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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.
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Envisioning Future Landscape Trajectories Using Multiagent-based models to Simulate Dynamics of Landscape Change John BolteBiological & Ecological Engineering DepartmentOregon State University
Landscape Planning and Simulation Models How can we use simulation models in landscape planning to help people achieve desirable futures? 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 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
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 scenarios of trends and assumptions about future conditions, generally using one or more models of change, • Assist in incorporating stakeholder interactions to define goals, constraints, trajectories, drivers, outcomes • Allow visualization of the results in a variety of types and formats • 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 of autonomous, adaptive agents (actors) • 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 (e.g. succession) simultaneously modeled
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).
Complex Theory of Action Drivers Context = difficulty, time, expense Systems personality social cultural economic biophysical Actor values attitudes behavior beliefs plan action normsdesires goals intentions I information/matter/energy
Envision – Conceptual Structure Actors Decision-makers managing the landscape by selecting policies responsive to their objectives Landscape Production Models Generating Landscape Metrics Reflecting Ecosystem Service Productions LandscapeFeedbacks Landscape Spatial Container in which landscape changes, ES Metrics are depicted Multiagent Decision-making Select policies and generate land management decision affecting landscape pattern Scenario Definition LandscapeFeedbacks Policies Fundamental Descriptors of constraints and actions defining land use management decisionmaking Autonomous Change Processes Models of Non-anthropogenic Landscape Change
ENVISION – Triad of Relationships Goals Actors Policies Values Intentions • 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 in Evoland • 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: • Autonomous Processes: Represent processes causing landscape changes independent of human decision-making – e.g. climate change, vegetative succession, forest growth, fire, flooding, ??? • Evaluative Models – 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, risk, ???
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 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) Actors have values that influence their decision-making behaviors. These values reflect landscape productions
Actor Decision-making • 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 • 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)
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 Adapted from Benenson and Torrens (2004:156)
Inferring Values from Actions: Votes on 1998 Environmental Ballot Measures
Definition of value categories including descriptive terms and text examples.
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 ENVISION Autonomous Process Models Harvested Acreage Rural Residential Expansion 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 Puget Sound Application Data Sources Evaluative Models IDU’s – GSU/LULC/… Impervious Surfaces Policy Set(s) Water Quality/Loading (SPARROW) Agent Descriptors Nearshore Habitat (Controlling Factors Model) ENVISION Autonomous Process Models INVEST Tier 1 Carbon Rural/Urban Development Resource Lands Protection Expansion of Nearshore Modifications 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
