Transcript Modeling Biocomplexity - Actors, Landscapes and
Envisioning Future Landscape Trajectories
Using Multiagent-based models to Simulate Dynamics of Landscape Change John Bolte Biological & Ecological Engineering Department Oregon State University
Landscape Planning and Simulation Models
How can we use simulation models in landscape planning to help people achieve desirable futures?
The future is uncertain The future is certain to come We have little control over much of what will happen We have substantial control over some aspects of what will happen The choices we make today will affect the choices we have tomorrow
Simulation models that project the future outcomes of different possible actions are particularly useful when the system is complex, relationships are poorly understood, or uncertainties are high
Why models? Types and uses
In the most general sense, a model is anything used in any way to represent anything else
• Physical model – a literal representation of something, generally in miniature, to show its construction or appearance.
Conceptual model – a simplified, abstract representation of a system or phenomena, typically of its components and the relationships among them. Commonly used to illustrate, explore or explain current understanding and gaps in understanding of a system and/or to generate hypotheses about how the system works.
Quantitative model – a model that uses numeric representations of system components and interactions among them to produce quantitative outcomes from quantitative inputs .
Quantitative Models
• Empirical model – a quantitative model based on empirical observations rather than on mathematically describable relationships of the system modeled.
Statistical model – a quantitative formalization of relationships between variables in the form of mathematical equations.
Mechanistic model – a model that uses cause and effect logic to describe the behavior of a system.
Simulation model – a computer program which attempts to simulate the behavior of a particular system. May be used to project the outcomes of specified interactions and to test the consequences of different actions or scenarios Spatially explicit model – a model that represents the behavior of a physical system, including the spatial relationships among its components. Multiagent-based model – a model that represents the behaviors of one or more “actors” in a system.
Building Models
“All models are wrong, but some are useful.” Box, G.E.P. 1976. Science and Statistics. Journal of the American Statistical Association 71: 791-799.
The essence of simulation models is to incorporate critical/dominant system features so that projections of the consequences of different scenarios can be made with some desired level of accuracy in representing likely real-world outcomes.
Alternative Futures Modeling
Examine multiple about future conditions, generally using one or more models of change, scenarios of trends and assumptions Assist in incorporating stakeholder interactions to define goals, constraints, trajectories, drivers, outcomes Allow visualization and formats of the results in a variety of types Ultimately are intended to assist in improving land management decision-making
Trajectories of Change and Alternative Futures
Source: Hulse et al. (2008), modified from Shearer (2005)
Approach: Multi-agent Modeling
Based on modeling behavior and actions autonomous, adaptive agents (actors) of Our approach: spatially explicit, represents land management decisions of entities ( actors ) with authority over parcels of land Actor decisions implemented through policies that guide & constrain potential actions Autonomous processes simultaneously modeled (e.g. succession)
A General Theory of Action
(Parsons and Shils 1951)
Systems Personality Social Cultural values attitudes 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).
Drivers Complex Theory of Action Context = difficulty, time, expense
Systems personality social cultural economic biophysical values beliefs norms goals Actor attitudes behavior plan action desires intentions I
information/matter/energy
Envision – Conceptual Structure
Actors
Decision-makers managing the landscape by selecting policies responsive to their objectives Landscape Feedbacks Scenario Definition
Multiagent Decision-making
Select policies and generate land management decision affecting landscape pattern
Policies
Fundamental Descriptors of constraints and actions defining land use management decisionmaking
Landscape Production Models
Generating Landscape Metrics Reflecting Ecosystem Service Productions
Landscape Spatial Container in which landscape changes, ES Metrics are
Feedbacks
Autonomous Change Processes
Models of Non-anthropogenic Landscape Change
ENVISION – Triad of Relationships Goals
•Economic Services •Ecosystem Services •Socio-cultural Services Provide a common frame of reference for actors, policies and landscape productions
Landscapes
Metrics of Production
Policy Definition
Landscape policies are decisions or plans of action for accomplishing desired outcomes.
from: Lackey, R.T. 2006. Axioms of ecological policy. Fisheries. 31(6): 286-290.
Policies in ENVISION
Policies are a decision or plan of action for accomplishing a desired outcome; they are a fundamental unit of computation Evoland in Describe actions available to actors Primary Characteristics : Applicable Site Attributes (Spatial Query) Effectiveness of the Policy (determined by evaluative models) Outcomes (possible multiple) associated with the selection and application of the Policy Example : [ Purchase conservations easement to allow revegetation of degraded riparian areas ] in [ areas with no built structures and high channel migration capacity ] when [ native fish habitat becomes scarce ]
Models in ENVISION
Models are “plug-ins” of two types: 1) 2) Autonomous Processes : Represent processes causing landscape changes independent of human decision-making – e.g. climate change, vegetative succession, forest growth, fire, flooding, ??? Evaluative Models risk, ???
– Generate production statistics and report back how well the landscape is doing a producing metrics of interest – e.g. carbon sequestration, habitat production, land availability,
Actors in Envision
Actors are entities that make decisions about landscape change Any number of actors can be defined ( 0-???) Actors can be defined in terms of A set of IDU attributes Prescribed areas on the landscape Randomly Each IDU is controlled by at most one Actor
Actors in Envision
(continued)
Actors have values that influence their decision-making behaviors. These values reflect landscape productions Actors make choices about landscape management by selecting policies based on some combination of: Internal Values relative to Policy Intentions Landscape Feedbacks/Emerging Scarcities (dynamically generated during a run) Global Policy Preferences (defined by scenario)
Actor Decision-making
Step 1: For each location and each time step, collect all relevant policies based on site attributes Step 2: Score the policies with respect to: 1) How well the policy intentions “align” with the actors on values (Self-interest) 2) How well the policies align with emerging landscape scarcities (Altruistic) 3) a “global preference” for the policy that can be defined conditionally 4) a “scenario-specific preference” for the policy 5) where an “lives” on a Self-Interest/Altruism scale Step 3: Stochastically select a policy based on a multicriteria score reflecting the above factors
Actor Associations in Envision
Actor associations are “collections” of actors, defined in one of three ways, based on: a common landscape attribute or set of attributes common values Spatial proximity Associations influence an actors decision-making process by modifying the actors values In theory, Envisions’s actor decision-making can be influenced by their group affiliations, but in fact we’ve never done anything with this.
ENVISION Actor Properties
Property Reactive Meaning Responds to environment Envision Yes Autonomous Social Controls own actions Interact with other actors Yes Sort of Goal-oriented Temporally continuous More than responsive to environment Agent behavior continuous Yes Once/step Communicative Mobile Flexible Learning Communicates with other agents Can transport self to other locations Actions not scripted Changes based on experience Sort Of No Yes No Character Believable personality or emotions No Adapted from Benenson and Torrens (2004:156)
Inferring Values from Actions: Votes on 1998 Environmental Ballot Measures Ballot Measure Statewide Yes Votes No Votes Statewide Percent Yes Lane County Percent Yes 56 (notification) 874547 212737 80 73 64 (timber) 66 (parks & salmon ) 215491 897535 742038 362247 19 67 21 70
Definition of value categories including descriptive terms and text examples.
Value Category Descriptive Terms Economic Property Rights reflecting economic production of the landscape, job activity, productivity, opportunities for capital production and revenue generation concern is with the freedom to own and use private property as a landowner desires Ecosystem Health Nonmarket Fairness Credibility Safety Recreation ecological health, diversity of the landscape, environmental protection and restoration reflecting aesthetics, scenic integrity, beauty, spiritual, future generations, “right thing to do,” undiscovered utility, learning about and gaining connection with the environment 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 refers to policies are justified by scientific or other expertise, or to policies that lack scientific or support by other expertise concerned with human safety in jobs and activites, from chemicals, from natural hazards emphasis on any type of recreational activity that could be helped or hurt by passage of the ballot measure.
Value Frequencies in Ballot Measures
MEASURE Notification No 9 Eco nomic
%
67 Pri vate prop erty rights 63 Eco system health
%
0 Non mar ket
%
15 Fair ness
%
100 Credi bility
%
15 Safe ty
%
0 Recre ation
%
0 Timber Pro Timber Con Salmon & Parks 20 29 21 73 74 84 0 35 3 93 52 84 55 13 52 28 38 16 30 51 6 77 15 3 35 3 71
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
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
Envision Andrews Application
Data Sources Evaluative Models
Parcels (IDU’s) Mean Age at Harvest Policy Set(s) Carbon Sequestration Agent Descriptors Forest Products Extraction
Autonomous Process Models
Rural Residential Expansion Harvested Acreage Fish Habitat (IBI) Vegetative Succession Resource Lands Protection Climate Change
Envision Andrews - Scenarios
Conservation - no Climate Change Development - no Climate Change Conservation - with Climate Change Development - with Climate Change
Envision Andrews Study Area
Scenario Results – Forest Carbon
Scenario Results – Forest Product Extraction
Scenario Results – Fish IBI
Envision Puget Sound Application
Data Sources Evaluative Models
IDU’s – GSU/LULC/… Impervious Surfaces Policy Set(s) Water Quality/Loading (SPARROW) Agent Descriptors
Autonomous Process Models
Nearshore Habitat (Controlling Factors Model) INVEST Tier 1 Carbon Rural/Urban Development Expansion of Nearshore Modifications Resource Lands Protection Residential Land Supply Population Growth
Envision Puget Sound- Scenarios
Status Quo – continue current trends Managed Growth – adopt a suite of additional policies aimed at conserving/restoring habitats, protecting resource lands, emphasizing denser development pattern near urban areas Unconstrained Growth – allow lower density patterns, less habitat protection, less resource land protection
Puget Sound
South Sound
Bainbridge Island
Ferry Terminal Area