Contrasting Citizen and Expert Approaches to creating alternative future scenarios

1. Contrasting Citizen and Expert Approaches to creating alternative future scenarios Dave Hulse Institute for a Sustainable Environment University of Oregon

10. Lessons Learned Two basic approaches to defining scenarios: 1) Citizen-driven Adv: greater stakeholder understanding, sense of ownership, highlights local knowledge, increased plausibility & likelihood of acceptance and use of ideas Disadv: expensive, time-consuming, few alternatives produced (typ. 3-10), scenarios don’t push the envelope 2) Expert-driven Adv: can quantify likelihood of alternatives due to large number of them, can characterize likelihood of a given outcome at a given location Disadv: unclear political plausibility

11. Willamette Alternative Futures Revisited: Assumptions • Patterns of natural resources and human systems emerge through the interplay of policy and pattern in coupled human/riverine systems as production (expressed in multiple forms) becomes scarce. • We hypothesize that as resources become scarce or impaired, a human/riverine system becomes more tightly coupled (connections become more important). • The system as a whole develops policy responses that feed back into emergent spatial and temporal patterns of both cultural and biophysical functions.

12. What if you want advantages of both citizen AND expert approaches? Agent-based modeling

13. Context of Biocomplexity and Agent-Based Modelingof Complex Systems

14. Agent-Based Modeling • Broad endeavor, relevant to many fields/disciplines based on modeling behavior (and resulting effects) of autonomous, adaptive agents • Our approach: spatially explicit, represents decisions of people / agents with authority over parcels of land • Agent decisions implemented through policies that guide & constrain potential actions • Autonomous processes (flooding, veg. succession) also modeled

15. Biocomplexity • Richness of living system’s capacity for adaptation & self-organization emerge from interplay of behavioral, biological and abiotic systems; operating in concert over space and time this interplay creates trajectories of landscape change • Our approach asserts scarcity is a major driver of change in complex systems • Landscape scarcity is the difference between the required or desired features of a landscape and the availability of those features. • Our approach asserts a landscape is vulnerable that exhibits scarcity of key resources • Landscape vulnerability is the degree to which a sector or region is unable to adapt to the scarcity of a valued landscape characteristic.

16. Evoland - A Biocomplexity Model Evoland (Evolving Landscapes) is a tool for conducting alternative futures analyses using: • A spatially explicit, GIS-based approach to landscape representation • Actor-based (multiagent-based) approach to human decisionmaking that explicitly represents real-world decision-makers with attributes and behaviors within the model • Actor decisions are guided by “policies” that define, constrain potential behaviors • Autonomous landscape process models produce non-human induced (natural) landscape change

17. Collaborators • John Bolte, Department of Bioengineering, Oregon State University • Court Smith, Department of Anthropology, OSU • Stan Gregory, Department of Fish and Wildlife, OSU • Michael Guzy, Department of Bioengineering, OSU • And a host of others…

18. Evoland – General Structure Policies: Fundamental Descriptors of constraints and actions defining land use management decisionmaking Policy Metaprocess: Manages existing policies, generation of new policies Landscape: Spatial Container in which land use changes are depicted Landscape Evaluators: Generate landscape metrics reflecting scarcity Exogenous Drives: External “program” defining key assumptions Autonomous Change Processes: Models of nonhuman change Actors: Decisionmakers making landscape change by selecting policies responsive to their objectives Cultural Metaprocess: Manages the behavior of actors

19. Policies in Evoland • 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]

20. Willamette Evoland Study Areas

21. Actor Value Mapping Ecosystem Health Economics

22. Evoland Agent Properties Adapted from Benenson and Torrens (2004:156)

23. Evoland Framework for WRB Evaluative Models Data Sources Fish Abundance/Distributions IDU Coverage Floodplain Habitat Policy Set(s) Small-Stream Macroinvertebrates Actor Descriptors Upslope Wildlife Habitat Evoland Parcel Market Values Autonomous Process Models Agricultural Land Supply Forest Land Supply Vegetative Succession Residential Land Supply Flood Event Conservation Set-Asides

24. Analysis • Resilience – determined by generating a large number of runs (Monte Carlo) and identifying characteristics of attractor basins in state space • Vulnerability – identify those portions of landscape likely to experience reversible, irreversible change of ecological function through frequency analysis • Connectedness – experiment with turning on/off feedback loops associated with: • Policy Generation • Actor Association Building • Time Lags in evaluative model feedback • Adaptive Capacity – Enable/Disable/Throttle policy evolution