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Decision Support Systems: Science/Modeling Organizations to
Bridge the Science-Policy Gap
Denise Lach, Director
School of Public Policy
Wicked Problems
• Solution depends on how problem is framed
• Stakeholders have radically different world views for understanding the problem
• Problem constraints and resources needed change over time
• Problem is never solved definitively
Super Wicked Problems
• Time is running out
• No central authority
• Those seeking to solve the problem are also causing it
• Policies discount the future non-rationally
Complications: Uncertain Futures
Role of Science in Wicked Problems
Decision Stakes
System Uncertainties
High
HighLow
Decision Stakes
System Uncertainties
High
HighLow
Normal Science
Decision Stakes
System Uncertainties
High
HighLow
Normal Science
Professional Consultancy
Decision Stakes
System Uncertainties
High
HighLow
Normal Science
Professional Consultancy
Post-Normal Science
Post-Normal Science
• Facts are uncertain, values in dispute, stakes high, and decisions urgent
• Less than desired information available
• Not all factors are necessarily knowable
• Always faced with uncertainties
• Mistakes can be costly or lethal
Coping with Wicked Problems
• Authority
• Competition
• Collaboration
Can we substitute process for certainty in resolving wicked problems?
Post-normal Boundary Organizations for Integrating Science and Policy
Form a research
agenda around the needs of stakeholders
Assemble needed
expert ise to address key
questions
Design decision support tools to
translate the research
answers into practical
applications
Produce useable knowledge about climate impacts in the PNW
Some Recent PNW Study Areas
Skagit 2060
Kitsap Futures
TillamookCoastal Futures
Willamette Water 2100
Forest People Fire
Treasure Valley
Big Wood Basin
Envision – Conceptual Structure
Landscape Performance Models
Generating Landscape Metrics Reflecting
“Stuff People Care About”, e.g. Water
Scarcity, Habitat, Jobs
Multiagent
Decision
Models
Actors selecting
policies and
generate land
management
decision affecting
landscape pattern
Landscape
Feedbacks
Landscape
Temporal GIS
Landscape Process Models
Biophysical/Social/Economic Models (e.g.
Climate, Hydrology, Population Growth, Veg
Dynamics, Fire, …)
Visualizations
Stakeholder
Engagement and
Understanding
Dynamic Maps,
Charts, Flyovers/
Flythroughs…
Policies and
Scenarios
(From Stakeholder
Process)
Scenario PlanningProcess
Identify System, Develop
Initial Datasets
Develop System Models
Create Scenarios
Evaluate Scenarios
Develop Preferred Scenario
Implement Plan
Scientists Stakeholders
Endpoints as Starting Points for fModeling
Alternative Scenarios: Economic base,
management approach
Highly Managed / Agricultural
Economy
Highly Managed /
Tourism Economy
Less Managed / Agricultural
Economy
Less Managed /
Tourism Economy
Economic BaseAg Economy Tourism Economy
Man
age
me
nt
Less
Man
aged
Hig
hly
Man
aged
Big Wood Climate Model Selection
12 Alternative Scenarios: economic base, management approach, climate scenario
ENVISION Model Framework
Thinking About Complicated Information: What’s Important?
Types of Information from Model:High Elevation April 1 SWE
1980-2009 Interquartile Range
2 out of 3 modeled simulations indicate a consistent reduction in April 1 SWE.
Types of Information from Model: SWE
Types of Information: Frost Free Periods
Big Wood Data Atlas
Lessons Learned: Modeling Challenges
EmpiricalBasis
Levelof Detail
Mechanism (Processes)
It’s a Balancing Act!
ComputationData Availability
StakeholderRelevance
Uncertainty
Lessons Learned: Project Design
• Projects are both challenging and interesting
• Integration should come first, not last
• Systems approach essential – we need more systems thinkers
• Multidisciplinary approach is critical
• Place Matters – be clear about what is general and what is specific
Lessons Learned: Collaboration
• Team dynamics determines success or failure
• The “Culture of Science” can be a plus and a minus -+ Solid scientific footing to be useful, credible
– “Out of box” thinking critical – disciplinary boundaries can limit thinking
• Stakeholders are generally pretty interesting people who know a heck of a lot – engage the thought leaders early and often
• Make assumptions, choices transparent
• Address important issues/questions
• Create simple visuals
• Provide options for individual exploration
• Develop intuitive interface – stories?
• Provide meta data and data access
Lessons Learned: Communicating Usable Knowledge
Questions?
“Standard” Envision Plug-insPlug-in Function
Target models growth of a surface based on total and available capacities and existingdensities – very useful for population growth and spatial allocation models
Modeler a high-level, XML-based model specification and execution tool for relatively simple models
Spatial Allocator Allows definition of global allocations, constraints and preferences, useful for a broad variety of applications, eg. Fire spread, insect infestation, crop rotations, management choices
Sync a tool for synchronizing changes to related columns
Trigger a tool for triggering a set of outcomes when a specified field change – similar to Sync, but more flexible, slightly slower
Flow a hydrological modeling framework
SppHabMatrix A flexible Habitat Suitability modeling framework
Developer A tool for specifying urbanization dynamics, can be used in conjunction with Target for modeling population growth and develop processes
Envision “Adapter” Plug-ins
Plug-in Function
VDDT/ DynamicVeg
Dynamic vegetation models (state-transition) for running VDDT-based vegetation models
FlamMap Detailed Process-based fire model
MAPPS Global biogeography model
Geospatial Data Reader
Dynamic spatial data object for reading a variety geospatial formats e.g. NetCDF
MC2 Global biogeochemistry model
Century V5 Biogeochemistry model
ENV
ISIO
N
Biofuel Production
Carbon
Forest Products Extraction
Fire Risk (Habitat)
Habitat Suitability
Resource Lands Protection
Evaluative ModelsData Sources
Autonomous Process
Models
Parcels (IDU’s)
Population Growth andResidential Expansion
Policy Set(s)
Agent Descriptors
VDDT Vegetative Succession (Spatialized and Climatized)
Climate Change
Envision Central Oregon
FLAMMAP Fire SpreadFire Risk (Structures)
Social Networks
Landscape Amenities
Terrestrial Biodiversity
Integrated Decision Units (IDUs)A spatial geometry to model both human decisions and successional processes
Each IDU described in GIS by a set of attributes used to model
climate effects, succession, wildfire and decisions