Linking thoughts to flows Integrated modeling of Social Ecological Systems
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Transcript of Linking thoughts to flows Integrated modeling of Social Ecological Systems
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Wildenberg – Presentation at the SSI, University of Maine | 1
Linking thoughts to flows Integrated modeling of Social Ecological SystemsMartin Wildenberg
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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
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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
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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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Wildenberg – Presentation at the SSI, University of Maine | 38Kamorta land-cover and land-use – 2005, 2015, 2025 and 2035.
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
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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
Po s it iv e
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Trend
Po s it iv e
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Glob
Po s it iv e
Ne g a t iv e
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Loc
Po s it iv e
Ne g a t iv e
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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
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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
ukt;
cm²/
kg P
rod
ukt
li te
r / k
g P
r od
ukt
L /
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
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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
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ODD
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