5751/ES-07396-200211
Research, part of a Special Feature on Exploring Social-Ecological Resilience through the Lens of the Social Sciences:
Contributions, Critical Reflections, and Constructive Debate
Using fuzzy cognitive mapping as a participatory approach to analyze
change, preferred states, and perceived resilience of social-ecological
systems
Steven A. Gray 1, Stefan Gray 2, Jean Luc De Kok 3, Ariella E. R. Helfgott 4, Barry O'Dwyer 2, Rebecca Jordan 5 and Angela Nyaki 6
ABSTRACT. There is a growing interest in the use of fuzzy cognitive mapping (FCM) as a participatory method for understanding
social-ecological systems (SESs). In recent years, FCM has been used in a diverse set of contexts ranging from fisheries management
to agricultural development, in an effort to generate transparent graphical models of complex systems that are useful for decision
making, illuminate the core presumptions of environmental stakeholders, and structure environmental problems for scenario
development. This increase in popularity is because of FCM’s bottom-up approach and its ability to incorporate a range of individual,
community-level, and expert knowledge into an accessible and standardized format. Although there has been an increase in the use of
FCM as an environmental planning and learning tool, limited progress has been made with regard to the method’s relationship to
existing resilience frameworks and how the use of FCM compares with other participatory modeling/approaches available. Using case
study data developed from community-driven models of the bushmeat trade in Tanzania, we examine the usefulness of FCM for
promoting resilience analysis among stakeholders in terms of identifying key state variables that comprise an SES, evaluating alternative
SES equilibrium states, and defining desirable or undesirable state outcomes through scenario analysis.
Key Words: bushmeat; fuzzy cognitive mapping; participatory modeling; resilience
INTRODUCTION
Over the last several years, considerable research effort has been
dedicated to understanding the drivers of change within social-
ecological systems (SESs) that can alter the system’s function to
the point where human well-being, conservation, or other
environmental management goals are compromised. These
research efforts have focused primarily on analyzing and
understanding the attributes governing these systems’ dynamics,
specifically those significant enough to shift the system into an
alternative regime (Walker et al. 2004). Understanding the
structure, defined dynamic relationships, and movement toward
or away from alternate regimes has been suggested as a starting
point to understand resilience and change across a range of SESs
(see Carpenter et al. 2001, Walker et al. 2002, Brooks and Adger
2004, Folke 2006, Füssel and Klein 2006, Gallopín 2006).
Although there are some variations in the literature with regard
to the definition of resilience (Brand and Jax 2007) depending on
the application in either an ecological (Holling 1973, Gunderson
and Holling 2002) or social (Adger 2000) system context, it is
generally considered to be the capacity of a system to experience
shocks while retaining a certain qualitative condition, including
the same identity, structure, functions, and feedbacks (Walker et
al. 2004).
Following the popularity of the resilience concept, new questions
have emerged regarding how the resilience paradigm can be put
into practice to support environmental management. A key issue
to resolve is the extent to which analytical methods that have been
commonly used to characterize and communicate SES change are
complementary to resilience analysis (Walters 1997, Gunderson
1999, Bennett et al. 2005). A considerable amount of research has
identified the nature of SESs generally, recognizing them as
dynamic, complex, adaptive, and uncertain systems with
feedbacks. This has led to the development of new modeling and
analytical approaches that explicitly incorporate surprises and
acknowledge the potential for a system to exist in multiple states
(Carpenter et al. 2002, Schwartz et al. 2011, Davidson et al. 2013)
as opposed to existing in a single and dominant equilibrium state
(Folke 2006).
Concurrent with this focus on incorporating complexity into
resilience analysis is a call to broaden participation of the actors
involved in environmental assessments (Walker et al. 2004).
Because many resource decision-making contexts are
characterized by low levels of controllability, high social and
ecological stakes, data poverty, and heterogeneity of social agents,
new modeling methods that support resilience must be informed
by or constructed with stakeholder input. Additionally, these
approaches should be flexible and able to be revised as new
information about the system becomes available (Holling 1973,
Walters 1986, Gray et al. 2014a). Such adaptive management
approaches are expected to protect against self-reinforcing and
inflexible decision making (so-called “rigidity traps,” see
Carpenter and Brock 2008). These inclusive approaches should
provide opportunities to incorporate not only stakeholder beliefs
to understand the perceived structure of the system (Gray et al.
2012), but also social values and preferences so that goal(s) for
management and attributes that are valued about/within the
system can be identified (Lynam et al. 2007).
These emerging demands for incorporating complexity and
stakeholder knowledge have led to a variety of qualitative and
semiquantitative techniques for perceived resilience assessment
(UNECE 1998, Bennett et al. 2005, Cumming et al. 2005, Fletcher
et al. 2006, Kearney et al. 2007, Kok 2009, Fuentes 2012) that
have been facilitated by significant growth in the field of
1University of Massachusetts, School for the Environment, 2Coastal & Marine Research Centre, Environmental Research Institute, University
College Cork, 3VITO NV, Flemish Institute for Technological Research, 4Environmental Change Institute, University of Oxford, 5Rutgers
University, Department of Human Ecology, 6University of Hawaii Manoa, Department of Natural Resources and Environmental Management
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participatory modeling (Sandker et al. 2010). Voinov and
Bousquet (2010) outline two major objectives that drive
participatory modeling: (1) to increase and share knowledge and
understanding of a system and its dynamics under various
conditions and (2) to identify and clarify the impacts of solutions
to a given problem. Although there has been a recent increase in
participatory tools and software available to environmental
managers, some critics have cautioned that the diversity of
modeling practices does not necessarily indicate diversity in
function because new stakeholder-driven modeling programs are
often prone to duplication of efforts (Jones et al. 2008).
Even though norms for understanding the characteristics of SESs
have been established and the development of participatory
modeling approaches has increased during the last decade, the
trade-offs between modeling tools in terms of ease of use with
stakeholders, model inputs, and outputs are only recently
becoming understood. Scholars in the field have lately begun to
review the strengths and weaknesses of different participatory
approaches (Lynam et al. 2007, Sandker et al. 2010, Voinov and
Bousquet 2010); however, this work is rarely explicitly linked to
the concepts used in resilience assessments, despite some notable
recent efforts (see Ross and Berkes 2014).
To contribute to this discussion, we compare how one specific
participatory modeling approach, fuzzy cognitive mapping
(FCM), can be explicitly used to support the resilience framework,
specifically with regard to incorporating the valuable knowledge
held by stakeholders. We use a case study for bushmeat hunting
in Tanzania to demonstrate the value of FCM in collecting and
standardizing the perceptions of stakeholders to identify and
analyze key state variables. Further, by using the capacity of FCM
to support semiquantitative scenario analyses, we illustrate the
relationship between the current and projected equilibrium states
of the bushmeat trade system and their relationships to desired/
undesired state outcomes under current pressures and various
potential management actions.
FUZZY COGNITIVE MAPPING
Originally developed by Kosko (1986) as a semiquantitative and
dynamic method to structure expert knowledge, FCM has
historical roots in cognitive mapping (Axelrod 1976). Similar to
other cognitive maps, FCMs are graphical representations of a
system that visually illustrate the relationships or edges between
key concepts, or nodes, of the system, including feedback
relationships. The relationships in a structural map are logically
defined by connecting concepts through semantic or otherwise
meaningful directed linkages (Novak and Cañas 2008). The
justification for representing cognition by means of structural
maps is derived from constructivist psychology (Gray et al.
2014b), which suggests that individuals interactively construct
knowledge by creating internal associative representations that
help catalogue, interpret, and assign meaning to environmental
stimuli and experiences (Raskin 2002). Knowledge constructed
in this manner can externally represent the foundation of an
individual’s organized understanding of the workings of the
world around him or her. Therefore, cognitive maps can be
considered external representations of internal mental models
(Jones et al. 2011). Individuals assimilate external events and
accommodate information into these mental model structures to
facilitate reasoning and exchange understanding (Craik 1943,
Flavell 1996, Lerner 1998). Using this theoretical framework,
cognitive maps can be elicited to represent an organized
understanding of a general context or domain, thereby providing
an illustrative example of a person’s internal conceptual structure
of the issue in question (Novak and Cañas 2008).
FCMs build on these notions and are highly structured and
parameterized versions of cognitive maps that represent direct
and indirect causality by combining aspects of fuzzy logic, neural
networks, semantic networks, and nonlinear dynamic systems
(Glykas 2010) in influential diagrams. Because FCMs are based
on cognitive mapping and are semiquantitative, they can be
considered representations of mathematical pairwise associations
using qualitatively, e.g., low, medium, or high, or quantitatively
assigned weighted edges between -1 and 1 among variables that
collectively constitute a representation of a particular domain
(Wei et al. 2008). These pairwise relationships allow computation
of the cumulative strength of connections between the elements
with weighted edges, highlighting any domain as a system (see
Fig. 1). Typical strengths of FCMs are the simple algorithms used,
transparent representation of system feedback structure, and the
possibility to mathematically average or weight different FCMs
from several individuals, e.g., scientific and local experts, or
domains, e.g., natural and social sciences, into one model.
FCMs have been used in a number of disciplines to indicate
relationships among variables as well as to understand and
communicate system dynamics. The applications of FCM can be
categorized in terms of the type of knowledge being represented
in the cognitive map and the perceptions they reflect (Gray et al.
2014b). Broad disciplinary categories include traditional scientific
experts (Hobbs et al. 2002), engineers (Amer et al. 2011),
physicians (Benbenishty 1992), and local experts including
pastoralists (Ortolani et al. 2010, Papageorgiou and Kontogianni
2012, Halbrendt et al. 2014), fishermen (Mackinson 2000, Wise
et al. 2012), environmental managers (Gray et al. 2013, 2014c),
and groups of several environmental stakeholders as a way to
facilitate shared decision making (Özesmi and Özesmi 2004,
Kafetzis et al. 2010, Gray et al. 2012, Meliadou et al. 2012,
Papageorgiou and Kontogianni 2012, Jetter and Kok 2014).
Fig. 1. An example of a simple fuzzy cognitive map (FCM),
illustrating weighted edge relationships (-1, 1) between system
elements A, B, C, and D.
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We focus on the use of FCM as a means of encoding and
aggregating stakeholder and/or expert knowledge into a
standardized format, thus allowing a broad range of knowledge
types to be integrated and communicated in pursuit of SES
resilience assessments. These external representations provide a
tangible way to allow knowledge claims regarding the structural
and functional identity of the system subjected to management
to be debated (Özesmi and Özesmi 2004, Amici et al. 2010,
Wildenberg et al. 2010). Further, the application of FCM in a
participatory modeling context provides an adaptable method to
support existing resilience assessment frameworks previously
outlined in the literature (e.g., Walker et al. 2002). We suggest that
FCM can be used to understand change and transition in SESs
by (1) sharing knowledge to define the state space of a given SES,
(2) analyzing the structure of an SES, (3) analyzing SES functions
through scenarios, and (4) evaluating how changes to structural
configurations may relate to movement toward or away from
desirable or undesirable future trajectories (Fig. 2).
Fig. 2. Mapping the participatory fuzzy cognitive mapping
(FCM) approach to social-ecological system (SES) resilience
assessment that we propose to the framework put forward by
Walker et al. (2002).
Constructing FCM based on shared community knowledge to
define the state space
FCMs also have been proposed as a unique tool for aggregating
diverse sources of knowledge to represent a scaled-up version of
individuals’ knowledge and beliefs (Özesmi and Özesmi 2004).
The product of the aggregation of individual FCMs is sometimes
referred to as a social cognitive map and is considered to be a
representation of shared knowledge (Özesmi and Özesmi 2004,
Gray et al. 2012, 2014c). The concept of shared knowledge in the
form of social cognitive maps has been used for a variety of
purposes: to gain a more comprehensive understanding of
complex systems, to describe consensus in knowledge among
individuals, and to define differences in individual and group
belief or knowledge structures. By applying FCM to understand
change within an SES, we focused on engaging in community-
generated modeling activities via workshops with a range of
stakeholders to generate a working model of the salient social
and ecological components that constitute a resource system in
relation to environmental change. Such definitions of the
variables that are contained within the perceived boundaries of a
system lend themselves to the idea of defining the state space in
resilience analysis (Walker et al. 2004). This is the
multidimensional state within which all combinations of the
defined variables can exist. These definitions of what constitutes
the state space of an SES are the components/variables that exist
within a given space, e.g., a protected area, or are required for a
system to have a given function, e.g., international timber trade.
Additionally, the relationships between state space variables that
are defined in terms of degrees, e.g., low, medium, or high, of
positive or negative influence together represent the networked
structure of a system.
Analyzing FCM structure
Because FCMs are derived from graph theory and are
semiquantitative, the static structure between state space variables
can be represented in mathematical terms (Table 1). These
structural measures are determined by translating the cognitive
map into an adjacency matrix and translating the positive or
negative values that define relationships between variables on a
scale between +1 and -1 (Table 1). Representing the structural
relationships of these concepts in a matrix allows each variable
included in a model to be categorized in one of three ways: as a
driving variable, i.e., forcing component; receiving variable, i.e.,
impacted component; or ordinary variable, i.e., intermediate
component (Nyaki et al. 2014). A variable’s relative importance
for the system can be determined by the strength of its incoming
and outgoing edge relationships relative to those of other
variables via centrality measurements common to network
analyses (see Özesmi and Özesmi 2004). FCMs can also be
characterized by a range of other quantitative metrics allowing
comparison of one model with another by measuring the general
structure of the model, including dimensions like complexity and
density (see Gray et al. 2014b for a review of structural metrics).
Table 1. Adjacency matrix derived from the fuzzy cognitive map
of Figure 1.
A B C D
A 1 0.5 -0.5 0
B 0 0 0 0
C 0 -0.5 0 0
D 0 0 1 0
Analyzing FCM dynamics: current basin of attraction
In addition to defining the state space and the structured
relationship between variables, the results of dynamic interactions
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between structured components within a domain can be
determined using matrix calculations through FCM scenarios.
The output of an FCM’s adjacency matrix is calculated using
matrix algebra over a series of iterations to illustrate its baseline
scenario, i.e., a representation of the steady state of the system
(Kosko 1986, 1994). The steady state of a system is
complementary to the resilience concept of a basin of attraction
(Walker et al. 2004). This provides a snapshot of how the variables
and linkages of the system given the current SES configuration
would resolve themselves in the absence of change or intervention,
with all feedback loops played out:
(1)
where Ai
(k+1) is the value of factor Vi at iteration step k+1, Ai
(k)
is the value of factor Vi at iteration step k, Aj
(k) is the value of
factor Vj at iteration step k, and wji is the weight of the edge
relationship between Vi and Vj. Threshold function f (e.g., logistic
or sigmoidal function) is used to normalize the values at each step
to keep the dynamic analysis bounded. This initial state of the
system, calculated based on the network structure and defined
influences between variables, indicate the region in state space in
which the system tends to remain (Walker et al. 2004) without
significant changes to any state space variable.
Analyzing FCM dynamics: alternative stable states
Inferences may be drawn regarding the dynamic attributes of the
system as modeled by analyzing the scenario output of an FCM
(Özesmi and Özesmi 2004). Analysis of the scenarios can either
focus on the equilibrium end states, if present, or the transient
behavior during the iteration steps. “What if” scenarios help
explore how the system might shift into another set of equilibrium
points within the same basin of attraction, or slip into an
alternative regime under a range of possible conditions as
variables included in the state space are artificially changed. This
is accomplished by increasing or decreasing (referred to as
“clamping”) key variables as continually high or low (Kosko 1986,
1994), resulting in a new system state that can be compared with
the steady state.
The persistence of a system’s identity in the face of disturbance
has been suggested as a useful measure of resilience (Cumming
et al. 2005). Therefore, by comparing current basins and
alternative equilibrium states, it is possible to characterize a
system’s current identity and determine the scale of disturbance
it can endure while maintaining a certain output (Kok 2009). Such
assessments draw on the concept of “stability landscapes”
described by Walker et al. (2004) to describe the transition
between alternative equilibrium states within a basin.
Reviewing equilibrium points and defining desirable and
undesirable states under different scenarios
In addition to understanding the structure and function of SESs,
the modeling process itself, i.e., developing an FCM with
stakeholders, has also helped policy makers frame regulations in
a manner responsive to the needs and terms of stakeholders and
maximize stakeholder buy-in of experimental policy measures
(Özesmi and Özesmi 2004). Murungweni et al. (2011) further
emphasize the potential of the FCM modeling process to form
strong links of communication and trust between stakeholders,
researchers, and policy makers.
To date, however, less attention has been paid in the literature to
defining desired and undesired states of an SES using FCM based
on the perceived system components included in a model. To add
to the discussion, we suggest that all concepts included in an FCM
that are thought to be important to the composition of state space
(Walker et al. 2004) can be designated as existing in one of three
states. Thus, stakeholders can indicate a preference that a concept
is increasing, preference that a concept is decreasing, or showing
no preference. Defining preferred states establishes system
desirability in the face of external or internal pressures. Further,
this explicit approach establishes a qualitative basis for
understanding the system’s identity, to which its current basin of
attraction or alternative equilibrium state under a scenario can
be compared (Walker et al. 2004).
CASE STUDY
To highlight the conceptual and analytical linkages between FCM
and resilience analysis, we present case study data modeling
changes to wildlife populations and community well-being in
relation to increasing immigration to the area. The data were
collected from a local-expert workshop in a village that borders
the Serengeti National Park in Tanzani.
Wildebeest and zebra populations and bushmeat consumption in
villages near the Serengeti National Park
An increase in bushmeat hunting in protected areas poses great
risks to many threatened and endangered species. The cumulative
impacts of extreme poverty, advances in hunting techniques, and
increased human populations near protected areas have
heightened the demand for bushmeat and significantly
endangered wildlife populations (Knapp 2012, Rentsch 2012,
Nyaki et al. 2014). Although the term “bushmeat” refers to the
hunting and consumption of all wildlife, on-going and extensive
bushmeat hunting has been particularly damaging to the endemic
biota of Africa, causing some species to be classified as threatened
or endangered (Ndibalema and Songorwa 2008, Mfunda and
Røskaft 2010) and resulting in hunting restrictions and even
moratoriums for many species.
The high profit returns from selling bushmeat on the black market
and low capital investment of bushmeat hunting have attracted
significant inbound migration of human populations to areas
close to the borders of protected areas. For example, the human
population living along the western edge of Serengeti National
Park is increasing at an average rate of 2.9% per year (Ndibalema
and Songorwa 2008, Knapp 2012, Rentsch 2012). Of the
approximately one million people estimated to live along the
borders of the protected areas of the Serengeti National Park,
between 52,000 and 60,000 people are estimated to engage in
illegal hunting (Loiboki et al. 2002, Knapp 2012), a number that
is predicted to increase in line with population growth. In
addition, increasing human populations alongside protected
areas not only impact wildlife directly through predation of
bushmeat species, but also indirectly by way of resource
competition, diminution of wildlife habitat, encroachment, and
increases in human-wildlife conflicts such as crop destruction
from wildlife and disease transmission (Estes et al. 2012).
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To define the perceived state space of the community in relation
to hunting in the protected area, the current basin of attraction,
and anticipated changes to system states based on the
perceptions of local stakeholders within the system, we present
data collected from a participatory modeling workshop with 15
zebra and wildebeest hunters, bushmeat consumers, and
bushmeat sellers in one village near the Serengeti National Park
in Tanzania. Individuals who participated in the workshop were
nominated by a larger group from the local community based
on their individual expertise (see Nyaki et al. 2014). Through
extensive discourse and collaboration, participants defined the
structure of the system and defined its preferred state identity.
After data were collected, the model was analyzed for shifts
toward or away from this identity under stakeholder-defined
scenarios to understand its current and future trajectories.
Step 1: Modeling bushmeat consumption and production in
Tanzania using FCM
A workshop was held in coordination with an international
nongovernmental organization working to develop policies to
reduce bushmeat hunting in communities near protected areas.
This workshop was facilitated by an academic researcher who
also had connections to local community members. During the
workshop, an unrelated FCM of an agricultural system was
used to lead discussions about how to generate a model.
Two central concepts were provided to guide FCM construction:
bushmeat consumption and zebra/wildebeest populations.
Workshop participants then participated in a brainstorming
session to identify other components that were important to be
included in the model. They began to structurally link these
components using directed arrows and qualifying the degree of
influence among these connections using these descriptors:
high, medium, and low positive and high, medium, and low
negative. The final model defined a state space consisting of 22
components with 52 connections between the components (Fig.
3). The components perceived as most important to the system,
i.e., those with the highest centrality scores, were (1) poaching,
(2) increased human population, (3) poor crop harvest, (4) crop
destruction, and (5) bushmeat market demand.
After the model was developed, workshop participants
discussed the preferred state of the system through deliberation
until agreement was reached. This was accomplished by
identifying their opinions about each component of the model
in terms of whether they held a preference that a concept would
increase (e.g., income, crop availability), preference that a
concept would decrease (e.g., disease), or indicated no
preference either way (e.g., hunting regulations). This evolved
into a representation of the preferred state identity that was
considered as desirable.
Step 2: Scenario analysis: current basin of attraction
After the workshop participants were satisfied with their model,
a photograph of the model was taken so it could be translated
into an adjacency matrix for scenario analyses (Özesmi and
Özesmi 2004). Further, the model was entered into the FCM-
based software Mental Modeler (see http://www.mentalmodeler.
org, Gray et al. 2013) for scenario analyses (Fig. 4).
Subsequently, the steady state was calculated, indicating the
current basin of attraction given the parameters defined
between components by the workshop participants (Fig. 5).
Fig. 3. Photograph of fuzzy cognitive mapping (FCM)
constructed in the workshop by the community that indicates
conceptual links between zebra and bushmeat populations and
bushmeat consumption.
Step 3: Resilience analysis: identifying drivers of social-
ecological change
During the workshop, participants identified increased human
population as being the most concerning change to environmental
conditions affecting their village. The community model was then
subjected to a scenario in which the concept of increased human
population was “clamped” as high (see Kosko 1986 for a
description of FCM scenario analysis). The outcome of this
scenario was compared with the baseline steady-state scenario to
understand relative change of state space variables under this
condition (see the Increased Human Population scenario, Fig. 6).
This scenario provided a useful illustration of how the system
might settle into an alternative equilibrium state, in terms of the
relative change of components included in the FCM, as human
population increases. This output was compared with the
preferred state conditions of each component to determine the
level of desirability of the SES state that results as human
population increases.
Step 4: Management/adaptation option evaluation
To understand how management plans might mitigate unwanted
outcomes and influence the level of desirability of a system under
the scenario of increased human populations, two new variables
were added to the state space. The added variables were
establishing (1) community wildlife management (CWM) and (2)
hiring community engagement officers to serve as liaisons
between the CWM and the local community. The two new
variables represented possible policy actions that might mitigate
some unwanted impacts associated with the increasing human
population; they were added to the community model by defining
their structural relationships and relative influences on other
components (Figs. 7 and 8).
Once these new components were added to the community model,
the increasing human population scenario was run again along
with the added strategies meant to mitigate its unwanted impacts
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Fig. 4. Screenshot of community model entered into fuzzy cognitive mapping (FCM) software Mental Modeler. Blue lines indicate
positive relationships and orange lines represent negative relationships between components. Line thickness indicates the degree of
influence between components. CWA indicates community wildlife management area; SRCP, a local microcredit lending program.
(see Increased Human Population plus Mitigation, Fig. 6) to
understand how these new strategies or configurations might
influence alternative equilibrium states. All scenarios were then
compared to understand (1) system states under pressures and (2)
the stability landscape under pressure and mitigation plans (Fig.
6). After the workshop, these scenario outputs were compared by
researchers to understand how scenario states compared to
stakeholder-defined preferred states identified during workshops.
Results
Of the 22 components included in the model, workshop
participants showed preference for 15 components as increasing
or decreasing (Table 2). Steady-state analysis indicated that in
comparison to the desired state, the current basin of attraction
led to a state in which 33% of the state variables would exist in a
favorable state, in general terms of positive or negative, including
income, availability of food and water for animals, suitable
wildlife habitat near villages, employment, and a small positive
increase in zebra and wildebeest populations. The steady-state
analysis also indicated that 40% of the variables included in the
model existed in a state that was counter to the preferred state;
one of these was poaching, which showed a substantial increase
under status quo conditions. Additionally, poor crop harvests
increased, which participants preferred to see decrease, while
sufficient rainfall decreased, which participants preferred to see
increase. There was no change, either positively or negatively, for
the remaining four variables for which participants indicated
preference.
Scenario results indicated that when human population increased,
74% of the 15 variables that constituted the preferred state showed
a trajectory away from a desired state (33%) or no change (41%),
whereas 26% indicated a trajectory toward a preferred state.
Under this scenario, the state variables that maintained a
preferred state were a decrease in crop destruction by vermin, a
decrease in poor crop harvests, and an increase in availability of
food and water and habitat near villages. When management plans
were added to the scenario of increased human populations for
comparison, 67% of the components indicated a shift toward a
preferred state, although the influence of the proposed
management action was not distributed evenly. Specifically, only
income and participation (13%) shifted from negative values to
positive values, an indication that these components were
perceived to be most affected by the management action and to
foster a more preferred state of the system by crossing a threshold
from an undesired state to a desired state.
DISCUSSION
Although the focus of this research was to align FCM explicitly
with participatory forms of resilience analysis, it is important to
note that a growing number of participatory planning and
modeling approaches are available to understand social-
ecological dynamics (Lynam et al. 2007). Examples include
narrative scenario planning (Swart et al. 2004), concept mapping
(Harr et al. 2014), Bayesian belief networks (Aalders 2008), and
agent-based modeling (Janssen and Ostrom 2006), among others.
Although many of these tools have the potential to support
different dimensions of resilience analysis, there are trade-offs to
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Fig. 5. Scenario analysis of steady state that indicates the current basin of attraction for state space variables under status quo
conditions independent of environmental or social change. BM indicates bushmeat; CWA, community wildlife management area;
FCM, fuzzy cognitive mapping; SRCP, a local microcredit lending program.
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Table 2. Comparison of scenario analyses and preferred state of the social-ecological system.
Component included in the community
model
Desired Change Scenario: Increased
population
Scenario:
Increased population plus
mitigation
Desired Change Achieved
(Yes = 1, No = 0)
Lack of conservation education Decrease 0 -0.06175 1
Decreased participation in a local
microcredit lending program
Decrease 0 0 0
Unplanned wildfires Decrease 0 0 0
Drought Decrease 0 0 0
Crop destruction by vermin Decrease -0.02087 -0.00259 1
Poor crop harvest Decrease -0.00413 -0.00066 1
Poaching Decrease 0.01487 -0.00066 1
Income from tourism Increase 0 0.06218 1
Sufficient rainfall Increase 0 0 0
Increased community wildlife management
participation
Increase -0.05059 0.01158 1
Availability of food and water Increase 0.00159 0.01195 1
Habitat near villages Increase 0.05055 0.06175 1
Employment Increase -0.05022 -0.05022 0
Wildebeest and zebra populations Increase -0.10509 -0.02756 1
Income Increase -0.03719 0.03264 1
Bushmeat price lower than beef/chicken/
fish
Neutral 0.04727 0.04389 -
Cultural preference Neutral -0.01055 0.00303 -
Predation Neutral -0.00140 0.00335 -
Bushmeat market demand Neutral 0.03724 0.03585 -
Law enforcement Neutral -0.01170 0.02750 -
Bushmeat consumption Neutral 0.01112 0.01487 -
Income from bushmeat demand Neutral 0 0 -
consider. As these approaches become more mainstream, it is
important to select participatory modeling methods based on the
community involved in the modeling process, research questions
or management goals, and how each tool differs across
dimensions such as ease of use with stakeholders, model inputs
and outputs, and the degree of spatial or temporal extent.
Although the theory behind each of these methods continues to
develop along with new methodological and technological
advances, we provide a general overview of the some of the
strengths and weakness of different approaches (Table 3).
For example, certain methods may be more or less amenable to
different groups involved in the modeling process based on the
amount of training required to create and analyze a model.
Although narrative scenario analysis and qualitative concept
mapping lend themselves to use across a wider range of
communities because they are more flexible than semiquantitative
approaches, the output of these models is often not dynamic,
limiting their ability to be used to evaluate competing system states
through post hoc analyses. Additionally, although most methods
to varying degrees allow stakeholders and scientists to define the
concepts, components, or variables that constitute the state space
of the system modeled, some methods are more flexible in terms
of the types of relationships that can be defined between variables.
FCM and agent-based modeling, for example, can represent
feedback relationships between variables, whereas Bayesian belief
network relationships are unidirectional. Further, although all
SESs modeled through these efforts are defined in terms of time
and space to some extent, the degree to which model outputs can
be interpreted in spatial or temporal units by stakeholders varies
and thus may influence analytical abilities to draw meaningful
conclusions that facilitate management action. When considered
together on a spectrum, as tools transition from more flexible and
qualitative to more parameterized and semiquantitative, ease of
stakeholder use decreases along with the ability to explicitly
evaluate competing system states. Further, although semiquantitative
approaches may provide a wide range of opportunities for post
hoc analysis, they may limit the degree to which stakeholder values
and knowledge are integrated into model-based assessments.
It is also important to highlight that although there has been a
dramatic increase in the use of FCM across multiple scientific
fields (Papageorgiou and Salmeron 2013), even proponents of the
method have begun to identify some analytical weaknesses in the
approach. In fact, several of the methodological shortcomings
that have recently been identified may present significant issues
when attempting to model and analyze the complexity found in
many SESs. For example, in their review Papageorgiou and
Salmeron (2013) indicate that FCMs are limited in their ability
to model time delays with regard to the interactions between nodes
and are limited to defining linear relationships within a system.
Additionally, they point out that FCM dynamics are of the first
order; that is, the next system state depends on the previous one,
and therefore the approach does not deal well with the
randomness associated with many complex domains. Because
SESs often, if not always, include nonlinear relationships,
thresholds at which system states can change significantly are
prone to surprises that are at times dramatic and, by definition,
unexpected (Carpenter et al. 2002, Schwartz et al. 2011, Davidson
et al. 2013) it is clear that FCMs are a useful “quick and dirty”
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Table 3. Comparison of participatory modeling as they relate to participatory resilience modeling.
Modeling
Approach/
Tool
Ease of use
with
stakeholders
Model input collected
from stakeholder
Model outputs
presented to
stakeholders
Spatial or
Temporal
Strengths in participatory
social-ecological system
(SES) resilience analysis?
Weaknesses in participatory
SES resilience analysis?
Narrative
scenario
analysis
High Focus group discussions;
envisioning future states
under parameterized
conditions; system
components defined
Alternative
system states,
usually
qualitatively
defined
Temporal Stakeholder driven, less
constrained and highly
flexible given stakeholder
priorities
Scenario outputs are often
constrained to the contexts
where data are collected.
Qualitative output often
must be translated into
quantitative or
semiquantitative format for
additionally model coupling
Qualitative
Concept-
mapping
High Concepts/system
components and
associations/
relationships between
components defined
System structure,
static qualities
and
characteristics of
the system
Neither Provides representation of
a problem space and the
associations and
characteristics of the
problem space
Static and therefore not
suitable for scenario
analysis or evaluation of
dynamic or emergent
properties
Fuzzy
cognitive
maps
Medium to
High
Concepts/system
components, structural
relationships between
concepts or components;
sign and strength of
causal influence between
concepts
System structure
and system
states, sensitivity
for changes in
system structure
Neither Allows for feedbacks;
system components and
relationships easily added
or removed. Often intuitive
since based on concept
mapping; problem-
structuring with
stakeholders
Model outputs not linked to
discrete values; nonlinear
relationships difficult
include; determining
consensus on components
and relationships takes time
Bayesian
belief
networks
Medium to
High
System components;
unidirectional
relationships between
components defined
based on probability
estimates
Probabilistic or
conditional
system states
Neither Often intuitive since based
on concept mapping. Real-
world probabilities can be
assessed for model validity;
deals with uncertainty
No feedbacks included;
determining consensus on
components and
probabilities may take time
Agent-based
models
Low Types of agents, rules of
behaviors for agents;
initial state conditions,
validation of the model
Aggregate-level
system behavior,
system states
Both Allow for feedbacks;
model parameters easily
changed. Discrete units
that reflect real-world
values can be modeled;
handles non-linearity
Agent types not easily
changed; not flexible in
participatory setting since
models are usually
constructed before
stakeholder workshops
and indeed “fuzzy” participatory approach that is most
appropriate as a way to promote social learning and deliberation
among diverse stakeholders and not as a formal assessment tool.
The method would benefit from further development, including
new analytical techniques, novel scenario algorithms that attempt
to account for complexity, and additional empirical assessments
that identify the social or ecological conditions that are more or
less well suited for the use of FCM.
Directions for future FCM research
A number of directions regarding FCM should be explored in the
future. Given the extent to which FCM allows for different kinds
of information to be integrated into the same model, the process
we describe can be used to gather multiple forms of evidence to
validate perceived understanding through adaptive management.
In the model-building process, not only do participants develop
structural understanding of a complex system subjected to
management, but through deliberation, they also discuss
uncertainty. Such conversations can be used to suggest points for
which further evidence is needed and can allow participants to
determine what and how data can be collected to validate
perceived dynamics empirically (Gray et al. 2014a). The notion
of citizens participating as data collectors and decision makers is
not new. However, because modern science is often seen as a solely
expert-driven endeavor, lay individuals and more traditional
knowledge forms often can be marginalized. Although some have
argued that scientific reasoning is innate (Caruthers 2002), several
examples of “citizen science” have yielded reliable evidence (e.g.,
Bonney et al. 2009, USA-based cases). We suggest that
participatory modeling, specifically FCM, may extend the
benefits of public participation in scientific research identified
recently (Shirk et al. 2012). If deemed relevant, additional
information in dynamically managed systems serves to directly
improve model development as well as informed, direct resource
management on the part of all stakeholders. Furthermore, the
sensitivity of the scenarios to changes in the edge weights could
be evaluated against the expectations of knowledge experts and
stakeholders to correct or improve the FCM structure. However,
to date, these appropriations of FCM are largely unexplored.
It is important to note that in participatory settings, FCMs are
constructed based on perceived dynamics of a system; therefore,
scenario analyses provide an understanding of perceived
resilience measurements rather than empirical resilience
measurements. However, we suggest that individuals can use
specific data collection protocols validate not only the structural
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aspects of the model but also the model’s predictions. Such
interplay between the conjectured and the actual outcomes will
allow for informed model refinement as well as provide a platform
by which individuals can systematically test adaptive steps in the
management process. In other words, as individuals ground-truth
elements of their models, either by local measures or through
available measures of greater spatial/temporal scope, they can test
the underlying causal links between elements by running
subsequent scenarios after new data/evidence forms are
integrated.
Lastly, although comparisons between the preferred state, current
steady state, and different scenario states provide useful
benchmarks for discussion with stakeholders, determining
conditions under which the system slips from one basin of
attraction into another basin is not straightforward. Given the
highly subjective nature of how the state space and preferred state
are identified, whether the qualitative identity of an SES is
maintained under scenarios is largely unclear and represents an
area of research that would benefit from additional study. Based
on our case study, when the steady-state condition was compared
with the human population scenario, 40% of the components
shifted from either positive or zero values to negative values
independent of participant preference. This is an indication that
the SES, given its current configuration, is perhaps not resilient
to this particular type of disturbance; however, the importance
of individual state-space variables was not determined and it is
likely that some variables, e.g., wildebeest and zebra populations,
likely contribute more to a system’s identity than others. Further,
when mitigation plans were added to the model, in some cases the
values did change significantly, e.g., income and participation,
which indicated that perhaps the management measures were
more effective in adapting to a new regime and mitigating
inevitable system responses, as opposed to building capacity
within the system to maintain itself in its steady state. We suggest
that researchers engaged in participatory modeling, resilience
analysis, and FCM begin to develop new ways of measuring
system identity. This may be accomplished by combining aspects
of more qualitative approaches, i.e., narrative scenarios, with
semiquantitative approaches iteratively, drawing on unique
aspects of each in the participatory process.
CONCLUSION
The FCM approach described here has resulted in shared
participation in management decision making. Not only do
FCMs provide the opportunity to help stakeholders participate
in management decisions, they can also facilitate the discourse
with governing agencies, players in the management outcomes,
and nonstakeholders seeking to understand the case. Because the
process of developing an FCM requires understanding a few
relatively lay terms and following simply logical heuristics, the
resulting models tend to be transparent and, with explanation,
can convey great meaning without cumbersome or jargon-laden
text. Furthermore, because FCM allows for the integration of
preferences and values, connections and outcomes can be judged
relative to inputs. The latter can be favorable when disagreements
about values and preferences halt discourse and negotiation.
Finally, although more work using this approach is warranted,
the possible benefits of using cognitive models to co-construct an
explanatory model by which predictions and subsequent actions
can be supported underscore the added value of FCM for
bridging the gap between qualitative and quantitative approaches
in participatory resilience assessment and ultimately resource
management.
Responses to this article can be read online at:
http://www.ecologyandsociety.org/issues/responses.
php/7396
Acknowledgments:
We would like to thank CREATE (Conservation Research for East
Africa's Threatened Ecosystem) who funded the case study
presented in this research. CREATE is funded by FZS (Frankfurt
Zoological Society) and the EU (European Union).
Complimentary funding was provided by IFP (Ford Foundation
International Fellowships Program). Additionally, we would like to
thank the United States Department of Agriculture and the
National Science Foundation for funding development of the
Mental Modeler software. Lastly, we would like to thank the
reviewers for constructive feedback that improved our manuscript.
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