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