social experiments on human interactions with ecosystems: agents, values, and policies in the...
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Social Experiments on Human Interactions with Ecosystems:
Agents, Values, and Policies in the Willamette Valley, Oregon
Biocomplexity in the Environment (NSF)
Stanley V. Gregory, David W. Hulse, John P. Bolte,
Courtland L. Smith, Michael Guzy, Chris Enright, Allan Branscomb,
Linda Ashkenas, Randy Wildman
Washington State University, Vancouver: April 11, 2006
Alternative Futuring Problem
• Imagine yourself as a property owner, what would you do to restore salmon runs?
• Situate yourself in the Puget-Willamette Lowland.
• What is the role of policy (institutional structure) and values when choosing restoration actions?
Theory
• Ecosystems – spatially-explicit, agent-based, multi-objective, scarcity-oriented, landscape futuring model
• Evolutionary Ecology – landscape “evolution” (change) to reflect human values, evaluating alternative futures
• Ethnoscientific/ideological/historical ecology – role of values in decision-making
Gallery Forests and Oak Savanna
Willamette River Basin Planning Atlas, p. 82
McKenzie JCT
Willamette River Basin Planning Atlas, p. 82
PNW-ERC change from 1990 initial conditions
-25 -20 -15 -10 -5 0 5 10 15 20
Cons
Plan
Dev
Per
cen
t
Conifer Riparian
Willamette River Basin Planning Atlas, p. 128
Springfield
Eugene
McKenzie-WillametteJunction
Spatially-explicit
Willamette Alternatives II –
Study Areas
Urban growthboundary in the vicinity of Eugene,
Oregon
Landscape
Landscape
Evaluative ModelsAgents
Policies Autonomous Processes
Choose
Inform
Outcomes
Change
ChangeOut
com
es
Evoland Framework
Evo
lan
d
Fish Abundance/Distributions
Floodplain Habitat
Small-Stream Macroinvertabrates
Upslope Wildlife Habitat
Parcel Market Values
Agricultural Land Supply
Forest Land Supply
Residential Land Supply
Conservation Set-Asides
Policy Set(s)
Agent Descriptors
Vegetative Succession
Population Growth
Parcel Coverage
Evaluative ModelsData Sources
Autonomous ProcessModels
EvoLand Agent Properties
• Each agent makes decisions for an IDU (homogeneous tax lot and vegetation type) averaging 5000 m2.
• Agents select policies that fit their values in adapting to scarcity.
• Policies result in changes on the landscape to reduce scarcity regarding economic conditions and ecosystem health.
• Scarcity metrics are updated with each iteration and agents make new decisions based on current scarcities and their values.
Evoland Agent Properties Property Meaning EvolandReactive Responds to environment Yes
Autonomous Controls own actions Yes
Social Interact with other actors No
Goal-oriented More than responsive to environment Yes
Temporally continuous Agent behavior continuous Once/step
Communicative Communicates with other agents No
Mobile Can transport self to other locations No
Flexible Actions not scripted Yes
Learning Changes based on experience No
Character Believable personality or emotions No
Adapted from Benenson and Torrens (2004:156)
Values Theory Integration
• Theory of mind – developmental psychology, philosophy, sociology, linguistics (Malle et al. 2001; Conte and Castelfranchi 1995; Bratman 1987)
• A general theory of action – sociology, developmental psychology, anthropology (Smelser 2001; Vaske et al. 2001; Rokeach 1973; Parsons and Shils 1951)
Theory of Mind
values attitudes action
A general theory of action
“… values are abstract concepts, but not so abstract that they cannot motivate behavior. Hence, an important theme of values research has been to assess how well one can predict specific behavior knowing something about a person’s values” (Karp 2001:3213).
values attitudes action
A general theory of action(Parsons and Shils 1951)
Systems
PersonalitySocialCultural
Actor
values attitudes behaviorbeliefs plan actionnorms desiresgoals intentions
A synthetic theory of action
Systems
personalitysocialculturaleconomicbiophysical
I
Actor
values attitudes behaviorbeliefs plan actionnorms desiresgoals intentions
Complex theory of action
Systems
personalitysocialculturaleconomicbiophysical
I
information/matter/energy
Context = difficulty, time, expense
Drivers
Inferring Values from Actions: Votes on 1998 Environmental Ballot Measures
Ballot Measure
StatewideStatewide Percent
Yes
Lane County Percent
YesYes Votes
No Votes
56 (notification) 874547 212737 80 73
64 (timber) 215491 897535 19 21
66 (parks & salmon ) 742038 362247 67 70
Definition of value categories including descriptive terms and text examples.
Value Category Descriptive Terms
Economic reflecting economic production of the landscape, job activity, productivity, opportunities for capital production and revenue generation
Property Rights
concern is with the freedom to own and use private property as a landowner desires
Ecosystem Health
ecological health, diversity of the landscape, environmental protection and restoration
Nonmarket reflecting aesthetics, scenic integrity, beauty, spiritual, future generations, “right thing to do,” undiscovered utility, learning about and gaining connection with the environment
Fairness refers to actor perceptions about economic justice, winners and losers, fears about litigation and its costs; unfair policies force an actor to do something she does not want to do
Credibility refers to policies are justified by scientific or other expertise, or to policies that lack scientific or support by other expertise
Safety concerned with human safety in jobs and activites, from chemicals, from natural hazards
Recreation emphasis on any type of recreational activity that could be helped or hurt by passage of the ballot measure.
MEASURE No Eco-nomic
%
Pri-vate prop-ertyrights
Eco-system health
%
Non-mar-ket
%
Fair-ness
%
Credi-bility
%
Safe-ty
%
Recre-ation
%
Notification 9 67 63 0 15 100 15 0 0
Timber - Pro
20 73 0 93 55 28 30 77 35
Timber - Con
29 74 35 52 13 38 51 15 3
Salmon & Parks
21 84 3 84 52 16 6 3 71
Value Frequencies in Ballot Measures
Economics Values
cell
ACTORWT_0
-2.00 - -1.76
-1.75 - -1.58
-1.57 - -1.22
-1.21 - -0.82
-0.81 - -0.61
-0.60 - -0.38
-0.37 - -0.11
-0.10 - 0.18
0.19 - 0.45
0.46 - 0.73
0.74 - 0.94
0.95 - 1.13
1.14 - 1.31
1.32 - 1.51
1.52 - 1.77
1.78 - 2.11
2.12 - 2.41
2.42 - 2.66
2.67 - 2.89
2.90 - 3.00
Scale of
River McMansion ?
“Natural” River ?
EvoLand Policies
Randy Wildman photo
Court Smith photo
Policy Name Persis-tance (yrs)
Manda-tory?
Site Attributes Outcomes
Tax credits for fish
10 -10 no In_Flood = 1 and In_UGB = 0 and (Lulc_C = 67 or Lulc_C = 71 or Lulc_C = 79 or Lulc_C = 83 or Lulc_C = 85 or Lulc_C = 86 or Lulc_C = 88 or Lulc_C = 90)
Lulc_C = 87 {shrubland}
Riparian Conservation Easement on Rural Lands
15 - 100 no Dist_Str < 100 and In_UGB = 0 {Outside Urban Growth Boundary} and (Lulc_C = 24 {Rural non-vegetated} or Lulc_A = 3 {Agriculture})
Lulc_C = 87 {shrubland}
River McMansions
40 - 100 no Dist_Str < 100 Lulc_C = 1 {Residential 0-4 DU/ac}
EvoLand Urban Growth Problem
• Initial Conditions• Conservation Scenario with UGBs, 50 years
– conservation policies, pop growth within UGB• Development Scenario without UGBs, 50 years
– development policies, expansion anywhere• Conservation and Development, 50 years
– which has the most impact
LU LC_AR oads (8)Water (7)Other Veg etation (6)Wetlands (5)F orest (4)Ag r iculture (3)R ural (2)U rban (1)N o D ata
R ang e: 1-8
Initial Conditions
McKenzie Study Area
7,091 hectares
36% Urban 9% Rural18% Agriculture 5% Other Vegetation13% Forest19% Roads & Water
40,000 people
Evaluative Models scaled -3 to +3
Economic = -1.8 Ecosystem Health = -1.5
LU LC_AR oads (8)Water (7)Other Veg etation (6)Wetlands (5)F orest (4)Ag r iculture (3)R ural (2)U rban (1)N o D ata
R ang e: 1-8
LU LC_AR oads (8)Water (7)Other Veg etation (6)Wetlands (5)F orest (4)Ag r iculture (3)R ural (2)U rban (1)N o D ata
R ang e: 1-8
Initial Conditions 50-Yr Conservation Run
Conservation Scenario Ecosystem Health Measures
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
0 4 8 12 16 20 24 28 32 36 40 44 48
Time
Sco
re
_Ecosystem_Health
_HabScore
_Small_Streams
_Willamette_Fish
Initial Conditions vs Conservation
• 36% Urban• 9% Rural• 18% Agriculture• 5% Other Vegetation• 13% Forest• 19% Roads & Water
• 40,000 people
• Economic = -1.8• EcoHealth = -1.5
• 37% Urban• 3% Rural• 7% Agriculture• 1% Other Vegetation• 32% Forest• 19% Roads & Water
• 82,300 (1.5%/yr inc)
• Economic = 0.3• EcoHealth = 2.1
LU LC _AR oads (8)
Water (7)Other Veg etation (6)
Wetlands (5)F orest (4)
Ag r iculture (3)R ural (2)
U rban (1)N o D ata
R ang e: 1-8
LU LC_AR oads (8)Water (7)Other Veg etation (6)Wetlands (5)F orest (4)Ag r iculture (3)R ural (2)U rban (1)N o D ata
R ang e: 1-8
Initial Conditions 50-Yr Development Run
Development Scenario EcoHealth Measures
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
0 4 8 12 16 20 24 28 32 36 40 44 48
Time
Sco
re
_Ecosystem_Health
_HabScore
_Small_Streams
_Willamette_Fish
Development
• 60% Urban
• 9% Rural
• 4% Agriculture
• <1% Other Veg
• 8% Forest
• 19% Roads/Water
• 82,500 people
• Economic = 2.5
• EcoHealth = -2.0
Conservation
• 37% Urban
• 3% Rural
• 7% Agriculture
• 1% Other Veg
• 32% Forest
• 19% Roads/Water
• 82,500 people
• Economic = 0.3
• EcoHealth = 2.1
Initial Conditions
• 36% Urban
• 9% Rural
• 18% Agriculture
• 5% Other Veg
• 13% Forest
• 19% Roads/Water
• 82,500 people
• Economic = -1.8
• EcoHealth = -1.5
0 5 10 15 20 25 30 35 40
urban
rural
forest
Lan
d U
se
Percent
Initial
Conserve
Develop
0 10 20 30 40 50 60
urban
rural
forest
Lan
d U
se
Percent
Initial
Conserve
Develop
PNW-ERC
EvoLand
UGB Futuring Conclusions
• UGBs cannot protect both farms and fish• Without UGBs development eliminates farms
and fish• Ecological change is slow relative to economic
change• Substantial conversion to forest required to
achieve benefits for fish• Will forestry may produce more income and fish
protection than agriculture?• What incentives will product more forests?
Modeling Conclusions
• EvoLand provides a generalized modeling structure• Agent-based modeling allows for investigation of a
broader set of future alternatives• Can we assume economic and ecological scarcity are
major driving forces for future policy selection?• Are values important? What mechanisms change
agents’ values?• Does institutional structure (policies) play a larger role
than values? Institutional structure comes from values?• How should the results of alternative futuring be
validated?
EvoLandA modeling framework for the analysis of
complex, coupled ecological/human systems
http://evoland.bioe.orst.edu/
http://oregonstate.edu/instruct/anth/smith/
Support from the National Science Foundation, Biocomplexity in the Environment