1

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

wwwwww..aalltteerr--nneett..iinnffoo

2

Manual for applying Fuzzy Cognitive Mapping – experiences from ALTER-Net

Authors

Kirsten G.Q. Isaka, ki@dmu.dk, Martin Wildenbergb, martin.wildenberg@uni-klu.ac.at, Mihai

Adamescuc, adacri@bio.bio.unibuc.ro, Flemming Skova, fs@dmu.dk, Geert De Blustd,

Geert.deblust@inbo.be and Riku Varjopuroe, riku.varjopuro@ymparisto.fi

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.

3

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

4

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.

5

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

6

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)

7

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

8

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

9

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.

10

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)?

11

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.

12

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

13

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.

14

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.

15

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.

16

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

17

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)?

18

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