How do you perceive environmental change?

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Applied Soft Computing 12 (2012) 3725–3735 Contents lists available at SciVerse ScienceDirect Applied Soft Computing j ourna l ho mepage: www.elsevier.com/locate/asoc How do you perceive environmental change? Fuzzy Cognitive Mapping informing stakeholder analysis for environmental policy making and non-market valuation Areti D. Kontogianni a,, Elpiniki I. Papageorgiou b , Christos Tourkolias a a University of Aegean, Faculty of Environment, 81100 Mytilini, Greece b Technological Educational Institute of Lamia, Dept. of Informatics and Computer Technology, Lamia, Greece a r t i c l e i n f o Article history: Received 1 August 2011 Received in revised form 24 April 2012 Accepted 5 May 2012 Available online 18 May 2012 Keywords: Fuzzy Cognitive Mapping Environmental management Decision-making Scenarios Non-market valuation a b s t r a c t In spite of considerable progress in our understanding of ecosystem functioning, our ability to design effective and enforceable environmental policies requires a deep understanding of human perceptions and beliefs. In this respect, what is called today stakeholder analysis is an eclectic mixture of qualitative and semi-quantitative techniques aiming at eliciting, understanding and de-codifying how individuals perceive risks and threats towards sustainability. Fuzzy Cognitive Mapping (FCM) is gradually emerging as an alternative methodology capable of assisting researchers in the domain of environmental policy. We explored the promise that FCM holds to support environmental policy makers. We suggest FCM approach as a new participatory method in environmental policy: through aiding in Multi-stakeholder (actor) analysis for risk assessment, capturing values and scenarios construction. To show how this is feasible we try to answer three basic questions: How cognitive mapping can support decision-making? How FCM can support environmental decision-making? How simulation of concepts may help in communicating stakeholders’ views to environmental decision makers? Then we explore the potential application of FCM in environmental policy, especially in environmental economics, trying to substantiate economic values for nature providing ‘flesh and bones’ to the concept of economic preferences. © 2012 Elsevier B.V. All rights reserved. 1. Introduction In the wake of 21st century, conserving natural resources and protecting climate stability is still unfinished business. Though this is true for pollution problems of the first generation (e.g. urban air pollution, solid waste and water pollution) the environmental problems our societies now face are of a more subtle and pervasive nature, e.g. global warming, habitat degradation and species loss, collapse of renewal resource stocks, land contamination an end- less suite of complex issues demanding a stronger commitment, a better science and a heavier financial burden. A number of biophys- ical indicators published by international agencies document this trend while environmental degradation appears intrinsically linked with issues of human rights, national security, human health and poverty [1–3]. In spite of our success in modelling and predicting impacts of man-made pressures on terrestrial and marine ecosystems, uncertainty of intensity and timing of impacts still looms large. A prominent domain full of uncertainties is climate change where Corresponding author. Tel.: +30 6945 550 841. E-mail addresses: [email protected] (A.D. Kontogianni), [email protected], [email protected] (E.I. Papageorgiou). low probability, high impact events – conceived as ‘tipping points’ [4] pose risks of extreme magnitude for continuation of our civ- ilization as we know it. What analysts have termed ‘statistical undecidability’ [5] seems to apply not only to world markets but also to climate change reality as well. New methodological tools are needed transcending the established divide between social and natural sciences, facts and values, objective forecasts and subjective visions. Our institutions are still inadequately equipped for addressing these challenges. Therefore, the need for informed policies is urgent and a plea for a holistic approach to scientific problem solving is emerging. This is especially true for environmental policies sup- porting critical ecosystem process whereupon the very essence of our existence depends. Formulation of a successful environmen- tal decision-making relies on integrated models of socio-economy and the natural environment able to provide decision-makers with flexible and adaptive policies. According to [6], creating ‘adaptive policies’ could help policy-makers to navigate within today’s com- plex, dynamic and uncertain fields [7] identifies the persisting gap between environmental experts and policy makers. Discussing the future use of actor analysis in environmental policy analysis the author proposes three plausible explanatory mechanisms for his ‘rather disappointing result’ regarding the role of actor analysis in water management: project and institutional path dependence, the 1568-4946/$ see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.asoc.2012.05.003

Transcript of How do you perceive environmental change?

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Applied Soft Computing 12 (2012) 3725–3735

Contents lists available at SciVerse ScienceDirect

Applied Soft Computing

j ourna l ho mepage: www.elsev ier .com/ locate /asoc

ow do you perceive environmental change? Fuzzy Cognitive Mapping informingtakeholder analysis for environmental policy making and non-market valuation

reti D. Kontogiannia,∗, Elpiniki I. Papageorgioub, Christos Tourkoliasa

University of Aegean, Faculty of Environment, 81100 Mytilini, GreeceTechnological Educational Institute of Lamia, Dept. of Informatics and Computer Technology, Lamia, Greece

r t i c l e i n f o

rticle history:eceived 1 August 2011eceived in revised form 24 April 2012ccepted 5 May 2012vailable online 18 May 2012

eywords:uzzy Cognitive Mappingnvironmental management

a b s t r a c t

In spite of considerable progress in our understanding of ecosystem functioning, our ability to designeffective and enforceable environmental policies requires a deep understanding of human perceptionsand beliefs. In this respect, what is called today stakeholder analysis is an eclectic mixture of qualitativeand semi-quantitative techniques aiming at eliciting, understanding and de-codifying how individualsperceive risks and threats towards sustainability. Fuzzy Cognitive Mapping (FCM) is gradually emergingas an alternative methodology capable of assisting researchers in the domain of environmental policy. Weexplored the promise that FCM holds to support environmental policy makers. We suggest FCM approachas a new participatory method in environmental policy: through aiding in Multi-stakeholder (actor)

ecision-makingcenarioson-market valuation

analysis for risk assessment, capturing values and scenarios construction. To show how this is feasiblewe try to answer three basic questions: How cognitive mapping can support decision-making? How FCMcan support environmental decision-making? How simulation of concepts may help in communicatingstakeholders’ views to environmental decision makers? Then we explore the potential application of FCMin environmental policy, especially in environmental economics, trying to substantiate economic valuesfor nature providing ‘flesh and bones’ to the concept of economic preferences.

© 2012 Elsevier B.V. All rights reserved.

. Introduction

In the wake of 21st century, conserving natural resources androtecting climate stability is still unfinished business. Though this

s true for pollution problems of the first generation (e.g. urbanir pollution, solid waste and water pollution) the environmentalroblems our societies now face are of a more subtle and pervasiveature, e.g. global warming, habitat degradation and species loss,ollapse of renewal resource stocks, land contamination – an end-ess suite of complex issues demanding a stronger commitment, aetter science and a heavier financial burden. A number of biophys-

cal indicators published by international agencies document thisrend while environmental degradation appears intrinsically linkedith issues of human rights, national security, human health andoverty [1–3].

In spite of our success in modelling and predicting impacts

f man-made pressures on terrestrial and marine ecosystems,ncertainty of intensity and timing of impacts still looms large. Arominent domain full of uncertainties is climate change where

∗ Corresponding author. Tel.: +30 6945 550 841.E-mail addresses: [email protected] (A.D. Kontogianni),

[email protected], [email protected] (E.I. Papageorgiou).

568-4946/$ – see front matter © 2012 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.asoc.2012.05.003

low probability, high impact events – conceived as ‘tipping points’[4] – pose risks of extreme magnitude for continuation of our civ-ilization as we know it. What analysts have termed ‘statisticalundecidability’ [5] seems to apply not only to world markets butalso to climate change reality as well. New methodological toolsare needed transcending the established divide between social andnatural sciences, facts and values, objective forecasts and subjectivevisions.

Our institutions are still inadequately equipped for addressingthese challenges. Therefore, the need for informed policies is urgentand a plea for a holistic approach to scientific problem solving isemerging. This is especially true for environmental policies sup-porting critical ecosystem process whereupon the very essence ofour existence depends. Formulation of a successful environmen-tal decision-making relies on integrated models of socio-economyand the natural environment able to provide decision-makers withflexible and adaptive policies. According to [6], creating ‘adaptivepolicies’ could help policy-makers to navigate within today’s com-plex, dynamic and uncertain fields [7] identifies the persisting gapbetween environmental experts and policy makers. Discussing the

future use of actor analysis in environmental policy analysis theauthor proposes three plausible explanatory mechanisms for his‘rather disappointing result’ regarding the role of actor analysis inwater management: project and institutional path dependence, the
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references of experts for their own professional tools and exper-ise and the manner environmental experts perceive their role asssue advocates [7].

An ever growing number of practitioners see a way out of thensuing dilemmas in opening up the process of designing effectiveolicies in two directions: on the one hand, experts are called upono provide their judgements and informed guesses in filling scien-ific information gaps. Eliciting expert judgements are nowadaysidely practiced in the domains of climate, biodiversity and energyolicy [8]. Expert judgement elicitation though has certain disad-antages (i.e. cost and time required, lack of flexibility, possibleoss of creativity and the appearance of a false objectivity), which

ight cause results to be accepted without a prudential degree ofcepticism [9].

On the other hand, researchers apply deliberative or inclusivepproaches, which promise an integration of stakeholder groupsnto policy design and evaluation. On several occasions it has beenhown that stakeholder values are the key to a structured deci-ion approach to public involvement [10–13]. Stakeholder valuesdentify what matter to participants and in turn highlight the conse-uences that require most careful attention and the trade-offs thatatter most [14]. According to [15], meaningful involvement in the

ecision making process requires not only an invitation to partic-pate but also a forum for careful deliberation and a mechanismor incorporating the results of technical analysis. New problemshough arise when applying deliberative or inclusive approaches:ow useful are lay people perceptions? Do we need welfare

elated perceptions only or should we investigate also percep-ions on ecosystem functioning? How to incorporate perceptionsnto decision-making? How to extract useful local knowledge?

hat to disregard? How to cope with biases due to the elicitationpproach? How to quantify? How to treat qualitative information?o tackle these challenges appropriately within an ecosystem-ased approach, current environmental management strategieseed to ‘navigate’ through an apparent tension: they must meet theemand for scientific knowledge-based policy, while the very sametrategies urge for stakeholder involvement and sponsor initiativeso elicit lay-people attitudes, beliefs and visions for the future. Thisension seems to reflect the everlasting standoff of bottom up andop down approaches.

Deliberative or inclusive approaches to environmental man-gement are usually referred to as ‘stakeholder analysis’ [16–18].takeholder analysis includes mostly qualitative approaches thatefer to the interaction of social groups and their dynamics:ocial network analysis [19–21], analysis of conflicts [22–25] andctor analysis [7]. It also includes qualitative or semi-quantitativepproaches exploring individual perceptions, values and attitudes.hese include: comparative cognitive mapping of social percep-ions and values [26,27], perceptions mapping [28], mind mapping29], concept mapping [30], focus groups and in-depth inter-iews. Approaches in stakeholder analysis as described abovehare some common characteristics: they are eclectic but prag-atic approaches with varying degree of sophistication, requiring

n average a low in-depth academic investigation, but able toanipulate a vast quantity of soft information using inter-

iewer survey-based methods for eliciting and recording theirata.

The present paper focuses on Fuzzy Cognitive Mapping (FCM), promising supplement to areas of environmental policy such asarticipatory environmental scenario development, subjective risknalysis and stated preference approaches in environmental valua-ion. The paper raises some fundamental questions referring to the

pplication of FCM in environmental management. It investigateseaningful questions rather than providing tailor-made solutions.

n Section 2 a concise introduction to the FCM methodology is pro-ided before we embark in Section 3 on the discussion of three

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questions related to specific aspects of FCM application in environ-mental management. The questions are chosen so as to illustratebasic challenges faced by an environmental policy researcher inhis/her attempt to get the best out of FCM within social environ-mental research. Section 4 interrogates the applicability of FCM inthe specific domain of estimating economic values for nature. Theconcluding Section 5 summarizes the insights gained and proposesareas of future research.

2. The FCM methodology

Proposed in 1986 by Kosko, Fuzzy Cognitive Maps are fuzzysigned graphs, which can be presented as an associative single layerneural network [31,32]. They describe particular domains usingnodes (also known as concepts), and signed fuzzy relationshipsbetween nodes. The fuzzy part permits degrees of causality, rep-resented as links between the concepts of these diagrams. Thisstructure establishes the forward and backward propagation ofcausality, allowing the knowledge base to increase when conceptsand links between them are increased.

Each of FCM’s edges is associated with a weight value thatreflects the strength of the corresponding relation. This value isusually normalized to the interval [−1,1]. The matrix E stores theweights assigned to the pairs of concepts. We assume that the con-cepts are indexed by subscripts i (cause node) and j (effect node).In the simplest case, it is possible to distinguish binary cognitivemaps (BCM) for which the concept labels are mapped to binarystates denoted as Ai ∈ {0, 1}, where the value 1 means that the con-cept is activated. The weights of BCM are usually mapped to thecrisp set, i.e. eij ∈ {−1, 0, 1}. The value 1 represents positive causal-ity, meaning that the activation (change from 0 to 1) of concept Cioccurs concurrently with the same activation of concept Cj or thatdeactivation (change from 1 to 0) Ci occurs concurrently with thesame deactivation of concept Cj. The value −1 represents the oppo-site situation, in which the activation of Ci deactivates the conceptsCj or vice versa. The eij = 0 means that there are no concurrentlyoccurring changes of the states of the concepts. Some researchers[33,34] assume that the elements on the diagonal of the matrixE are not considered. In FCMs, each node quantifies a degree towhich the corresponding concept in the system is active at iterationstep.

The development and design of the appropriate FCM for thedescription of a system requires the contribution of human knowl-edge. Usually, knowledgeable experts familiar with the FCMformalism are required to develop FCM using an interactive proce-dure of presenting their knowledge on the operation and behaviourof the system [35]. Experts and/or stakeholders are asked to deter-mine the concepts that best describe the model of the system,since they know which factors are the key principles and functionsof the system operation and behaviour, introducing a concept foreach one. Experts have observed the operation and behaviour ofthe system during its operation, since they are the operators andsupervisors of the system, who control it using their experience andknowledge. They have stored in their mind the correlation amongdifferent characteristics, states, variables and events of the systemand in this way they have encoded the dynamics of the system usingfuzzy if–then rules. Each fuzzy rule infers a fuzzy weight, which inprocedure is translated to a numerical one used in the FCM reason-ing process [36,37]. Fig. 1(a) and (b) shows a generic representationof the FCM model.

Once the FCM is constructed, it can receive data from its input

concepts, perform reasoning and infer decisions as values of itsoutput concepts [37–39]. During reasoning the FCM iteratively cal-culates its state until convergence. The state is represented by astate vector Ak, which consists of real node values A(k)

i∈ [0, 1], i = 1,

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ig. 1. Example of FCM model: (a) FCM graph and (b) Adjacency connection matrix.

,. . ., N at an iteration k. The value of each node is calculated by theollowing equation:

(k+1)i

= f

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N∑

j /= i

j = 1

A(k)j

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here A(k+1)i

is the value of concept Ci at simulation step k + 1, A(k)j

s the value of concept Cj at step k, wji is the weight of the inter-onnection between concept Cj and concept Ci and f is a sigmoidhreshold (activation) function that transform the values of con-epts in the range [0,1] [40]. The iteration stops when a limit vectors reached, i.e. when Ak = Ak−1 or when Ak − Ak−1 ≤ e; where e is aesidual, whose value depends on the application type (and, in mostpplications, is equal to 0.001). Thus, a final vector A is obtained.

In order to remove the spurious influence of inactive conceptswith Ci = 0) on other concepts, and to avoid the conflicts thatmerge in cases where the initial values of concepts are 0 or 0.5, asell as the missing data, a modified FCM reasoning formalism can

e used [35]. Based on this assumption, we reformulated Eq. (1) as:

i(k + 1) = f ((2Ai(k) − 1) +N∑

j /= i

j = 1

(2Aj(k) − 1) · Eji) (2)

Eq. (2) overcomes the limitation present by the sigmoid thresh-ld function. Thus, the insufficient knowledge and/or missingnformation for each node can be handled with less deviation fromeality.

. The questions

The basic questions concerning the application of FCM in thenvironmental policy analysis discipline, raised by this paper are:

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3.1 How cognitive mapping can support decision making?3.2 How Fuzzy Cognitive Mapping can support environmental

decision-making?3.3 How simulation of concepts may help in communicating stake-

holders’ views to environmental decision makers?

3.1. How cognitive mapping can support decision making?

What is the history behind Fuzzy Cognitive Mapping? Howdecision-making theory encapsulated such a technique? What wasthe cognitive basis on which the Fuzzy Cognitive Mapping leaned?All these questions may be answered by recalling the predecessorsof FCM: mental models, mind mapping, concept mapping, cognitivemapping as well as their use in decision-making.

Decision-making is the generic process of identifying, quanti-fying, ranking and choosing the best among many alternatives.Decision support systems (DSS) are software systems designedto assist this problem-solving procedure. DSS needs input fromhuman behaviour dynamics, that is information on human cog-nitive ability to understand/process data (and result in a sensibledecision). How can human behaviour be modelled for use in DSS?Recent research on this huge subject indicates the importanceof artificial neural networks, fuzzy logic and inference systems[41,42].

A well-known artificial intelligence technique used in DSS is theExpert (knowledge based) system [43,44], in which human exper-tise in a certain area is elicited and codified within a computerprogram. Expert systems are designed to solve complex problemsby reasoning about knowledge, like an expert in a scientific fielddoes. Historically this research was initiated with mental modelsfrom early 19th century thinkers. Johnson-Laird refers to KennethCraik, a Scottish psychologist/physiologist who in 1943 wrote: “Ifthe organism carries a ‘small scale model’ of external reality and of itsown possible actions within its head, it is able to try out various alter-natives, conclude which is the best of them, react to future situationsbefore they arise, utilize the knowledge of past events in dealing withthe present and the future, and in every way to react in a much fuller,safer and more competent manner to the emergencies which face it”[45, p. 61].

According to Norman [46, p. 7]: “Mental models research isfundamentally concerned with understanding human knowledgeabout the world. In the consideration of mental models we needreally consider four different things: the target system, the concep-tual model of that target system, the user’s mental model of thattarget system, and the scientist’s conceptualization of that mentalmodel”.

Johnson-Laird [45, p. 1] wonders: “What is the end result of per-ception? What is the output of linguistic comprehension? How dowe anticipate the world, and make sensible decisions about what todo? What underlies thinking and reasoning? One answer to thesequestions is that we rely on mental models of the world. Perceptionyields a mental model, linguistic comprehension yields a mentalmodel, and thinking and reasoning are the internal manipulationsof mental models”.

Mind mapping is the process of representing in diagram ideasaround a central concept. It is a visual thinking tool, encouragingbrainstorming for elicitation of various concepts arranged in a non-linear way, classified as groups or branches. This process helps inorganizing information and aids decision making and developmentof new ideas. It is usually an informal way of illustrating words,tasks and ideas around the governing concept [29,47,48] (Fig. 2).

Concept mapping was invented by Novak and his research team

in Cornell University in the 70s [49,50], based on Ausubel [51]learning psychology theory. David Ausubel supported the idea thatlearning proceeds by assimilating new concepts into existing cog-nitive structure. Concept mapping (as mind mapping) is also a
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Fig. 2. A mind map available in (www.live-the-solution.com).

Fig. 3. A concept map showing the key features of concept maps (to be read from top downwards).Reproduced from Institute for Human and Machine Cognition (http://cmap.ihmc.us/Publications/ResearchPapers/TheoryCmaps/TheoryUnderlyingConceptMaps.htm).

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raphical tool for knowledge representation but here concepts areepresented (usually in boxes) hierarchically; at the top of the mapre general concepts, whereas specific concepts are drawn belowFig. 3). The important in concept mapping is that relationshipsetween concepts are indicated by a connecting line, enhancinghe contextual base of reasoning and creative thinking.

Risk and cognitive psychology, communication and decisionnalysis, linguistics, anthropology and philosophy and especiallyrtificial intelligence helped in further development of mentalodels. As Gentner [52, p. 3] states: “In the last decade computa-

ional models have moved from early models which emphasizednformation flow and channel capacity to exceeding rich, finelytructured formalisms for representing both data and processes in

uniform framework. The combination of a rich variety of psy-hological techniques and a new and seemingly apt formalism forepresenting human knowledge and processes has led us to believehat the time is ripe for developing theories about how peoplenderstand the world”.

A theory, which inspired cognitive mapping, was Kelly’s the-ry of personal constructs [53]. The American psychologist Boereeommenting on Kelly’s biography states: “Kelly believes that tonderstand behaviour you need to understand how the person con-trues reality – i.e. how he or she understands it, perceives it – morehan what that reality truly is. Constructive alternativism is the ideahat, while there is only one true reality, reality is always experi-nced from one or another perspective, or alternative construction.et no-one’s construction is ever complete – the world is just tooomplicated, too big, for anyone to have the perfect perspective.nd no-one’s perspective is ever to be completely ignored. Eacherspective is, in fact, a perspective on the ultimate reality, and hasome value to that person in that time and place. Through the ideaf ‘fruitful metaphor’ he stated that ordinary people act as scientistsn their everyday life: “they have constructions of their reality, likecientists have theories. They have anticipations or expectations,ike scientists have hypotheses. They engage in behaviours that testhose expectations, like scientists do experiments. They improveheir understandings of reality on the bases of their experiences;ike scientists adjust their theories to fit the facts” [54].

Kelly developed 11 corollaries, 3 of them functioning as prereq-isite [55] to work for decision making within organizations (with

ndividuals or in teamwork):

The individuality corollary: each individual through different fil-ters (experiences) interprets events differently, constructing thusa personal reality.The sociality corollary: understanding and communicating withother people enables taking part in the social process (similar torole playing theory by George Mead).The commonality corollary: since there are common experiencesamong people, there also exist similarities in human interpreta-tions of reality (leading usually individuals to seek for validityfrom other similar people).

Kelly’s theory inspired the Bath team [56] to develop theognitive mapping technique. ‘Problem definition and problemtructuring’ were regarded by the Bath team as the basic man-gerial characteristics in order to formulate strategy developmentithin project management. Based on Kelly’s theories, operational

esearch techniques and mathematical modelling, they developedurther cognitive mapping. Cognitive mapping suggests hierar-hical order in the mapped concepts/ideas. Thus hierarchicallydentification of (model) outcomes comes through cause/effect,

ow/why. Depending on the used approach the output might be

mapping of beliefs, fears, risks, goals.What is generally termed ‘cognition’ is a representation of

uman knowledge as a set comprised of a collection of elements

Fig. 4. The pyramid of cognition.

– the ‘concepts’ – and binary operations – the ‘structure’. In termsof transparency, cognition can be represented as a pyramid (seeFig. 4) exhibiting a floating level (beliefs) and two submergedlevels (concepts and structure). Cognition becomes visualized andtractable in cognitive maps, i.e. mental models or belief systemsrepresenting information processing and decision-making byindividual actors in the face of otherwise complex problems. Whycognitive maps are important in decision-making is a story toooften told: firstly, cognitive maps raise self-awareness of com-plexities and inform others by making tacit knowledge explicit.Secondly, cognitive maps reveal in a meaningful and objectify wayhow individual perceptions of reality shape choices; it accordinglymakes critical choice parameters transparent. Thirdly, the twofirst reasons combined lead to an understanding of real trade-offsencouraging negotiation and promoting consensus.

The arsenal of cognitive mapping contains techniques aimingat revealing critical concepts in individual mental models (e.g.content analysis) but also techniques going deep into cause andeffect relationships (e.g. repertory grid techniques). Causal map-ping techniques are going deeper describing the cause and effectrelationships among critical concepts.

Brightman [55] gives a synopsis of differences and similaritiesamong four qualitative data analysis (QDA) methods: mind map-ping, concept mapping, cognitive mapping and dialog mapping:in mind mapping there is an associative relationship among ideasand concept mapping expresses any form of relationship betweenideas, while according to the same author the strength of cognitivemapping methodology lies in the causal relationships among ideasand in the fact that the maps are capable of analysis due to the ‘onlyone form of relationship’ they are depicted.

Fuzzy Cognitive Mapping is an analytical culmination of the afore-mentioned methods. It takes the deterministic cognitive maps astep further by introducing fuzziness in the structure among con-cepts. The merits of this method are extensively discussed in theremaining sections of this paper.

Fig. 5 represents a collective FCM model with the most impor-tant concepts assigned by Ukrainian laypeople for the Black Seamarine environment resilience. The example FCM model consistsof 19 concepts and 78 connections (weights) among concepts.Table 2 in Appendix explains abbreviations/descriptions of con-cepts.

3.2. How Fuzzy Cognitive Mapping can support environmentaldecision-making?

Ecosystems and geophysical systems are notoriously complexentities encompassing highly variable biotic and abiotic compo-nents. Being that the case, what does the recognition of complexityentail for methodological approaches aiming at investigatinghuman perceptions on environmental and climate change? In the

past, the analytical legitimacy of simplification has all too oftenbeen taken for granted when investigating actors’ perception andattitudes towards the natural world [57]. Today’s environmentalcrisis reveals the fact that such purposeful abstractions can prove
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roblematic when used for policy prescriptions. Fuzzy Cognitiveapping helps deepen our understanding of individual percep-

ions, and accordingly raise the odds for effective implementationf state interventions. This is sought for by revealing the cognitivespect of the main pillars of environmental web: complexity andncertainty.

Fig. 6 demonstrates the structure of an environmentalesearch strategy integrating both biophysical and socio-economicpproaches. Similar structures are widely used (implicitly orxplicitly) today in international research consortia investigat-ng specific environmental problems (e.g. RUBICODE, PERSEUS,ESAME, CLIMSAVE). It can be seen from Fig. 6 that both lay peopleerceptions and expert judgements are needed in order to close theolicy cycle: either isolated (i.e. expert judgement for biochemicalescription and environmental change processes and lay peopleerceptions for stakeholder mapping) or combined (i.e. both areeeded in modelling and risk and uncertainty analysis). Laypeoplend expert mental models were used early in 80s to illustrate publicerception of risk for problems associated with technology, envi-onmental pollutants, drugs and medical devices, natural disasters,utrition, occupational health and safety [58–64]. The distinctionetween expert knowledge and laypeople perceptions is crucial innderstanding the specific role that FCM may play in environmen-al policy research.

FCM is characterised by: (a) a knowledge base and (b) an infer-nce engine (or process). The knowledge base can be either aactual, general accepted and objectively replicated body of infor-

ation held by the epistemic community or a heuristic, lessigorous and more judgemental understanding of system per-ormance held by individuals. It is the latter kind of knowledgease that stakeholder analysis in environmental management is

oncerned about. The reasons are twofold: from a deontologicaloint of view, we seek to understand individual perceptions as aormative component of an inclusive, participatory deepening ofodern democracies. From an ontological point of view though, we

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ap of Ukrainian stakeholders.

study individual beliefs and perceptions in order to raise generalcompliance with environmental regulations. Moral hazard, asym-metric information and the ensuing impossibility of monitoringpolluting activities in totto lead to what is known in economicsas ‘principal–agent’ problem [65]. In such situations, knowing inadvance how people perceive the structure and drivers of environ-mental degradation may help in designing more effective, fair andefficient policies.

Within this context, the usefulness of FCM is primarily linkedto substantiating stakeholder mapping. Since Fuzzy CognitiveMapping is a symbolic representation for the description and mod-elling of complex systems, observing its graphical representationbecomes clear which concept influences other concepts by show-ing the interconnections between them. For the proper designof subsequent research steps it might prove crucial to know ifand how stakeholders comprehend the cyclical nature of ecosys-tem processes. And certainly they do in some cases [66]. Graspingthe cyclical nature of ecosystem processes through FCM leadsinevitably to a better understanding of how policy drivers lead toanthropogenic pressures and degraded ecosystem states. Knowinghow people perceive the Drivers-Pressure-State-Impact-Response(DPSIR) chain of events [67,68] leads us to realize what consti-tutes for them a real trade-off or a win–win option. In doing soresearchers are able to identify ignorance, misconceptions and mis-understandings, which lead to conflicts among stakeholder groups,creating the sense of gainers and losers. Environmental problemsare usually linked to global commons, e.g. natural resources charac-terised by free access and pervasive externalities leading to marketand/or institutional failures. In this respect, the perceived delim-itation of gainers and losers presupposes a specific belief in theexistence and fairness of de facto or de jure property rights. An

important contribution of FCM in this respect is informing decision-makers and researchers how stakeholders perceive property rightson the resources and a fair allocation thereof. It is in the advantageof policy makers to understand in advance stakeholder perceptions
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BIOGEOCHEMICAL DESCRIPTION

ENVIRONMENTAL CHANGE PROCESS

(DPSIR)

Global National Local scales

INSTITUTIONAL ANALYSIS:

Description and Analysis

of relevant Policies and Regulatory

Regime

STAKEHOLDER MAPPING:

ENVIRONMENTAL CHANGE

INDICATORS SET:

Analysis of Trends and their

Implications for Chosen Policy

Context;

Identification of gaps and need for new

indicators covering both ambient

quality states and wider ecosystems

changes [quantitative and qualitative

indicators]

SCENARIO ANALYSIS:

Formulation and Application of

Futures Scenarios

DATABASES

MODELLING

POLICY CONTEXT

ENVIRONMENTAL RISK AND

UNCERTAINTY PERSPECTIVES

Related to chosen scenario and chosen

policy context; precautionary principle

and critical threshold concepts

POLICY PACKAGES

i.e. targets/standards/objectives and

related enabling measures and

instruments

� Cost -effectiveness analysis and/or

� Multi criteria analysis

Regulators

Science

Other Stakeholders

Fig. 6. A Conceptual framework of environmental research strategy.S

ossaddncs

ource: [69].

n relevant property rights, in order to design proper compen-ation measures and policies. Environmental regulation built onuch knowledge has a much higher probability of stakeholdercceptance increasing potential compliance. On the other hand, aecision maker might ask for a bottom up (instead of top-down)

esign of environmental policies, seeking among stakeholders forew ideas, support/approval for already designed scenarios or poli-ies. Once you have this knowledge coming from people you maypeak ‘truth to power’.

Another merit of FCM for environmental decision-making is itspotential to work with uncertainty (a major attribute in most envi-ronmental problems) and the inclusion of dilemmas, which arecaused as side effects of policy measures and conflicting goals [70].By construction, FCM approaches uncertainty mostly in the sense

of linguistic uncertainty, i.e. vagueness in the direction and inten-sity of effects geared by the use of linguistic variables [71]. In orderto visualize this process of (subjective) preference uncertainty, thereader is reminded that after the number and kind of concepts are
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732 A.D. Kontogianni et al. / Applied

etermined, each causal relationship among these concepts isescribed by the respondents with an if–then rule that infers a fuzzy

inguistic variable from a determined set T{influence} = {negativelyery very strong, negatively very strong, negatively strong, neg-tively medium, negatively weak, negatively very weak, zero,ositively very weak, positively weak, positively medium, posi-ively strong, positively very strong, positively very very strong}.ll linguistic variables are then aggregated and an overall linguisticeight is produced which is then transformed into a numericaleight eji by using defuzzification methods. Using defuzzifica-

ion, all weights of the FCM model are quantitatively inferredrom the linguistic variables. The produced weight associates theelationship between the two concepts and determines the gradef causality between them. Although imperfect, this approach ofepresenting and eliminating linguistic uncertainty has the advan-age of supplying environmental decision-makers with essentialnformation on the uncertainty level with which specific envi-onmental relations and parameters are associated in the publicinds.

.3. How simulation of concepts may help in communicatingtakeholders’ views to environmental decision makers?

Simulation of concepts refers to the ability of FCM to performualitative simulations and experiment with a dynamic model.imulations allow for analysis of several aspects of FCMs, suchs concepts activation levels at the final state (if there is any)nd changes/trends in the activation levels throughout the sim-lation. Simulation can be performed with either all concepts or

subset of concepts that is of interest to the decision maker.his type of analysis allows investigating “what-if” scenarios byerforming simulations of a given model from different initialtate vectors. Once a FCM has been subjected to an initial stim-lus, it is possible to gain insight into a system’s behaviour bytudying the resulting stable state or cycle of states. Simulationsffer description of dynamic behaviour of the system that can besed to support decision-making or predictions about its futuretates [72].

FCM simulations are therefore thought experiments, arrived aty changing initial activation vectors and observing the resultingnal states through the simulation of cognitive process. This is sim-

lar to analysing possible futures on the basis of environmentalhange scenarios but whereas in the former case a real processf environmental change is simulated [73], in the latter case were simulating a cognitive what-if experiment. We are thus seek-ng to understand how stakeholders would have thought had theyaced a constellation of specific activation levels. The results of thishought experiments simulate the impacts of certain policy mea-ures to the stakeholders’ minds. This is a useful tool enhancingecision-making capabilities, since the decision maker is able toorecast how laypeople would perceive final states of the system,iven an exogenous manipulation of activation levels at the initialtate.

Actually, the constructed FCM model with the initial values ofoncept states and the weighted relationships among concepts issed for the simulation process. The inference process, as described

n Section 2, is implemented for the simulations performed foraking environmental policy decisions. In order to show how

he simulations are performed, let us consider the sample FCModel presented in Fig. 5. The weight matrix E, for the above

ollective FCM model, determined by Ukrainian laypeople opin-ons, is a square matrix and is illustrated in Table 3 in Appendix.

he collective FCM model was then used for analysing systemehaviour and to run management simulations. Simulations wereade by taking the product of a vector of initial states of vari-

bles (A) with the square matrix E of the collective map, where

mputing 12 (2012) 3725–3735

A is a row vector of size 1 × N, where N being the total numberof concepts (system variables). Simulation boils down to calcu-lating system states over successive iterations. System state isdefined by the degree of activation of all the concepts, which isusually limited to [0,1] interval [31]. The value of zero suggeststhat a given concept is not present in the system at a particulariteration, whereas the value of one indicates that a given con-cept is present to its maximum degree. Other values correspondto intermediate levels of activation. Consequently, the simulationrequires knowledge of an initial state vector in order to deter-mine successive states of the model, which is carried out usingEq. (1). Giving the initial state vector and the weight matrix E(Table 3-Appendix), the FCM performs reasoning and infers deci-sions as values of its output concepts. The calculated output ofthe FCM model shows how the system reacts under the assump-tions given by the stakeholders. Usually, the calculated outputis different from the expected one, thus presenting a potentialadded-value of Fuzzy Cognitive Map as a policy making tool[74,75].

In performing the cognitive simulations with FCM, we first runthe generic FCM model with a number (usually 100) of differentrandom initial states for all variables between 0 and 1 drawn from auniform distribution. We call these first runs ‘no-management’ sim-ulations. The purpose of no-management simulations is to excludechaotic behaviour of the cognitive system under examination bytesting the convergence of the system to a steady state after a finitenumber of simulations. The next step is to analyse the dynamicbehaviour of a number of policy targeted environmental scenarios.Two extreme policy targeted conditions can be analysed throughcognitive simulation: One where all initial concepts are set to zero(‘de-activated’). The simulated results, characterised by high val-ues of certain concepts, can be perceived as an upper bound forthe performance of the studied system, conditioned by the basicstructure of respondents’ cognitive reality. In an alternative con-text where all concepts are activated, the final values of our systemcomponents decrease dramatically presenting a lower bound forthe perceived state of our system. In a final step, intermediate pol-icy scenarios are developed based on a number of ad hoc, simulatedchanges where individual concepts are randomly activated and thefinal state of the FCM system under these scenarios determined[39,66,75].

Let us give an example of the cognitive simulations performedfor the sample FCM model. At first lets consider a scenario (Sce-nario I) where all the initial values of concepts are set equal to 0(i.e. “de-activated” concepts). In this case, the FCM simulates usingEq. (2) and the final values of our system components are illustratedin third column of Table 1. The values of the two output conceptsbiodiversity (Bd) and ecological state (ECOL) have been increased,showing the impact of this scenario on system’s biodiversity andecological state. Next, let us consider a scenario where all the initialvalues of concepts are 1 (i.e. “activated” concepts). In this case, theFCM simulates using Eq. (2) and the final values of our system com-ponents are illustrated in fifth column of Table 1. The values of thetwo output concepts (Bd and ECOL) have been decreased dramati-cally, showing the impact of this scenario on system’s biodiversityand ecological state of marine environment. Finally, we consider ascenario (Scenario III) where three of the initial concepts, the mostcentral ones, are de-activated. These concepts are: MSW (Munic-ipal Solid Wastes), HA (Human Activities), S (Urban Sewage). Allother 16 concepts (Table 2-Appendix) are considered as activatedtaking values equal to 0.5 according to modified inference processin Eq. (2). In this case, the FCM simulation produces the results

illustrated in the seventh column of Table 1. The simulated resultsshow that the system can increase its biodiversity and ecologi-cal state, thus accomplishing the targets set in the policy-makingscenario.
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Table 1Simulation results of cognitive scenarios for Black Sea stakeholders.

Concepts Initial state-Scenario I Final state-Scenario I Initial state-Scenario II Final state-Scenario II Initial state-Scenario III Final state-Scenario III

AOSP 0 0.368 1 0.632 0.5 0.368BD 0 0.9969 1 0.0031 0.5 0.9969CD 0 0.392 1 0.608 0.5 0.392CW 0 0.2452 1 0.7548 0.5 0.2452D 0 0.2884 1 0.7116 0.5 0.2884DFS 0 0.0346 1 0.9654 0.5 0.0346HAB 0 0.0219 1 0.9781 0.5 0.0219HA 0 0.2613 1 0.7387 0 0.2613IA 0 0.5687 1 0.4313 0.5 0.5687ISP 0 0.2519 1 0.7481 0.5 0.2519LF 0 0.3542 1 0.6458 0.5 0.3542MP 0 0.0331 1 0.9669 0.5 0.0331MSW 0 0.1298 1 0.8702 0 0.1298PST 0 0.3632 1 0.6368 0.5 0.3632RP 0 0.3638 1 0.6362 0.5 0.3638S 0 0.4244 1 0.5756 0 0.4244Sphi 0 0.0084 1 0.9916 0.5 0.0084Tourism 0 0.7034 1 0.2966 0.5 0.7034ECOL 0 0.9912 1 0.0088 0.5 0.9912

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. Identification of potential application of FCM innvironmental economics

FCM has been selectively applied in environmental researchn order to model ecosystems, assess local knowledge in naturalesource use, anticipate management outcomes, design semi-uantitative scenarios and as a communication and learning tool35,76]. In this section we plea for a concise application of FCMn a specific area of environmental management, the so calledxternality or non-market valuation. Externality or non-marketaluation of environmental resources is a branch of environmen-al economics specializing in assessing how people assign values tonvironmental goods and services not supplied, and therefore notalued directly or indirectly, by private markets [77,78]. The usualutput of non-market valuation studies is estimates of individuals’illingness-to-Pay (WTP), a welfare measure based on the theoret-

cal premises of neoclassical welfare economics [79]. Non-marketnvironmental resources leave by definition no behavioural tracesn markets; a fact that makes public surveys a central pillar of theiresign and implementation. Notwithstanding the fact that indirectethods based on surrogate markets of related private goods can

n principle be used to elicit non-market values of environmentalesources, direct methods of stated preferences – such as contin-ent valuation and choice experiments – are now widely used tonform policy makers [80,81]. WTP estimates are widely used interlia for: raising awareness, conducting social cost–benefit analysis,upplementing National Income Accounts, and estimating compen-ation claims in natural resource damage assessment.

Since the early days of non-market valuation studies, the needas felt to increase their reliability by combining quantitative esti-ation of WTP with qualitative, in-depth exploration of related

actors such as: pro-social norms, attitudes, motives, perceptions,nfamiliarity, stability and consistency of preference [82,83]. Toate, most of qualitative research in non-market valuation reliesn focus groups. Focus groups have been used both in explorative,x ante understanding of preference parameters as well as in anx post manner, validating WTP estimates [84]. Notwithstandinghe merits of focus groups as a technique informing WTP estimates,revious empirical research is not conclusive regarding the relative

erformance of focus groups vis-à-vis other qualitative techniquesuch as in-depth interviews [85,86]. FCM offers a promising alterna-ive but has not been used up to date in this domain. This, usually adoc collected and analysed piece of data could thus be organized in

a more coherent theoretical basis and deliver a solid cognitive andknowledge-based substrate to WTP estimation. We suggest thatthe FCM-based section of the questionnaire precedes the valuationscenario and the WTP/WTA question.

[87] uses ‘cognitive mapping’ interview techniques to shed lighton the segment of the population holding measurable value for theresource (the extent of the market) and their perceptions regardingthe scope of the resource to be valued (the extent of the resource).Nevertheless, the use of the term ‘cognitive’ in [87] is misleading;‘cognitive maps’ are hand drawn geographical sketches mirroringhow lay people perceive space and activities in space, not graphtheoretic replications of cognitive structures about cause/effectsrelationships.

[88] represents a first attempt to develop a fuzzy logic inferencemodel to relate consumer perception variables to the purchas-ing probability of solar panels. In technical terms, the purpose oftheir research was to determine the purchasing probability of res-idential solar panels among a sample of homeowners in Arizona,United States. Using data from a questionnaire survey, the authorsdemonstrate the role of three perception variables (perceived cost,perceived maintenance requirement and environmental concern)in influencing consumer acceptance of solar panels. By definingthe main model output as the purchasing probability of residen-tial solar panels, the authors in fact estimate a probabilistic versionof WTP similar to the one calculated in close-ended, dichotomouscontingent valuation applications.

[75] is to our knowledge the only example up to date of a FCMapplication that comes close to addressing the issue of WTP. Theauthors apply FCM to model perceptions of stakeholders regard-ing the current state of water resources in the Pinios river basin(Greece), drivers of environmental change, potential solutions andthe possible implications of different water resources managementpolicy options. During the FCM exercise, respondents were askedabout the inclusion of direct, environmental and resource costs ofurban and irrigation water uses into water charges expressed aswelfare losses due to misallocation of water resources in the region(i.e. WTP). By estimating 30 individual and one social cognitivemap (the ‘Social Current State Map’) the authors are able to gaininsight on respondents perceptions relating, among other things,

to environmental costs and the effect of water pricing on waterconsumption. They discern groups of stakeholder with a positiveattitude towards environmental costing and reasons for disagree-ment.
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734 A.D. Kontogianni et al. / Applied

. Discussion

Discussing causal inference in FCM in 2000, Miao and Liu [33]sserted: “the current techniques for constructing and analysingCMs are inadequate and infeasible in practice. Furthermore, ashe FCM is a typical, nonlinear system, the combination of severalnputs or initial states may results in new patterns with unexpectedehaviours. Systematic and theoretical approaches are required forhe analysis and design of FCMs”. Since then, FCM has come ofge: a considerable literature on the topic witnesses the progresschieved as well as the remaining open questions. Analysis ofomplex environmental problems on the basis of Fuzzy Cognitiveapping can promote communication among stakeholders, expert

roups and policy-makers. This is achieved through the potentialf FCM in quantifying/analysing qualitative data, cognitive simula-ion of environmental scenarios and capturing uncertainty in theorm of fuzziness. That way we get a better understanding of stake-olders’ cognitive perception about complex ecosystem functions,

parameter substantially co-determining the limits and possibili-ies of environmental policy, management, and enforcement. Thiss especially needed in the environmental domain where complex-ty and uncertainty loom large and substantiation of individuallyeld values for the natural world is a prerequisite for deliberativend inclusive policies.

We propose that future research in this domain be directedowards integrating FCM techniques in stated preference sur-eys. This can be achieved by substituting the opening section oftated preference questionnaire by FCM based questions. Since thepening section of stated preference questions usually addressesndividual beliefs and perceptions about the goods and servicesnder valuation, such a suitable substitution would enhanceubstantially the analytical ability to link perceptions to stated eco-omic values. We call this approach “a synchronous FCM/economicaluation” approach. This can be especially useful in the casef deliberative monetary valuation. The usual, “asynchronous”pproach is the ex ante and ex post application of FCM to non-arket valuation approaches. The documentation and analysis of

takeholders’ cognitive maps will presumably offer insights into aumber of technical problems such as the definition and extend ofontingent markets, the appropriateness of attributes and levels inhoice experiments, the framing of scope sensitivity tests, use andimitations of local knowledge and management.

It goes without saying that, as in any modelling endeavour basedn surveys, conclusions of FCM analysis must be carefully quali-ed. The usual qualifications refer, inter alia, to the dependencyf surveys and interviews on the framing of questions asked, theisrepresentation of perceptions due to the dynamics of group

licitation, and last but not least strategic elements in respondents’ehaviour.

cknowledgements

This research is partially funded by the EU-FP7 Collaborativerojects PERSEUS Contract No. 287600 and CLIMSAVE Contract No.44031. Part of this research was also funded by EU-FP6 Integratedroject SESAME Contract No. 2006-036949.

ppendix A. Supplementary data

Supplementary data associated with this article can be found, inhe online version, at http://dx.doi.org/10.1016/j.asoc.2012.05.003.

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