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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