Manual for applying Fuzzy Cognitive Mapping
Transcript of Manual for applying Fuzzy Cognitive Mapping
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Project no. GOCE-CT-2003-505298
ALTER-Net
A Long-Term Biodiversity, Ecosystem and Awareness Research Network
Manual for applying Fuzzy Cognitive Mapping – experiences from ALTER-Net
Kirsten G.Q. Isak, Martin Wildenberg, Mihai Adamescu, Flemming Skov, Geert De Blust and Riku Varjopuro
Deliverable type: Report
WPR6-2009-02 - Deliverable 4.R6.D2
Instrument: Network of Excellence
Thematic Priority: Global Change and Ecosystems (Sub-priority 1.1.6.3, Topic 6.3.III.1.1)
Due date of deliverable: May 08
Submission date: April 09
Start date of project: 1st April 2004
Duration: 5 years
Deliverable lead contractor: NERI
Revision: 1.0
Work Package: R6
WPR6-2009-02
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Manual for applying Fuzzy Cognitive Mapping – experiences from ALTER-Net
Authors
Kirsten G.Q. Isaka, [email protected], Martin Wildenbergb, [email protected], Mihai
Adamescuc, [email protected], Flemming Skova, [email protected], Geert De Blustd,
[email protected] and Riku Varjopuroe, [email protected]
a) National Environmental Research Institute, Aarhus University, Denmark.
b) Institute of Social Ecology, University of Klagenfurt, Austria.
c) Department of System Ecology, University of Bucharest, Romania.
d) Research Institute for Nature and Forest, Belgium.
e) Finish Environment Institute, Finland.
Introduction
This paper outlines how Fuzzy Cognitive Mapping (FCM) can be applied as a tool in
nature conservation. The paper is based on the experienced gained in case studies using
FCM conducted in ALTER-Net from fall 2007 to spring 2009. The results from the case
studies, and how these results can be used in nature conservation, will be presented in a
separate paper, expected to be published in 2009.
Presentation of Fuzzy Cognitive Mapping
Fuzzy cognitive mapping (FCM) is a soft
systems methodology that consists of a
number of variable concepts and connections
which illustrate the cause and effect relations
between the concepts (Kosko 1986). Figure
1 illustrates a simple fuzzy cognitive map
which shows a perception of biodiversity in
a forest. Two concepts affect the
biodiversity, namely the cultivation of the
forests and the amount of water in the forest.
The latter is also perceived to be affected by
the cultivation of the forest. The biodiversity
it self, is affecting how important the forest is for people.
Biodiversity in a forest
Intensive cultivation
of the forestry
Importance of the forest in
peoples mind
Amount of water in the forest
negative
negativepositivepositive
Figure 1: An example of a fuzzy cognitive map describing a forest in a simplified manner. The maps contains of four elements: Biodiversity in a forest, Intensive cultivation of the forest, Amount of water in the forest and Importance of the forest in the peoples mind. The four elements are linked through positive and negative effects on each other.
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A cognitive map like a fuzzy cognitive map is a visual presentation of a system e.g. a complex
and dynamic problem. It describes the central factors and their causal relations as 'a directed
graph'. (Aguilar 2005; Giordano et al. 2005; Hobbs et al. 2002; Mendoza & Prabhu 2006; Özesmi
& Özesmi 2004). As the map tries to depict a complex system, the method tries especially to help
understanding feedbacks and long causal chains between the factors.
In a fuzzy cognitive map, the causal relationships between the factors are always given a negative
or positive value (see figure 1). A polarity (+ or -) of a causal relationship explain whether a factor
is increasing or decreasing the factors that it influences. For instance, the figure 1 tells us that
when there is more intensive agriculture in the forest the amount of biodiversity in the forest
decreases and also that amount of water decreases. Furthermore, as the amount of water has a
positive influence on the biodiversity, increase of intensive cultivation leads to a double effect on
biodiversity – directly and mediated through amount of water. One must notice a positive or
negative causal relationship tells whether the factors develop to the same or opposite directions. A
positive relationship from amount of water to biodiversity, means that when there is more water
there is more biodiversity and similarly: when there is less water, there is less biodiversity. The
figure 1 describes a system in which biodiversity does not influence the amount of water at all,
because causal relationship is only from water to biodiversity. Therefore, a decrease in
biodiversity in the forest would not have any impact on the amount of water.
A positive or negative relationship in a fuzzy cognitive map does not tell whether the change is
for good or bad even though the figure 1 by chance may evoke such connotations. The polarity
only tells about the relationships of the factors as parts of the system. For instance, in a part if a
fuzzy cognitive map in figure 2, were it tries to capture consequences of environmental policy
interventions ('pollution limits') the strength of environmental protection measures have negative
impact on pollution load ('pollution emissions') that, for one, has a negative impact on the state of
environment: negative impacts in the FCM represent a very positive development.
Figure 2. Environmental policy intervention to improve the state of the environment by reducing pollution.
pollution limits
pollution emissions
state of the environment
negative negative
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FCM originates from the cognitive maps developed by Euler in 1736 which were based on
directed graphs (Özesmi & Özesmi 2004). Axelrod (1976) presented binary cognitive maps by
defining and describing variables in a cognitive map, and Kosko (1986) applied fuzzy causal
functions with number (-1, +1) to the connections. Furthermore, he computed the outcomes of a
fuzzy cognitive map and modelled the effects of different policy options. When Axelrod in 1976
first introduced FCM, he used lay people in his research and when applied by Kosko in 1986,
experts were used in the research. In recent research, FCM has both been applied in collecting and
presenting expert’s knowledge (Skov & Svenning 2003, Tan & Özesmi 2006), and for collecting
and comparing knowledge from experts and from laypeople (Giles et al. 2006). FCM has been
applied in working with different stakeholder groups (Özesmi & Özesmi 2003, Skogoey & Skov
2007) and in cases where the focus has been nature conservation and landscape management (Isak
2008, Khan & Quaddus 2004).
Fuzzy cognitive mapping (FCM) is a method for analysing and depicting human perception of a
given system. The method produces a conceptual model which is not limited by exact values and
measurements, and thus is well suited to represent relatively unstructured knowledge and
causalities expressed in imprecise forms. It is furthermore suitable for including knowledge from
different sciences such as natural knowledge as well as social issues and more personal aspects
(Isak 2008). FCM is a dynamic tool because cause-effects relations and feed back mechanisms are
involved (Kosko 1986). Furthermore, the emergent properties in the system can be investigated by
asking “what-if” questions regarding the system (Khan & Quaddus 2004). A fuzzy cognitive map
can be produced by one individual or by several individuals together, and more maps can be
merged into a larger fuzzy cognitive map covering more aspects of the system (Tan & Özesmi
2006). FCM focuses on the components and features in the system and is fairly simple and easy to
understand for the participants, which opens up the possibility for involving lay people as well as
planners, managers and experts (Isak 2008).
A fuzzy cognitive map may have several functions such as 1) involving the public in the planning
and managing process, 2) elucidate potential and/or existing conflicts regarding the planning and
management of the area, 3) identify areas where research could/should be focused, and 4) identify
drivers, pressures, impacts and responses according to a DPSIR model.
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Conceptual models are used to capture a systemic understanding of a phenomenon that could be
for instance a conservation problem. These models identify the basic building blocks and processes
of the system and especially the FCM type of cognitive maps allow simulations to study the
systems behavior under various policy decisions (Hobbs et al. 2002). However, it is important to
notice that the method maps how people managing or otherwise living with the system understand
it – it is a cognitive map. (Özesmi & Özesmi 2004). Even though the cognitive nature of a FCM
makes it inevitably subjective representation of the system Mouratiadou & Moran (2007)
emphasise that the model is not arbitrary as it is built carefully and reflexively with stakeholders
(in groups or individually). The conclusions drawn from a FCM exercise are credible precisely as
they bring together views of various experts and thus present a qualified understanding of the
system (Hobbs et al. 2002).
Hobbs et al. (2002) remind that the conclusions based on FCM should be viewed together with
existing scientific knowledge. Conclusions based on an analysis and/or simulations of FCM can
be counter-intuitive or against scientific results. If such are encountered, one must further study
the assumptions depicted in fuzzy cognitive maps, but also be open to insights gained from a
systemic approach to problem analysis that FCM is. It may well be the case that previous
scientific studies were not based on systemic approach, e.g. ecosystem approach, and thus have
missed the unexpected linkages and feedbacks in the system. Cognitive mapping methods are
especially designed for systemic approaches and can thus make visible previously unknown and
surprising effects of the system.
Scholars that had used and /or studied FCM emphasis strongly that one must understand the
nature of cognitive maps and not to confuse them with 'real' systems as they occur in nature
(Aguilar 2005; Mendoza & Prabhu 2006; Özesmi & Özesmi 2004). The weights (values from -1
to +1) given to linkages between factors and the values of factors in a fuzzy cognitive map
describe relative strengths in the cognitive map. One must be especially careful with factors that
have measurable, quantifiable counterparts in natural systems. FCM can be used to simulate for
instance a policy intervention and its systemic consequences. If the fuzzy cognitive map then
depicts the causalities of natural systems correctly, simulations of fuzzy cognitive map and
consecutive changes in the factors do give an understanding of possible directions of changes, but
an actual value of factors after the simulation is an artifact produced by a calculus of fuzzy
cognitive map (Hobbs et al. 2002). Consequently, fuzzy cognitive maps cannot be used directly
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to draw policy recommendations, but as they do improve our understanding of how the studied
system could behave and help us to identify the central mechanisms FCM is a powerful tool to
direct our research (Hobbs et al. 2002; Özesmi & Özesmi 2004; Mouratiadou & Moran 2007).
Application of fuzzy cognitive mapping
When conducting the process of FCM, it is important to have the aim of the study clear. A crucial
part is to have a clear understanding of what the informants shall describe in the fuzzy cognitive
maps. This will form the question upon which the fuzzy cognitive maps evolves around (e.g. What
is important in this landscape? or What threatens the biodiversity in this landscape?).
Identification of the case study area
Identifying and especially delineating the case study area must be done with considerations. FCM
can both describe small as well as large areas, but the challenge lies in getting the different issues
in the area described. If a small area is chosen, if may be difficult to include all the nature and
landscape qualities, as the quality of biodiversity, the atmosphere and characters, the pictorial
qualities and the historical and narrative values (Arler 2000). On the contrary, if a larger
landscape is chose, if may become difficult to include the relations to the specific places in the
area, such as the spirit of place – the Genius Loci (Nordberg-Schultz 1980).
Figure 2: Stakeholder grid (Eden & Ackermann 2004)
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Identification of informants
Informants can be identified differently, according to the aim of the study. If the aim is the
process of FCM and to involve stakeholders, a stakeholder analysis can be conducted by plotting
the stakeholder into a grid, as described by as shown in figure 2. Here the stakeholders are placed
in the grid according to their interest in the strategy making organisation and to their power in
relations to strategy realisation. This identifies four categories of stakeholders, Subjects, Players,
Crowd and Strategy Context Setters. According to the aim of the study the stakeholders can be
chosen based on this analysis (Eden & Ackermann 2004). If the aim of the study is the maps
themselves and how accurate they depict the landscape, it may be necessary to include experts
from different sciences as well as planners, managers and lay people. To identify these
participants, is may be necessary to supplement the stakeholder analysis with and analysis of the
participants knowledge of the area, their involvement in the management of the area, their
research in the area etc.
Example of identification of informants
In a case study in Denmark, three categories of stakeholders were identified and were classed as:
recreational users, landowners and planners
and managers. Stakeholders from the
recreational users (RU) category were
defined by their interest in outdoor activities
in the area, either through their profession or
individual interests. This is illustrated in
figure 3 which, at the X-axis, illustrates the
impact of the stakeholder’s activity, and at the
Y-axis illustrates the importance of the
management to the stakeholder. Thus the
stakeholders were identified by investigating:
1) What impact their activities were assumed
to have on the flora and fauna and on the
recreational quality of the area, and 2) how
strongly the stakeholders were assumed to be
influenced by the management of the area.
Figure 3: Analysis of the stakeholder category “recreational users”. The x-axis shows how strong the impact from the activity is, on the flora, fauna and recreational quality, with one visit. The y-axis shows how important the management is assumed to be for the stakeholder. The three stakeholders in bold were selected for participation in this study.
Danish Gymnastics and Sports Associations,
Karpenhøj
Røndefolk school
UnorganisedGeo-chaching
La
rge
imp
ort
ance
Sm
all
imp
ort
an
ce
Unorganised Mountain bike riders
Jökull–Society for Icelandic Horses
The Nature day care centre Mols Bjerge
High impactLow impact
Active in nature:A private company
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The informants in the top right quadrant were identified for participation in the study, and three
informants, were selected through interviews and their willingness to participate in the study.
The landowners (LO) were defined as individuals, who either own or manage land and are affected
by the planning of the area. The informants were
identified based on, 1) the location of the
individual’s land, 2) the individual’s previous
participation in management of the area, and 3)
interviews with people in the local community.
The aim was to cover the major ownership types
and four informants were selected. Planners and
managers (PM) were defined as stakeholders
who have authority in the planning and/or
management of the area, and a stakeholder
analysis is presented in figure 4. The choice of
stakeholders was made through investigating
their authority in relation to management
decisions by using a stakeholder grid (Eden &
Ackermann 2004). Figure 4 shows the stakeholders within politics, public administration and non
government organisations (NGO) and the three most influential PLAYERS were identified. Five
informants were selected, based on their willingness to participate in the study (Isak 2008).
Creating fuzzy cognitive mapping with stakeholders
The aim of an interview session is to build a fuzzy cognitive map that describes how a stakeholder
perceives a complex system e.g. a landscape.
Creating a fuzzy cognitive map is a step by step approach. As with many other interview techniques
it is helpful to produce an interview guideline describing the single steps before starting with the
interviewing. An example for an interview-guideline as used in some of the ALTER-net case
studies is given below. The following interview guidelines should function as a guidance/inspiration
for how to conduct the interviews, and how to create fuzzy cognitive maps over the case study
areas. They are largely based on Kvale & Brinkmann (2008) and Özesmi & Özesmi (2004).
Figure 4: A Stakeholder grid presenting the stakeholders from the category “planners and managers” as SUBJECT, PLAYERS, CROWD, or STRATEGY CONTEXT SETTERS (Eden & Ackermann 2004). The PLAYERS considered in the case study are marked in bold.
SUBJECTS PLAYERS
CROWD
STRATEGY CONTEXT SETTERS
Bystanders Actors
Sta
keh
old
ers
Un
affe
cte
d
Power in relation to management
Interest
in the
mana-
gement
strate-gies
Danish Society for Nature Protection
Danish Forest and Nature Agency
Danish HuntingAssociation
Djursland’sFarmer Association
Danish Society for Ornithologist The Danish
ForestAssociation
Syddjurs Municipality
The Danish Outdoor Council
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Group interview versus individual interviews
The interview can be conducted with individuals as well as groups. If the interviews are conducted
with individual persons, it is important to have in mind that it can be difficult to cover the subject
broadly and it will be necessary to conduct interview among several persons with different
connection to the area, and especially that the selection of interview persons has a high
significance. If the interviews are conducted with groups, it is very important to be aware of the
group dynamics and facilitate the process carefully.
Example of conducting an interview
If the fuzzy cognitive maps are created during interview, an interview guide is a necessary tool. An
example of an interview guide used for creating maps with individuals through semi-structured
interviews. Table 1 shows an example of an interview guide.
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Table 1: Interview guide from a case study in Mols Bjerge, Denmark (Isak 2008).
Research questions Interview questions
What does the informant perceive as important concepts in this landscape, and how is this being influenced by other concepts?
When you experience this place:
What is important for you?
What do you appreciate?
What do you not like?
What:
Affects X
Causes X to have the value you describe?
Which factors (natural changes, human activities etc) can change this system?
What
Do you believe can change this picture?
Have changed since you started coming here? (natural changes / changes caused by humans
What if:
More people are coming?
More noisy people are coming?
There are decided limitations to the management?
There are decided limitations to the traffic?
How affects these concepts each other (positively, negatively, feed back mechanisms)?
What happens with X when Y becomes larger/smaller?
What happens then with Z?
How strong are these effects (small, medium, large)? How:
Large effect positive/negative effect does concept X have on concept Y (small/medium/large)?
Important is it for concept X that concept Y changes (small/medium/large)?
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The suggested interview process has three stages: (1) A formal introduction, (2) the actual interview
where a fuzzy cognitive map is created, and a (3) conclusion of the interview.
1. Introduction
Present yourself
A presentation helps build up trust and creates a friendly and relaxed atmosphere for the interview.
A presentation could include:
Professional background (where do you work and what do you work with; what is your core
competence)
Private background if judged as relevant (it may be an advantage to underline the
similarities between your background and the informant’s life in order to narrow the
difference between you. This may affect the interview situation positively).
The objective of the interview (a brief overview of what will happen)
It is important that the informant(s) understand the objective of the interview. It will help obtain the
information needed. E.g., “The objective of this interview is to draw maps that show what you find
important in the area, based in different themes. The maps will consist of concepts that you find
important and connections between these which illustrate how the concepts influence each other”.
The objective of the project (what is the context of the interview)
It is important that the informant(s) understands how the interview fits into the whole project. For
example:
“The objective of this project is, to investigate how different individuals/groups describe the same
spatial landscape/area”.
This information will be used to 1) strengthen the cooperation between the different stakeholders, 2)
focus the future research in the area, and 3) get a deeper understanding of how the different factors
in the landscape are connected.
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A more detailed description of the procedure and the FCM approach
A successful interview depends on the informant(s) feeling safe and in control of the situation. This
may be obtained by proper briefing and making sure the informant(s) participates without external
pressure. Reliability can be enhanced by emphasizing the dialog between the interviewer and the
informant(s). During the interview, always remember to confirm that you have understood the
interview person correct.
Use manner of speech such as:
“is it correctly understood that…….”
“does that mean that…….”
“have I understood it correctly, that you…..”
The informant(s) may need some time to reflect in order to find the right concepts, and it is
important not to interrupt this process.
When introducing the method to the interviewee it is helpful to show some fuzzy cognitive maps
either from literature or by drawing a simple example FCM on a piece of paper. These “example-
maps” should not be too close to the topic of the interview so that the interviewee will not be
influenced by their content. It has proven useful to choose topics from everyday life. Important
characteristics of FCM should be made clear to the interviewee by using examples.
a.) Emphasis that all kind of topics can be included (no restriction to a certain discipline).
b.) Describing the properties of concepts is very important as they have to fulfil certain criteria.
When creating the fuzzy cognitive maps, it is important to consider that the concepts must be
quantifiable in order to be able to be affected by other concepts. E.g. the concept “forest” could be
“total area covered by forest in the area”, or “number of small area covered by forest”. And the
concept “view” can be understood as “area with land cover, which makes it possible to see far” or
as “lack of unpleasant and unwanted things in the view”. Thus it is important for the understanding
of the maps, that the concepts are clearly described in a manner which makes the FCM work.
c.) Another issue which should have specific focus during the FCM process is to determine how
strongly the concepts affect each other. When explaining the method, state that the numbers which
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express the strength of the influences exerted on one concept do not need to sum up to one. The
strength of the influence may be difficult for the participants to determine, but different interview
techniques and communication tools, which will start thinking and reflecting and facilitation of
discussions may help this challenge (Kaner et al. 1996; Kvale & Brinckmann 2008; Lewicki &
Wiethoff 2000; Rosenberg 1999; Thomas 1998).
After explaining FCM and its properties, the interviewee should be informed about the process of
creating the fuzzy cognitive map.
2. Creating the fuzzy cognitive map
The process of creating a FCM is done in three steps: a.) Listing the concepts b.) Connecting the
concepts with arrows and indicating negative or positive influence c.) Determine the strengths of
the connections. As the discussions with and explanations of the interviewee during the whole
process can contain relevant information it is helpful to record the whole session.
a.) Listing the concepts
After confronting the interviewee with the central question or statement he/she is asked to list all
relevant concepts that come into his/her mind. The facilitator should also mention that it is possible
to introduce new concepts, which are not listed during the drawing session. When the interviewee is
producing the list, the facilitator
should make sure that the listed
concepts fulfil the criteria
mentioned above and that he/she
exactly understands what each
concept means to the interviewee.
This should be done by asking
comprehensive questions like
“what exactly do you mean with
x”. If a concept does not fulfil the
necessary criteria the interviewee
should be made aware and asked
for modifying his concept (Box 1).
Box 1:
Example from one of the case studies:
A interviewee asked to about factors and agents important for the development in his region puts down the concept “municipality”.
Facilitator (F): What do you mean with municipality?
Interviewee (I): Well, you know the people living there and every thing
F: Hmm so what will it mean if we get more or less of that?
I: Well if it is more then the people like their municipality and like to life in it. It is actually more the community and if the people feel at home in their municipality
F: Ok I understand. So it is somehow the quality of live the people experience in their community?
I. Yes that is what I mean.
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b.) Drawing the map
After the interviewee believes that he/she has listed all concepts relevant, the actual drawing of the
map starts. Before starting, the facilitator can remind the interviewee that he/she neither needs to be
restricted to the concepts on the list nor that he/she needs to use all of them. The interviewee is
asked to pick one of the concepts of the list e.g. the one which is considered most important. This
concept is then written in the middle of a large drawing paper (minimum A3). Then the interviewee
is asked to think of what other concepts influence this concept either positive or negative. He/she
should connect them with arrows and assign “+” to the positive connections and “-“ to the negative
ones. During this process the facilitator should ask questions to again clarify the meaning of the
concepts.
The drawing is finished when the interviewee has the feeling that the map which has evolved on
his/her drawing-paper represents the system under question accuratly.
c.) Weighing the influences
After the map is completed the interviewee is asked to give strengths to the conections. The easiest
way to do that is to start with the strongest or weakest one and to rate the other connections in
relation to the extremes.
One way to help determining the strengths is to check – after all the strongest linkages are
determined – the strengths factor by factor. The interviewee is asked to consider all the incoming
arrows to a factor in relation to each other. Initially the weights can be given by increasing numbers
of '+' or '-' signs (e.g. +, ++ or +++) to avoid summing up to one and then later give actual
numerical values. At this stage it is also important to recognise that the factor can be influenced by
other factors that are not depicted in the fuzzy cognitive map. These other factors could be added as
external factors ('external' in FCM vocabulary means a factor that influences the described system
from the outside and is not influenced by any other factor), but it is not necessary in the case that
the external factors are well-known and almost self-evident, but not otherwise relevant in relation to
other factors in the fuzzy cognitive map. After all, the fuzzy cognitive map is not an absolutely
complete description of a real system.
In a last step the facilator should review the map with the interviewee to dedect missing weights or
connections.
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3.) Closing the interview
It is important for the further process that the informant(s) leaves the interview situation with a clear
idea of the process ahead. This includes giving information on how the participant(s) will be
involved in process that follows the interview and what products he/she can expect to receive (e.g.
the digitalized and cleaned version of his map, final report of the project etc.)
Defining the concepts in the fuzzy cognitive maps
The fuzzy cognitive maps can either be created by letting the informants defined the concepts in
their own words, or by giving the informants a list of concept from which they can chose concepts
for the fuzzy cognitive map. The former will give the informants the possibility to include all
aspects which they find relevant for the landscape. This will be done in a personal manner, as the
concepts will be phrased by their own words. The latter will present the informants with aspects
defined beforehand, and by this, the focus of the maps can be controlled and directed. Furthermore,
this will result in comparable fuzzy cognitive maps, across very different landscapes.
The first approach is most suitable if the focus is on the process of FCM i.e. involving stakeholders
in a public participation process. The last approach is most suitable if the aim is to model the
landscapes and this way to compare different landscapes. The authors have discussed possible
concepts, and produced a list, shown in table 2, with possible concepts to describe a European
landscape. The process of creating the fuzzy cognitive map can be steered by an interview guide
such as the one presented in table 3 and a concept tree such as the one in table 2, which is an
incomplete list over concepts which can describe the different factors in a spatial landscape.
During the interview, new concepts should be added to the concept tree as they appear, if they can
not be interpreted as belong to an already mentioned concept.
Table 2: List of possible concepts for describing a landscape with the use of FCM. First column shows four landscape themes, column 2, 3 and 4 shows how these four themes can be described in more and more detailed concepts, and column 5 shows a list over possible concept to be used in FCM.
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Themes Concepts
Erosion
InundationProcesses
Ecosystem functioning
Conservation status
Number
Diversity
Species
Characteristic species
Conservation status
Size of area
Nature
Habitat
Characteristic habitats
Memories
Spirituality
Sense of place
Individual
Ownership
Material
Values
Cultural (common)
Stories
Landscape pattern
InfrastructureHuman
Archaeological
Terrain
Hydrology
Shoreline
Landscape scenery
Natural
Landscape pattern
Industry
Area with agriculture Agriculture
Intensity of agriculture
Area covered by forest
Intensity of forestry
Production
Forestry
Drinking water
Tourism
Economic
Services Housing
Types of recreational activity Recreational Intensity of activities
ManagementNature conservation
Restoration
Human activities
Politics and planning
Education and information
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Table 3: Example of an interview guide working with the list of concepts in table 2.
Research questions Interview questions How to conduct
What is important for the interview person in this location/place?
When you experience this place, what is important for you?
The nature? The values in the area? The landscape scenery? The human activities?
Make the interview person chose one of the four themes:
Nature Values Landscape scenery Human activities .. other themes?
With which concepts/variables, do the interview describe this location/place, when considering the concept/variable chosen above?
When considering X,
What affects X What constitutes X What makes X
important for you?
Guide the interview person to describe concepts and chose the appropriate concept/variable from the concept-tree
Which factors (natural changes, human activities, etc.) does the interview person see as being capable of changing this system?
What
Do you see as capable of changing this picture?
Have changed since you started coming here? 1. natural factors 2. factors which are
caused by human actions
Guide the interview person to include concepts/variables from the list over pressures and drivers
How do the concepts affect each other (positive, negative, feedbacks)?
What happens with X when Y becomes bigger/smaller?
What happens then with Z?
How strong are these influences (small, medium, large)?
How
Important is this positive/negative affect on X (large, medium, small)?
Much does it mean for X that Y changes (much, little, in between)?
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Analysing the fuzzy cognitive maps
Before analysing the fuzzy cognitive maps created by the informants, it is important give attention
to some issues. If more maps will be put together into fewer, it might be necessary to group
concepts from different maps. This may have an influence on the following analyses and thus the
result, and a possible grouping should ideally be validated by the informants or secondary be done
carefully and consistently.
Structural and dynamic analyses
The structure of fuzzy cognitive maps can be analysed and used when comparing maps created by
a number of informants or groups. It can be investigated how many times a given concept is
mentioned, and if many informants mention the same concept, it can be interpreted as important
for the system. Fuzzy cognitive maps can also be compared through three indices, the density
index, the hierarchy index and the complexity index. The density index looks at the number of
concepts (variables) and connections in the maps, and expresses how connected the variables in the
maps are. A high density index indicates that the map represents a perception where many causal
relationships are present. The hierarchy index looks at how the variables affect and are affected by
other variables. The hierarchy index ranges between 0 and 1 and expresses how adaptable the
system is to changes. A low index value (democratic map) is more adaptable to changes due to the
level of integration and dependencies, than an index value near 1 (hierarchy map). The complexity
index is the ratio between the receiver variables (R) and the transmitter variables (T) in the map. A
receiver variable (R) is affected by variables without affecting the other variables (is said to
contain only indegree values) and a transmitter variable (T) affects other variables without being
affected by any (is said to contain only outdegree values). Variables can also be ordinary which is
defined as containing both indegree and outdegree values. A large complexity index illustrates
many usable outcomes and less controlling forcing functions. The variables themselves can be
analysed by their centrality index. The centrality index is the sum of the indegree and the
outdegree, thus, the centrality expresses how large a role a given variable plays in the system. A
high centrality shows a large importance and a low centrality reflects a lesser importance (Özesmi
& Özesmi 2004).
Fuzzy cognitive maps can also be analysed in a dynamic manner by investigating what happens if
some variables (e.g. variables which are acting as drivers in the system) are given specific values
19
continuously. The outcome of the simulation of the system can be investigated but it should be
kept in mind that FCM can not make predictions but works as a tool for gaining an understanding
of the system.
Acknowledgements
This work was supported by ALTER-Net (A Long-term Biodiversity, Ecosystem and Awareness
Research Network). ALTER-Net (www.alter-net.info) is a Network of Excellence funded by the
6th Framework Programme of the European Commission.
20
References
Aguilar, J. (2005). A survey about Fuzzy Cognitive Maps papers. International Journal of
Computational Cognition, 3: 27-33.
Arler, F. (2000): Aspects of landscape or nature quality. Landscape Ecology. 15:291-302.
Axelrod, R. (1976): Structure of Decision: The Cognitive Maps of Political Elites. Princeton
University Press, Princeton, NJ.
Eden, C. & Ackermann, F. (2004): Making Stretegy, The Journey of Strategic Management.
Sage. London. ISBN: 0-7619-5224-1.
Giles, B.G. et al. (2006): Integrating conventional science and aboriginal perceptives on diabetes
using fuzzy cognitive maps. Social Science & Medicine, DOI: 10.1016/j.socsimed.2006.09.007.
Giordano, R., Passarella, G., Uricchio, V., Vurro, M. (2005): Fuzzy Cognitive Maps for issue
identification in a water resource conflict resolution system. Physics and Chemistry of the Earth,
30: 463-469.
Hobbs, B., Ludsin, S., Knight, R., Ryan, P., Biberhofer, J., Ciborowski, J. (2002). Fuzzy
Cognitive Mapping as a tool to define management objectives for complex ecosystem. Ecological
Applications 12: 1548-1565.
Isak, K.G.Q. (2008): Investigating Fuzzy Cognitive Mapping as a participatory tool for
conceptual landscape modelling. MSc Thesis in Landscape Management. Faculty of Life
Sciences, University of Copenhagen
Kaner, S., Lind, L., Toldi, C., Fisk, S., Berger, D. (1996): Facilitator’s guide to participatory
decision-making. New Society Publishers. Gabriola Island.
21
Khan, M.S. & Quaddos, M. (2004): Group Decision Support Using Fuzzy Cognitive Maps for
Causal Reasoning. Group Decision and Negotiation. 13: 463-480.
Kosko, B. (1986): Fuzzy Cognitive Maps. International Journal of Man-Machine Studies. 24.1:
65-75.
Kvale, S., Brinkmann, S. (2008): Inter Views, Learning The Craft Of Qualitative Research
Interviewing. Sage Publications Ltd. London. ISBN: 0761925422
Lewicki, R.J. & Wiethoff, C. (2000). Trust, trust development and trust repair. In: The
handbook of conflict resolution. Theory and practice. Deutsch, M. & Coleman, P.T. (eds.) Jossey-
Bass Publishers, San Franscisco, Chapter 4, pp. 86-92; 96-107
Mendoza, G., Prabhu, R. (2006). Participatory modelling and analysis for sustainable forest
management: Overview of soft systems dynamics models and applications. Forest Policy and
Economics, 9: 179-196.
Mouratiadou, I., Moran, D. (2007). Mapping public participation in the Water Framework
Directive: A case study of the Pinius River Basin, Greece. Ecologica l Economics, 62: 66-76.
Nordberg-Schultz, C. (1980): Genius Loci. Towards a phenomenology of architecture. London,
Academy Edition. SBN: 85670 700 7.
Özesmi, U., Özesmi, S. (2003): A participatory approach to ecosystem conservation: Fuzzy
cognitive maps and stakeholder group analysis in Uluabat Lake, Turkey. Environmental
Management. 31.4: 518-31.
Özesmi, U., Özesmi, S.L. (2004): Ecological models based on people's knowledge: a multi-step
fuzzy cognitive mapping approach. Ecological Modelling. 176.1-2: 43-64.
22
Rosenberg, M.B. (1999). Non-violent communication. A language of compassion. Puddle Dancer
Press, Del Mar
Skogoey, K.I., Skov, F. (2007): Fuzzy Cognitive Mapping – a model for public participation. In:
Chmielewski, T.J. (ed.) Nature Conservation Management: From Idea to Practical Results.
ALTER-Net, Lublin, Poland. ISBN: 83-87414-98-0. [Cited on 25.09.08]. Available at the
internet: <http://www.alter-
net.info/POOLED/ARTICLES/BF_NEWSART/VIEW.ASP?Q=BF_NEWSART_300866>
Skov, F., Svenning, J.C. (2003): Predicting plant species richness in a managed forest. Forest
Ecology and Management. 180.1-3: 583-93.
Tan, C.O. & Özesmi, U. (2006): A generic shallow lake ecosystem model based on collective
expert knowledge. Hydrobiologia. 563:125-142.
Thomas, C.W. (1998). Maintaining and restoring public trust in government agencies and their
employees. Administration & Society 28: 166-193