Wildenberg – Presentation at the SSI, University of Maine | 1

Linking thoughts to flows Integrated modeling of Social Ecological SystemsMartin Wildenberg

Wildenberg – Presentation at the SSI, University of Maine | 2

Structure of this talk- Introduction:

- Modeling social-ecological systems (SES)

- Some examples of integrated SES models- Modeling in LTSER Platforms- Model of Kamorta Island, Nicobars- Fuzzy Cognitive Modeling- Sustainability Indicator: Integration of models

- Integrated Modeling- Partizipation- Process oriented modeling…- Outlook

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Social Ecological Systems

Complex

Adaptive

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We are part of them…and that's what makes them

especially messy

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Let´s zoom in…

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•Human behavior•Communication•Learning •Local knowledge

Important for formulation & application of SES - model

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Modeling in LTSER platforms

LTSER:Focus on coupled socio-ecological systemsLink biophysical processes to governance and communication

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SERD (Simulation of Ecological Compatibility of Regional Development)

Veronika Gaube, Heidi Adensam, Julia Lutz, Tina Kaiser, Andreas Richter, Helmut Haberl

• Analysis links between socio-economic

dynamics and substance, material and energy

flows

• Understand reasons and impacts of long-term

changes in economic and social dimension, land

use and ecosystems.

• Contribute to local sustainable development:

Involvement and support of the local

stakeholders.

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The Casestudy AreaLTSER Platform Eisenwurzen

•99 Municipalities•5.740 km²•304.762 Inhabitants•Forest Cover = 64%

Reichraming

Eisenwurzen

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ReichramingThe municipality:• 103km²• 80% Forest• 356 m – 2000m• National-park Kalkalpen• 1880 people • Decreasing Population

The farms:• Average size: 20ha• Family owned (extended

families)• Greenland / milk cows• Highly dependent on subsidies• Decreasing number of Farms

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Landscape I

Extensive land-use leads to:• Small structured landscapes• Extensive species rich meadows• Hedgerows• Forest on steep slopes

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Landscape II

Forest:• Long (historical) period of deforestation • After breakdown of industry

reforestation • Continuing today• Mostly intensive forest: species poor

spruce monoculture

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Land use 1900

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Land use 2001

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External conditionsSocio-economic / political

frameworkEnvironmental conditions

e.g. climate

Agent-based modulefarmstourismfirmsmunicipality

Integrated stock-flowmodule

Socio-economic stocks/flows(humans, livestock, artifacts)

Ecological stocks flows(forests, grasslands, cropland)

Land useLand cover

Model outputs

Socio-economicchange

Land-usechange

Changes in material/energy flows

Model concept

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Behavior of the Farm Agents

Farms aim to• maintain the cultivation• achieve at least a minimum of income• achieve at least a minimum of leisure

time

Farms percept framework conditions like• spatial characteristics of their land• subsidies• product prices • production costs

Possible reactions are• Ex- or intensification of production• organic production• increase/Decrease of farm size • change of type of production• increase/Decrease of non-agricultural labour time• increase/Decrease of external wage labour• abandonment of farm

Interaction • via land market

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Behavior of the Farm Agents

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Participatory process

• Questionnaire for farmers

• 6 in-depth interviews with farmers

• 6 expert interviews

• 3 stakeholder workshops

• 2 focus groups with women of the

municipality

• 3 working groups with farmers

User Interface

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Three Scenarios

TREND

GLOB

LOC

+ Base Run

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Trend Scenario

Land-use today

More farmsLess forestation (-15%)Households increase: 10%Less commuters

Highest GHG emissions & Nitrogen loss form farms

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GLOB Scenario

Land-use today

Less farms – extensive, high workloadsMore forestationHouseholds decreaseMore commuters +75%

Lowest GHG & Nitrogen emissions form farms

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LOC Scenario

Land-use today

Less farms – extensive, high workloadsless forestation (-15%)Households increaseLess commuters

Medium GHG & Nitrogen emissions form farms

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Another region, another topic, similar challenges:

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Waves of change

Modelling the Nicobar Islands in the Aftermath of the 2004 Sumatra Tsunami

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RECOVER(research on coping with vulnerability to environmental risk)Simron J. Singh, Willi Haas, Marina Fischer - Kowalski

Understand how natural, cultural and institutional features determine the resilience of local socio-ecological systems.

Explore the potential linking between functional tools and tools dealing with meaning, human preferences and choice.

Scientific support for the planning, implementation and evaluation of reconstruction and development options after the 2004 Tsunami that maintain the social and cultural integrity of the indigenous population and the balance of the natural environment.

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Modelling

TheoryDevelopment

Intervention

Workpackages of Recover

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2004 Tsunami

More then 4000 dead Almost all Houses destroyed & infrastructure damaged coconut plantations destroyed Reef & coast topology changed

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The Second Wave

Over 60 NGOs (in Port Blair) desperately wanting to spend millions of US$. Unrepresented inflow of money & commodities. The Nicobars - A badly hit region or a promising “development market”?

Some Side Effects of Aid:Change in Family structure, Social coherence, Conflicts over land, increasing social stratification, change in Consumption

and Production patterns…

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Level Focus Target group Tools

Island Ecological constraintsBiophysical constraintsMarket

Tribal CouncilAdministrationNGOs

Integrated Model

Village Cooperate societiesProduction (best mix)

SIF (NGO) Village councilCooperate societies

Planning Tool

Household What do people want?Cultural constraintsLifestyle changes Time use

Determines other levels

Participative

Data

Collection &

Participative observation

Integrating different levels in RECOVER

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Model Components

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Screenshot of the Model

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Results I

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Available, required and desired working times per adult. Required workingtime includes time for subsistence and market oriented activities.

Results II

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Lets get softer…

a tool for integrated modelling of SES

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Fuzzy Cognitive MappingHow does it work?

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Experience from FCM projects

Six case studies accross LTSER Plattforms

FCM as eductaional tool at the PIK Summerschool

Vulnerability of Indian cities towards extreme weather events (ongoing)

www.FCMappers.net

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Structured data collection

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Visual comparison of maps

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Social Learning & Education

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Vulnerability to climate change

Collective Fuzzy cognitive map of professionals interviewed in Delhi (Diana Reckien et al.)

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Simulation of future scenarios for the Eisenwurzen

FCM Simulation in a nutshell

ji

N

jij

kj

ki

ki wAAA

1

)()()1(

= value of concept Ci at iteration step k+1

= value of concept Ci at iteration step k

= value of concept Cj at iteration step k

= weight of the link between Ci and Cj

)1( kiA

)(kiA

)(kjA

jiw

X =

C1

Cn

Activation Vector

Interaction Matrix

C1

Cn

C1 Cn

Vector contains values representing the state of the concept

Matrix contains weights of relations between concepts

First iteration

X

C1

Cn

C1

Cn

C1 Cn

Vector contains values representing the state of the concept after one iteration

Seconde iteration

=

C1

Cn …

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Again the three scenarios

TREND

GLOB

LOC

+ Base Run

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Base Run

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Trend

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Glob

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Loc

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Comparison of results with ABM

High overlap in predicted trends for three scenarios

Predicted trend in Farmers income is seen in 2008 Statistical Data

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• Structured participative process

• Social learning

• Communication between participants

• Depict complex systems as perceived by different stakeholders

• Mapping of complex systems

• Analysis

• Comparing qualitative data

• Modeling & scenarios

What can Fuzzy Cognitive Mapping offer?

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Pros - Cons

• (- )No time, no quantitative results

• (+) more process oriented• (+) participative• (+) faster

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The Sustainability-Programm of GLOBAL 2000

Objectives:

Labeling of (conventional) products which are produced more sustainable then comparable products

Develop measurable indicators

Information for costumers

Information and recommendations for producer

Initiate progress towards sustainabilityLess resource useLess emissionsLess environmental impact

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Bilances (N,P,H)Pest management intensityEnergy intensityErosionSoil compaction

Water, abiotic, biotic, CO2 footprint, area footprint / kg product

Farm Distributor Retailer

Repro-model

from INL

Footprint calculation in DB

Calculating indicators: Integrating MIPS with REPRO

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Most important:Willing participants!

So, it is not just about collecting data and analysing it…

It is about a PROCESS…

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Discussion and involvment of stakeholders:

Learning from praxis

– What are the day to day problems faced by farmers?– What do the stakeholders perceive as sustainability problems?– What are workable solutions?

=> Work out guidelines to progress towards sustainable production

Ensure good data quality

– Make data collection as easy as possible! This reduces workload and avoids misunderstandings!

– Ask the stakeholders what data has to be estimated and what data they already have measure for

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First results for strawberries:

Freiland, At Folientunnel, Israel Folientunnel, Es Gewächshaus, At0

1000

2000

3000

4000

5000

6000

7000

8000

9000

0

10

20

30

40

50

60

70

80

90

100

Ressourcenverbrauch Erdbeeren

biotisch, g/ 1 kg Produktabiotisch, g/ 1 kg ProduktFläche, cm²/1 kg ProduktCO2e, g/ 1 kg ProduktWasser, l/ 1 kg Produkt

g/k

g P

r od

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cm²/

kg P

rod

ukt

li te

r / k

g P

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kg

Pro

du

ct

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Challenges: • Enable win-win situation• Programme must be applicable for the broad

market• Transparent and scientific approach• Workload for producers – remuneration?• Setting of benchmarks

Part III

Integrated modeling

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So, what is an integrated model?

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And how can integration be achievd?

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A integrated model integrates…

- two (or more) independent models - knowledge from different disciplines- people from different disciplines- scientists and “other” people- theory and praxis- different modelling methodes (ABM,

SD, GIS)- Different types of data (qualitative &

quantitative)

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Models can integrate:

• people

• topics

• knowledge

• technology

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Stakeholders involved in modeling process

Heuristic model, visualization of results

Agents, activitiesStocks-flowsLandscape

Etc.

System model(e.g. integrates agentswith stocks and

flows)

Formalization

Modeling

Scenarios

Results

Simulation

Interpretationof local knowledge

Conclusionsacceptance

Integrating people, topics and knowledge: constructing models in iterative loops

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Lessons learned:

• Important to involve actors in the modeling

process right from the beginning

• Acceptance of the model among the

stakeholders very high

• Models can structures the discussion process

during the whole process

• The model helps the stakeholders to understand

“their” system better

• The model development process is an important

focus of the research

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The modeling process

Always iterative

Integration: Develop methods that help

structure this process.

Use models as form of communication

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Conceptual Model

Interface

Formal Model

Building blocks for Integration

Build common understanding of usability

Similar functionalities & appearance

Common way of describing models (e.g. ODD)

Definition of interface to higher and lower scales

Modular design

Keep other models in mind

Evaluation of basic analytic tools

Typologies of SES

Development of consistent SES theory

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There are common challenges in the creation of useful SES models

To handle most of them some form of integration is required.

To integrate between models or modelers a common fundament is needed.

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Thank you!

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Decision finding process of each farm

Ecological dimension

Economic dimension

Social dimension

ODD

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