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Delivery date: April 30, 2015 Authors: Jean-Luc De Kok VITO E-mail : jean-luc.dekok@vito.be Lieve Decorte VITO E-mail : lieve.decorte@vito.be Jennifer Bailey NTNU E-mail: jennifer.bailey@svt.ntnu.no Rachel Tiller NTNU E-mail : rachel.tiller@svt.ntnu.no Russell Richards Griffith University E-mail : r.richards@griffith.edu.au

OCEAN CERTAIN FP7-ENV-2013.6.1-1

Project number 603773

Deliverable5.1

Qualitative Design of the DSS

WP’s leader: VITO

Principal investigators: Jean-Luc De Kok , VITO (B)

Lieve Decorte , VITO (B) Jennifer Bailey, NTNU(N) Rachel Tiller, NTNU(N)

Russell Richards, Griffith Univ. (AUS)

Project’s coordinator: Yngvar Olsen, NTNU (N)

Version nr. & date:

Version 4.0 (Final), 28.04.2015

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1. Introduction Typically, DSS are computer-based information systems developed to assist decision makers to address semi-structured or ill-defined tasks in a specific decision (Sprague and Watson, 1993; Uran and Janssen, 2003; Engelen, 2004; Giupponi, 2007; De Kok et al., 2009). They provide a formal analytical platform for support by allowing decision makers to access and use data and appropriate analytic models (El-Najdawi and Stylianou 1993). The terms ‘semi-structured’ and ‘appropriate’ used here refer to the fact that Decision Support Systems are typically applied to find answers for problems that, due to their specific nature and complexity lack an unambiguous solution method. Rather, usage of the most appropriate analytical solution methods available approximates the unique answer. Thus, the DSS provides the decision maker(s) with a suit of analytic models, which are considered appropriate for the decision domain. But a DSS is more than a model base alone. Typically three more components can be distinguished (Engelen et al. 1993); (1) a user interface enabling easy interaction between the user and the system, (2) a database containing the required data of the domain and the area at study to drive the models, and (3) a tool base with the methods, analytical techniques, and software instruments required to work in an effective manner with the domain models and the data (Error! Reference source not found.). Data may refer to any kind of knowledge or information, hence the often cited term knowledge base.

User interface

Model- base

Data- base

Tool-base

Figure 1: Basic functional components of a Decision Support System Decision support systems in the domain of environmental management have been developed from the early 90s onwards, with varying degree of acceptance among stakeholders, end users and scientists. The purpose of a DSS is to translate scientific knowledge and data into a format that is understandable for environmental managers and, often, the general public, thereby bridging the gap between science and policy. The challenge lies in balancing the complexity and robustness of the scientific models used and the accessibility and utility of the DSS for decision makers. DSS in the water management sector usually consist of simulation models, and/or of techniques and methods for decision analysis, recently extended to include the support to participatory processes. Therefore, a DSS typically integrates multi-source geographically referenced data and data management systems, a variety of models and elaboration procedures within a customized user interface. Emphasis is given to hydrologic models accompanied by environmental assessment and/or socio-economic evaluation. The models include those aimed at reconstructing and simulating the physical reality, and those constructed to manage divergent objectives and to find a compromise among the expectations of different actors in a participatory process

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(Giupponi et al. 2007). An ICZM-DSS can be defined as a computerized system capable of supporting and assisting decision-making in ICZM (Westmacott, 2001). Decision support tools have been developed since the 1970’s. Unfortunately, DSS have found very limited implementations in the real world, thus demonstrating that most of the DSS tools developed so far have failed to meet the objective of being used in the real world (Unran and Janssen, 2003; Giupponi et al. 2007). The are several drawbacks to the development of a DSS. Several authors that review DSS (Westmacott 2001, Engelen 2004, Guipponi et al. 2007; De Kok and Wind, 2003; De Kok et al., 2009) blame ineffective communication between the DSS developers, scientists and end-users as the major reason for failure. Good communication is indeed the key to overcome many obstacles to the acceptance of models. These obstacles include (Westmacott 2001):

Resistance and hesitation towards new technology

Suspicion about objectivity of the models used in the DSS

Commitment to existing non-threatening concepts

Difficulty in understanding new concepts

Resistance to aggregating results that go beyond the scale of the individual

Different interests in distribution effects of who will gain and who will suffer from new policies vs. efficiency gains measured in economists or ecologists terms

Apparent irrelevance due to failure to identify what questions are of primary interest DSS are generally too complex for end-users. This is also a conclusion of the EU demonstration program for ICZM. Managers and policy makers have problems with applying complex DSS. This is especially evident when a spatial context is added. Uran and Janssen (2003) show in a review of spatial DSS for ICZM that there is a mismatch between the users ability to use spatial information and map design. Spatial information is not easy to use, yet people have a high preference for, and affinity with, maps. In the attempt to cope with issues of increasing complexity, methods and computer tools have shown a tendency to become more and more sophisticated and complicated. So there is a growing gap between the specialised knowledge of the DSS developers and the application of this knowledge in decision making. A third problem that returns in review studies is what can be defined as the knowledge acquisition bottleneck. It is the difficulty of transferring any kind of knowledge from the expert to the computer as well as from the computer to the user. Large gaps exist between DSS data requirements and data available to users, as well as between intuitive expert knowledge and final and concrete data input. Decision making inevitably has to deal with uncertainty (Van Kouwen et al. 2007). Trends in climate change, economic development or social and political systems are hard to predict without addressing the effects of epistemic and stochastic uncertainty. Parameters describing these processes therefore contain a degree of uncertainty as well. Often, DSS are built on the assumption that parameters, and the relationships between parameters, are exact. Consequently, DSS do not cope with uncertainty or with unexpected information gaps. The adoption of the

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DSS should encourage the competent administration to deal with the various sources of uncertainty and include their treatment in the communication of results (Guipponi et al. 2007). One final problem that deserves attention is language: most DSS are developed in English, but not all the users are English-literate, which is a barrier to effective communication between the DSS developers and users. In Ocean-Certain, this will be addressed by organizing the stakeholder workshops in the local language (i.e. Norwegian, Turkish and Spanish). . Input must be summarized into English and the output must ultimately be transmitted to key stakeholders in the relevant language. Successful application of a DSS depends on:

End-users and policy makers who are willing to participate in the development and engage a long-term commitment.

Developers and scientists who try to understand how people learn so that this knowledge can be applied into DSS design

A collaboration of all parties involved communicate with each other throughout the entire development process.

In the development of a DSS one can distinguish between a conceptual, qualitative phase and the quantitative, implementation phase. Stakeholder engagement throughout the entire model development process helps to strengthen communication between researchers and end-users and will increase the probability of implementation (Sterman, 2000). The conceptual phase is aimed at identifying the problems at hand, the potential users of the DSS or project clients, and, most importantly, a qualitative system description. The latter is a conceptual model linking the problems to their causes and the solutions. The implementation phase is aimed at developing the quantitative model concepts, collecting the data to parameterize equations, computer coding, and the design and implementation of the models and user interface (Figure 1). Ideally, the existing scientific models and data needed to describe the physical, ecological and social processes underlying these cause-effect relationships are available at the beginning of the project or are developed before the implementation stage. The common practice, however, is that the scientific developments in project are not in line with such an optimal planning process. Taking this lesson in mind, a flexible, generic framework will be developed in the OCEAN-CERTAIN project. The flexibility of the DSS framework is based on the exchange of information between the different models used, and their scheduling, focusing on the model integration and user control options. This means that representative input and output of these models needs to be specified, without the models being already available. Different approaches can be followed to simulate a system of interacting models, depending on the end-user needs, human resources and data available. For example, a metamodel can statistically describe the dependencies between model output and input, or the model can be reconstructed in a simplified version taking into account the basic model mechanisms. A database exchange concept will be applied in OCEAN-CERTAIN. The technical details of the DSS architecture will be discussed in Deliverable D5.4 ( Design Report I). Here, we focus on the conceptual development stage: 1. An exploration of the problem resulting as direct or indirect consequence of climate

change-driven changes to the ocean carbon pump mechanism (the biological pump) 2. A definition of the (potential) end users and interest groups that could benefit from the

DSS 3. A qualitative, conceptual model of the “system” underlying the DSS model base

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4. An introduction to the semi-qualitative modelling technique of Fuzzy Cognitive Mapping with examples for the OCEAN-CERTAIN project

End users and stakeholders are to be involved as early as possible in the development process. This is to avoid false expectations, obsolete efforts, and a lack of interest or trust in the final product. For practical reasons the social-economic work package did not manage to organize the interactive workshops with stakeholders in the first project year, and these were postponed to the second year (2015). Nevertheless, stakeholders were approached informally on different occasions, including the WOC Business Forum organized in New York in September 2014. The resulting information was used to fine-tune the conceptual system model that was constructed by the project partners. In addition, a mock-up interface of the DSS was prepared and scientific experts and the project partners were presented with different options for the DSS to obtain more detailed information on the functional requirements (Figure 2).

Figure 2. DSS Mock-up interface (VITO, June 2014). The problem formulation (De Kok and Wind, 2000; De Kok et al., 2009) is a comprehensive analysis aimed at identifying the problems to be addressed and users of the DSS (stakeholders and managers), linking these problems to the causes by means of a qualitative system description and examining potential solutions. A purely technical approach is long known to be superseded (Westmacott, 2001); the vulnerability and adaptive capacity of stakeholders are important aspects of the DSS design which should be included in the analysis. The design of a DSS is a transdisciplinary process which is is why the social sciences play a key role in the development of the DSS (WP3). The integration of natural and social sciences is not straightforward, primarily due to the differences in scientific language, methodology and areas of expertise. Whilst DSS are

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characterized by computer coding and quantification, semi-qualitative modelling techniques have been developed and successfully applied to implement conceptual models addressing stakeholder interests. Ideally, these models are developed interactively with stakeholders. An interesting technique is that of Fuzzy Cognitive Mapping (Kosko, 1986), which helps researchers identify and analyze the feedback mechanisms of systems interactively. Figure 3 shows how Fuzzy Cognitive Maps (FCMs) contribute to the integration of the OC work packages.

WP6

Conceptual Model

Quantitative Model

WP1

WP3

WP2

WP4

data mining

text mining

WP5

feedback linkages

quantification

social

responsestakeholders

system

model

interactiveproblem structuringwith FCMs; scenario analysis with DSS

scenarios

adaptivemanagement

strategies

text mining input to FCM design

web-based

dissemination

data delivery

thematic

focus

marine database

BNNs

model

improvement

Figure 3. WP integration in OCEAN-CERTAIN. We will first discuss the problem definition for the OCEAN-CERTAIN project and then examine how fuzzy cognitive mapping are being applied in the project.

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2. Contributions of partners

VITO is responsible this deliverable (main author, coordination + quality control). NTNU and Griffith University were responsible for quality control of this and the scientific work underlying the example FCM VITO took the results from the workshop and built a simplified model using FCM techniques to understand the impact of different variables and scenarios of climate change. 3. Quality control Version 1.0 delivered March 13, 2015. Version 2.0 delivered April 8, 2015. Version 3.0 deliver April 24, 2015. Final Editting and Version April 28, 2015. Corrections Griffith University, April 9, 2015. Corrections NTNU, April 22, 2015. Quality control by partners and Final Version delivered April 29, 2015.

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4. Problem definition and conceptual system model The Biological Pump refers to the mechanism of fixation of atmospheric carbon to organic carbon in the ocean through photosynthesis and food-web interactions (Volkt and Hoffert, 1985; Longhurst et al., 1989). The Biological Pump is not to be confused with the Microbial Pump, referring to the microbial transformation of organic carbon to other states ( Jiao et al., 2011). Both mechanisms contribute to the export and sequestration of carbon. Although the general mechanisms of the biological pump are understood, the uncertainties in the parameterization make quantification difficult. The most important and direct factors affecting the biological pump are the sea temperature, the availability of nutrients and the acidity (pH and carbonate chemistry) of the ocean, all of which are indirectly or directly related to the anthropogenic changes. OCEAN-CERTAIN does not address the role of the biological pump in the global carbon cycle. Instead the project examines the influence of these stressors on the basic mechanisms underlying the biological pump and the (in)direct impacts that changes in these stressors can have on marine and coastal stakeholders for the selected case studies (Mediterranean, Arctic Sea and Patagonia). The modelling effort of WP2 focuses on two modules: the lower trophic level (LTL) of organic matter and bacteria and higher trophic level (HTL) of fish. These two levels interact through the grazing of marine plankton by fish. A general, rapid, problem analysis by experts during the first project meeting (Nov. 2013) resulted in a generic conceptual model (or “horrendogram”) of the system (Figure 4). The project identified seven stressors: 1) Reduction in light; 2) Changes in Micro and Macro Nutrients; 3) Increases in Temperature; 4) Acidification; 5) Deoxygenation; 6) Pollution; and 7) Overfishing. The idea of the horrendogram was to link these stressors to the indicators through the physical, ecological and social-economic feedback mechanisms. It is a pictorial representation of the outcome of the exercise, fullfilling more functions (as will become clear). Positive (reinforcing) and negative (balancing) system feedback can result in unexpected, counterintuitive response to management solutions aimed at solving problems and is the basis paradigm underlying the design of the DSS. Conceptual system models are very useful and serve several purposes. The diagram is a network of concepts of concept network reflecting the outcomes of exchanges between scientists on the nature and causes of problems. These can support the communication with stakeholders and serve as a qualitative layout for the design and/or context of [numerical] models and the DSS architecture .

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Figure 4. Conceptual system model for the issues addressed by OCEAN-CERTAIN.

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The complexity of the diagram reflects the nature of the discussion, which only lasted half an hour but involved the majority of the experts present, and the complexity of the system feedback structure. Polishing of the diagram is needed before further use. However, the diagram points to the three sectors which are potentially vulnerable for changes in the biological pump mechanism: fisheries, fish farming and tourism. The diagram of Figure 4 is purely conceptual and does not show the spatial-dynamic complexity of the processes included. Although quantification of all interactions will be difficult, and in some cases undesirable or impossible, the key feedback mechanisms are included.A number of conclusions can be drawn from Figure 5 Fishing pressure should be included as human stressor on the system. The occurrence of jellyfish was identified as a relevant problem because it affects the fish community and tourism, and should be included as well. Some variables can be combined, and a clearer distinction between the exogenous drivers of the system and internal variables will be needed. The general conclusion from the researchers was that the qualitativ system diagram was still too complex to be used for the DSS architecture and a simplified working version of the diagram was needed for WP5 and the exchanges with WP2 and WP4. The process of progressing the model towards a DSS includes reducing the number of variables by combining variables (i.e. where variables had some shared attribute) and making a clearer distinction between the exogenous drivers of the system and internal variables. A revised update of the diagram was prepared as a result of meeting with the WP2 modelers in Lowestoft, UK (April 2014). This resulted in a diagram where the LTL and HTL processes and rivers were separated more clearly and reduced the number of variables to 29. An update of the diagram is shown in Figure 5.

Figure 5. Update of the conceptual system model, version June 2014 (Sonja van Leeuwen, CEFAS – with adaptations). Depending on the nature of the interactions in a closed loop the feedback may have a positive (reinforcing) or negative (damping or balancing) effect. Positive feedback can result in increased change in variables (for example exponential population growth), whereas negative feedback

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results in a decline in the rate of change of variables (e.g. nitrogen limitation for primary production). More complex system ‘behavior’ occurs when there are multiple, interacting feedback mechanisms (e.g. population dynamics has both reinforcing (births) and balancing (limits to growth) loops) that are subject to time delays and where there are thresholds or maximum capacity levels of variables. For example, the fish stocks in Figure 5 are divided in lower trophic level of non-predatory and higher trophic level of predatory species (not be confused with the lower, green, and higher, blue trophic level of the system as a whole shown in Figure 5). The feedback is negative: non-predatory species serve as food source for the higher trophic level species and hence have a positive impact whereas the higher trophic level species grazing on the lower trophic level species (predation mortality) and have a negative impact. The majority of the feedback loops in the diagram are negative (balancing). The type of model that quantifies these feedback structures is referred to as System Dynamics Model (SDM) and was developed in the late fifties to address business problems first. Later, SD models were developed to analyze urban change and the depletion of world resources (Forrester, 1961; Forrester, 1969; Meadows, 1972; Senge, 1990; Sterman, 2000). SD models represent systems at the outline level of key variables, the focus and “intelligence” of the model is in the feedbacks rather than the scientific complexity of the models describing the individual linkages in the system. In the project SD modelling is used primarily to link up the different modules and integrate the models developed in WP2. Typically, SD models are based on difference equations describing the change of a state variable X(t) in a time step Δt as follows:

X(t+Δt) = X(t) + Δt × Rate of change of X

The rate of change of variable X(t) is affected by other state variables e.g. Y(t) interacting with the variable. It can have time delays, adding to the complexity of the bahvior that can be generated with the model if needed. Each equation in an SD model requires quantitative information and parameter settings. This information is difficult to retrieve in case a process is subject to scientific uncertainty, or when quantification is less straightforward as is the case with social-economic and political processes. An example is the impact of Adaptive Capacity on Vulnerability in the diagram. How does one quantify Vulnerability? It may be possible to measure the Adaptive Capacity and Vulnerability of e.g. a fishing community on a scale ranging from 0 to +100 but the dependency between these two variables is represented in the form of equations or graphical functions. It is relatively easy to add a large number of variables and linkages but this increases the amount of data needed for their quantification. During the design phase of a DSS one may want to analyze the feedback mechanisms conceptually without a need to quantify the model. This is the case, particularly, when scientists and stakeholders are joining forces to achieve consensus on the nature and causes of problems and potential solutions. Fuzzy Cognitive Mapping (Kosko, 1986; De Kok et al., 2000; Kok, 2009; De Kok et al., 2015) and Bayesian Belief Networks (Richards et al., 2013) are two techniques that can help bridge the gap between qualitative concept networks andcomprehensive quantitative models. The difference is that FCMs use a process-based approach to focus on the feedback mechanisms, whereas Bayesian Belief Networks (BBNs) use a probabilistic approach to explore acyclic causal structures. Both techniques are used in OCEAN-CERTAIN, but for WP5 the priority is given to FCM modelling because of the natural platform it provides for analyzing system feedback (see next Section). During an informal discussion between the WP3 and WP5 partners held in Trondheim (January 2015) it was decided to apply BBNs for the initial encounters with stakeholders in April/May

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2015. FCMs will be used in the second iteration of the workshops in the third project year (Spring 2016).

5. Fuzzy cognitive mapping

Fuzzy Cognitive Maps or FCMs (Kosko, 1986; De Kok et al., 2000; Kok, 2009; De Kok et al., 2015) are directed, causal graphs which can be used to describe the dynamic feedback behavior of systems of varying complexity and help bridge the gap between qualitative and quantitative knowledge. FCMs take concept networks one step further by assigning weights in the range [-1,+1] to each of the linkages in the diagram (the range can be adapted if necessary). The weight of a link expresses its strength and the nature of the impact on a variable. A positive weight implies that a variable is affected positively in case of an increase of a variable (or a negative impact in case the influencing variables decreases), when all other variables in the diagram remain stable. Ideally, the weights take on values in the full range [-1,+1], but FCMs can also be used with weights taken from the discrete set {-1,0,+1} if further differentiation is difficult. However, this reduces the complexity of the FCM and behavior that can be examined. FCM analysis is based on a step-wise, iterative processing of the system model. Time dependency is implicitly present but the results of the computations with an FCM cannot be interpreted on a temporal basis. The system feedback is analyzed step-by-step without explicit specification of the time. The reason for this is that time delays are not accounted for in the weights. The computations are repeated until the system reaches an equilibrium state or shows periodic behavior (some examples presented during the IMBER 2014 conference held in Bergen are shown below). The example will be used to explain the methodology in a bit more detail. Attempts have been made in the past to upgrade the FCM methodology (Hagiwara, 1992), but this makes the approach more complex and less intuitive for interactive use with stakeholders which is the prime strength of the method. Strengths of FCMs (more or less in order of importance):

the ability to represent and analyze system feedback in an intuitive and transparent way using graphics, allowing quick and interactive use with stakeholders

the usefulness as a semi-qualitative platform to ensure the consistency of conceptual models

domain independence and no limitations to the complexity of the concepts used in the FCM

the ability to combine FCMs constructed from different viewpoints, for example if experts disagree on the causalities or weights

the simple matrix algebra underlying the computations

Some limitations to mention are:

the lack of conditional rules (If … Then) in the description of the links

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the linearity and lack of time delays in the links

sensitivity of the results for some technical aspects of the computations

a lack of user-friendly software to support the design, construction and application of FCMs

Interesting tools for FCM analysis are Topic-FCM and MentalModeller http://www.mentalmodeler.org ). The latter tool support web-based construction and use of FCMs. Nevertheless improvements of the tool are still in progress. The construction and use of an FCM is based on six steps, including iteration (Figure 6).

Figure 6. Steps in FCM analysis (IMBER, 2014). Once the causalities have been identified the weights need to be assigned based on expert knowledge. Compromise weights of averages can be applied when the experts disagree on the strengths of the causalities. The strength of a complete feedback loop is determined by the product of all the causalities in the loop. The actual computations with the FCM take place in Step 4 and are based on simple, linear matrix algebra to obtain the new vector of all system concepts (variables) from the values for the previous step. For the majority of FCMs this can result in values exceeding the allowed range [-1,+1]. In this case the values can be clipped off at the range between a minimum and maximum using so-called “squashing” functions. The type of squashing function used can affect the results (Tsadiras, 2008). For this reason it is important to test different squashing functions. The algorithm used for the variable update is:

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Si � f j j �

Si � f t t

W11 W1N

…

…

…

WN1 WNN

example trivalent squashing: f(x) =

We will now discuss an example developed for the project and presented at the IMBER 2014 Open Science Conference following the second bi-annual project meeting held in Bergen (June, 2014). The example used describes the conflict between fish farming and catch fisheries, which was identified as a key problem for the case studies of the project. Fishermen may, for example, destroy fish farm nets with their lines. On the other hand, fish farms may affect the quality of wild stocks due to the introduction of new species (bio invasion) or pollution. Social implications, such as changes in employment or job migrations can be expected as well. Furthermore, the gear of fishermen may be lost. The complexity of the conflict is well discussed in (Tiller et al., 2013) and their qualitative model has been used to derive an FCM (Figure 7 below). The conflict has spatial planning aspects which cannot be addressed easily with an FCM in the way spatial planning tools can (Engelen, 2004). Still, an FCM can help illustrate and compare different scenarios qualitatively, which gives an added value to the analysis.

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Figure 7. Fuzzy Cognitive Map for the conflict between catch fisheries and fish farming (adapted after Tiller et al., 2013). The first step was to assign the weights to the linkages in the conceptual model, which provides the actual FCM for the problem. Following the example of an FCM for bio-energy (Penn et al., 2013) we used a 7-valued weight set, chosen from the set {-0.7 (strong negative impact), -0.5 (medium negative), …, +0.7 (strong positive)}. Fat lines shown in Figure 7 indicate the feedback loops present in the FCM. It is useful to distinguish between three types of variables in the FCM (Papageorgiou and Kontogianni, 2012):

exogenous driver variables or so-called “transmitters” which influence other variables within the system, but are not affected themselves (for example Climate Change)

endogenous or “ordinary” variables which are influenced by one or more variables, but also have an impact on one or more variables (for example Gear Loss); as usual, the majority of the variables belongs to this category

outcome or “receiver” variables which are at the end of the chain and are affected by one or more variables without affecting other variables (for example “Conflict”)

A proper balance in the distribution over the three variable types reflects the quality of the FCM. FCMs with a large number of transmitters are less flexible, the behavior of the system is not generated by the feedback but imposed externally, making the system less self-organizing and “intelligent”. Two related FCM characteristics, taken from graph theory, are:

the connectivity: ratio of the number of connections and the squared value of the number of variables (which is the theoretical maximum number of connections); for a fully connected FCM this is 1.0

the ratio of the number of receiver and transmitters – a measure of the complexity of the FCM; an FCM with a large value of this ratio primarily generates the system behavior endogenously

In this example the FCM has two receivers, two transmitters (Climate Change and Fishing Effort) and 26 ordinary variables. The total number of connections is 37. The connectivity is 0.055 (rather low compared to other examples of FCM found in the literature) and the complexity is 1.0 (rather low as well). Systematic analysis of an FCM to trace all feedback loops becomes more difficult for complex FCMs. For the example it turned out there are five negative and five positive feedback loops. Multiplication of the connection weights encountered in a feedback loop gives an indication of the total strength compared to other loops. An adaptation of the “brute force” Iterative loop Counting Algorithm (ILCA) developed by J. Kirk (Kirk, 2012) was used to trace all loops, using over 10.000 iterations. So-called self-loops (variables affecting themselves) are permittable, but not present in this example.

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Seven negative feedback loops (with partial overlaps) were identified:

AdaptationVulnerabilityAdaptation (weight -0.14)

Fish Stock SizeOffshore CatchFish Stock Size (weight -0.49)

AdaptationFishing IncomeVulnerabilityAdaptation (weight -0.07)

Access to Fishing GroundsOffshore CatchFish PriceFishing IncomeFisheries

Sector InfluenceDegree of MSP (Marine Spatial Planning)Room for Fish

FarmingAccess to Fishing Grounds (weight -0.004375)

Animal WasteFish Stock QualityFish Stock SizeOffshore CatchFish

PriceFishing IncomeFisheries Sector InfluenceDegree of MSPAnimal Waste (weight -0.001225)

Animal WelfareFish Disease Fish Stock QualityFish Stock SizeOffshore

CatchFish PriceFishing IncomeFisheries Sector InfluenceDegree of MSP Animal Welfare (weight -0.000245)

Access to Fishing Grounds Offshore Catch Fishing Income Fisheries Sector

InfluenceDegree of MSPTraffic ControlAccess to Fishing Grounds (weight -0.01225)

Six positive feedback loops:

Access to Fishing GroundsOffshore CatchFish PriceFishing IncomeFisheries

Sector InfluenceDegree of MSPTraffic ControlAccess to Fishing Grounds (weight 0.006125)

Fishing IncomeVulnerabilityFishing Income (weight +0.007)

Animal WasteFish Stock QualityFish Stock SizeOffshore CatchFishing Income

Fisheries Sector InfluenceDegree of MSPAnimal Waste (weight 0.00245)

Animal WelfareFish DiseaseFish Stock QualityFish Stock SizeOffshore

CatchFishing IncomeFisheries Sector InfluenceDegree of MSPAnimal Welfare (weight 0.00049)

Degree of MSPIndustrial WasteGear LossFishing IncomeFisheries Sector

InfluenceDegree of MSP (weight 0.0028)

Access to Fishing GroundsOffshore CatchFishing IncomeFisheries Sector

InfluenceDegree of MSPRoom for Fish FarmingAccess to Fishing Grounds (weight 0.00875)

The feedback loops with the largest strength have been indicated in blue and red (note: the values should not be interpreted in the absolute sense). It turns out that Vulnerability and Access to Fishing Grounds are among the variables playing a key role in the feedback structure. This is due to the way the FCM has beendesigned. A more reliable examination of the significance of specific variables for the feedback structure and system behavior is to run scenarios and remove connections, which will be done now. In addition there are a number of bi-directional feedback loops between two variables which are of reasonable strength and importance: We compare two scenarios. Scenario A is characterized by a sustained high fishing effort and increasing climate change, in scenario B the fishing effort is reduced step-by-step. Imposing such

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changes is only possible for the transmitters and “forces” the system. Figure 8 shows the changes for a selection of variables over 25 iteration steps for Scenario A, starting from a medium value of (zero) for all state variables except the fishing effort and adaptive behavior. A clear distinction exists between the transient phase in which the system is highly dynamic, and the equilibrium phase.

high

low

normal

transient phase equilibrium phase

Fish stock

decline

iteration step

income

decline

vulnerability

follows income decline

adaptation follows

vulnerability

stock equilibrium

Figure 8. Scenario A (sustained high fishing effort). Following the changes step-by-step we observe how the system behavior changes to a gradual development of the equilibrium state. The fishing community appears to go through a difficult period with high fluctuations in income and vulnerability (again, the FCM can be used to time these fluctuations, but their presence and order is predicted). Ultimately, a comfort zone of high income is reached. Whether this is reasonable under the assumptions made is to be examined, and a matter of validation of the FCM. If necessary one can add or remove connections, or change connection weights or even alter their sign. The initial values of ordinary variables will not affect the equilibrium state of the system but can change its transient behavior. For example, one can start from a high income + low vulnerability situation (Figure 9).

0 5 10 15 20 25

-1

-0.5

0

0.5

1

hyperbolic tangent squashing = 2

Adaptation

Climate Change

Fish Stock Size

Fishing Effort

Fishing Income

Vulnerability

Figure 9. Scenario A with a high initial income and low vulnerability. In this scenario the system will even more quickly reach the equilibrium states, which are identical (under certain conditions it is possible to prove the mathematical uniqueness of the equilibrium, given the FCM weights and squashing function used). Nevertheless, the transient phase is of importance for understanding the system.

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Scenario B produces an entirely different behavior, because now we change an exogenous driving force (Figure 10).

high

low

normal

transient phase equilibrium phase

Figure 10. Scenario B – gradual decline in fishing effort, starting from a high fishing effort. The system behavior is highly volatile and periodic with increases in income, fish stock size and vulnerability followed by decreases, although there is a damping effect in the amplitudes of the fluctuations. Clearly, more scenarios need to be prepared, including initial states, to obtain a proper understanding of the response of the system to different driving conditions. These are just examples to demonstrate the methodology. The sensitivity of the system behavior for changes in it’s structure can be examined by removal of a connection between variables to see how the system responds, for example the negative impact of JellyFish on Fish Farming Production (Figure 11). The connection is not part of any feedback loop but still important for the behavior of the system as a comparison with Scenario A for the original FCM shows (Figure 12).

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Figure 11. Removal of a negative connection.

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Figure 12. Comparison of Scenario A with and without impact of Jellyfish of Fish Farming Productivity. The results at first is might appear counter-intuitive: removing the negative impact of Jellyfish leads to fluctuations in the income. But this is understandable, because the variable Fishing Income refers to catch fisheries, which competes with Fish Farming.

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6. Conclusions and Outlook For WP5 and the design of the DSS three important questions still need to be answered:

who will use the DSS and for which purpose?

what are the main problems to be addressed?

how are the social-economic aspects of these problems best integrated with the natural science knowledge?

At this stage (end April, 2015) the potential end users have not yet been identified, and neither have the problems to be addressed by the DSS. The first round of workshops, to be organized in April 2015, is expected to result in a clearer picture of the DSS functional requirements. The web-based survey which was organized with the mock-up did not produce satisfactory results for the DSS development, primarily due to a lack of response and more specific examples of the product to be developed. During the WOC Business Forum (September, 2014) it was decided to fine-tune the questions and distribute these among the marine and ocean business community (shipping, oil industry and fishing). The main observation during the conference was that marine spatial planning is the key issue. The question arose whether this can be addressed with the type of models and expertise available in the project team. There is general consensus on the sectors affected by climate change and anthropogenic changes to the efficiency of the biological pump and primary productivity. These are catch fisheries, the aquaculture sector and the tourism sector. Although the scientific complexity and uncertainty related to the biological pump mechanism are large, the impacts are qualitatively understood. This means that the lack of scientific information refers to the quantitative rather than the qualitative aspects of the models. This is fortunate, because it allows the DSS developers to set up the framework without awaiting the completion of the outcomes of the model and consilience work packages WP1, WP2 and WP4. A general framework, using a system dynamics model and database communication as a basis for model integration, was proposed during the bi-annual project meeting in Trondheim (Jan. 2015). The framework was considered satisfactory for the purpose in mind. One of the challenges remaining is how to integrate the social-economic aspects in the model and DSS. Fuzzy cognitive mapping and Bayesian Belief Networks are useful tools to analyze and model complex systems interactively with stakeholders (participatory modelling) and to integrate across multi- and trans-disciplinary areas. The FCM scenarios that were developed to demonstrate the potential of FCMs are useful but the examples need to be validated before use in the DSS. The second iteration round of workshops (2016) is expected to be of use in this respect. Once the FCMs have been tuned to the stakeholder needs and case studies, and validated, it is necessary to identify “coupling variables” for the integration with the (natural) science modules of the DSS (for example the fish stock).

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