Intro: EVP631/GEOG 631/CSS645, “Spatial Agent-based Models of Human-environment Interactions”

1. Intro: EVP631/GEOG 631/CSS645, “Spatial Agent-based Models of Human-environment Interactions” Jan. 21, 2009 Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

2. Plan for today • Introduction – myself • Course logistics (syllabus) • Introductions – yourselves • Questions • Brief introduction/review of spatial model of human-environment interactions: what are they, and why use them? • Some examples from my ongoing work • Spatial, temporal, and behavioral complexity Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

3. My background • Graduate training in environmental and resource economics • Post-doc at CIPEC, interdisciplinary • Major research areas: land-use modeling, human/environment interactions, complexity theory • Current major research activity: agent-based models of land use • What to call me • Other important info Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

4. Course logistics (see syllabus also) • Description: Survey of state of the art related to design, implementation, interpretation, and application of agent-based models as applied to the study of human-environment interactions • Format: lecture, student-led discussions of related readings, and in-class hands-on demos of software models when possible. • Guest presenters will be brought in whenever the opportunity arises. • Office hours: Wed. 4:30-5:30 and by apt. Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

5. Course Themes • Model ontologies and model communication • Alternative theoretical models of decision making • Empirical methods for building agent decision models • Modeling market interactions • Modeling institutions • Modeling cross-scale feedbacks and interactions • Integration of agent-based modeling and GIS • Understanding the behavior of complex models • Model verification and validation (esp. pattern-oriented validation) Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

6. Desired prerequisites • Familiarity with spatial structures and concepts (some GIS helpful) • Some background in social science • A high level of computing competence (previous formal programming experience helpful but not required) • Ideally EVPP/GEOG 531 or CSS 600 • You are all likely to have a complementary set of strengths and weaknesses; we will build on this and learn from each other. (This makes thing fun and never dull.) Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

7. Course Requirements and Grading • Article presentation (likely 1) (25%). Please choose 4 articles to present from the syllabus. • Short writing assignments, lab write-ups, and class participation. (25%) • Student term project (25%) • Take-home final exam (25%) (Policy details on syllabus--stop now and go over) Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

8. Structure of the class • Introduction and general conceptual overview: Model definitions, modeling issues, and ABM-GIS integration (weeks 1-4). Content will be covered as we get to it to leave time for discussion. • Goal of introduction--understand the context and significance of course themes. • Applications (weeks 5-13 & 15) • Project presentations and discussion (weeks 14 and 16) Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

9. Readings • All readings on the syllabus which are available electronically I will bring on a mini-drive or post next week. (Have intro reading this week). • Readings still in development--need your votes on water resources management vs. IBM. Hybrid weeks? Forest resources? Climate change? Offer suggestions. • There may be articles in a given week for which there is no formal presentation. • Course bibliography contains many, many more readings (useful for initial term project research) • I may have other readings on the bibliography if you need copies Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

10. Break for questions, introductions Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

11. What are ABM models? • ABM or MAS models are simulation models • Generally implemented as computer code • ABM’s do not have a set of equilibrium conditions imposed on the model; generally, you do not “solve” or “estimate” the model • ABMs can both complement and substitute for other modeling techniques Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

12. Spatial agent-based models of H-E consist of: An electronic representation of a landscape An “agent-based” simulation of decision-makers whose choices alter the landscape (usually a computer program) (Parker et. al 2003; Parker, Berger, Manson 2002) H/E Interactions ABM/LUCC Spatial Modeling Complexity Theory Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

13. Cellular/Spatial Landscape Model • May or may not be based on real-world maps via geographic information systems layers • May contain a variety of geographic and socioeconomic features such as: • Slope, elevation, vegetative cover, soil types, zoning restrictions • Road and rail networks, information on social networks (who knows who) • Models of “spatial diffusion,” such as how air pollution spreads and disperses across a region Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

14. Figure 7, Forthcoming LUCC Report #6 Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

15. Agent-Based Model • Autonomous decision-making agents • Interaction environment • Interdependencies among agents, their environment, or both • Rules governing sequencing of actions and information flows Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

16. What is an “agent”? • Goal-oriented entity • Model of cognition that links goals and behavior: • Capable of autonomous action • Capable of responding to changes in its environment Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

17. Agent-Based Model of Decision Making • Each individual decision maker is represented through a set of rules that link information about his/her environment to a decision • Decisions often depend on the agent’s physical environment (the landscape) • Decisions may also depend on what other agents do as well -- can lead to “path dependence” Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

18. Potential Decision Models • Simple heuristics • Classifier/rule based (LUCITA) • Imitative behavior (FEARLUS) • Boundedly rational profit or utility maximization or risk minimization • Analytical implementations for theoretical models (SLUDGE, SOME) • Genetic algorithms (SYPRIA) • Mathematical programming (Berger, Balmann) Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

19. H/E Interactions ABM/LUCC Spatial Modeling Complexity Theory • Advantages of ABM/LUCC for spatial modeling: • Your model can have a realistic (and appropriate) geographic representation • Potential links with geographic information systems for data input and output visualization • Modeling of structures that are nested in time and space (cross-scale) Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

20. H/E Interactions ABM/LUCC Land-Use Modeling Complexity Theory • Modeling human/environment interactions: • Socioeconomic and biophysical models can be linked spatially • Simulation approach allows for feedbacks between dynamic social and environmental processes • Applications include crop yields, hydrology, forest growth, pest species modeling, endangered species populations Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

21. H/E Interactions ABM/LUCC Land-Use Modeling Complexity Theory • Human/environmental landscapes are complex: • Characterized by: • Interdependencies (one agent’s action depend on what another has done previously) • Heterogeneity (diverse variation in the same type of object: object-oriented programmers think “subclass”) • nested hierarchies (overlapping structures in time and space) Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

22. Soccer example: Nested hierarchies goalkeeper defense midfield offense Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

23. Complex properties of soccer … • Local actions of players lead to reoccurring, recognizable patterns that are semi-stable • A small change in strategic play can lead to large changes in the state of the system • In general, successful strategic play is general the result of interdependent team effort (“whole greater than the sum of its part”). In short, the relationship between players is of key importance. Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

24. Properties of complex systems: • Analytical intractability • Path dependency • Linkages across hierarchies • Emergence: “Organization into recognizable macroscopic patterns” (Epstein and Axtell, 1999) Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

25. Quilting examples of emergence Start with two very simple objects that are heterogeneous with respect to shape, size, and color … Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

26. Quilts, scaling up … By defining the relationship between other similar objects at a very local level, we obtain some structure and pattern … Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

27. Quilts, scaling up again … And by combining these elements, we create structure at a coarser spatial scale … Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

28. One emergent form … Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

29. And another … Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

30. Key sources of agent heterogeneity: • Pecuniary and non-pecuniary motivations: profits, preservation of family farm, environmental ethic • Experience and knowledge • Financial, physical, and human capital • Access to credit • Expectation formation mechanisms • Decision strategies Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

31. Types of Interactions Information transfer Technology diffusion Land markets Local labor exchange Community-based resource management Hydrology (ground and surface) Erosion Deforestation Transport of pollutants Species migration Soil fertility Agent-Agent Agent-Environment Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

32. Sequencing and information transfer • Pre-determined: • Synchronous • Asynchronous • Event-driven Note: Any event sequencing mechanism might introduce path-dependency into model outcomes Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

33. What can spatial ABMs do? • Produce fine-scale spatial outcomes that can be linked to socioeconomic and biophysical processes • Model complex dynamic linkages between human decisions and environmental processes • Incorporate spatial, biophysical, and agent heterogeneity • Incorporate interdependencies such as social networks Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

34. What are important limitations of MAS models? • Lack of analytical solutions may mean that model properties are not completely understood • Simulation approach cannot be classified as formally inductive • Techniques for model calibration, verification, and validation not yet well developed • Communication of model mechanisms and results is challenging Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

35. How might ABM be used to study H-E interactions? • To link socioeconomic drivers of resource use to their biophysical impacts • To explore the effects of feedbacks between humans and their environment • To examine whether current systems of resource use are sustainable • To design policies to encourage more sustainable resource use See “uses of models” LUMTA intro, for more for those new to modeling. Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

36. Identified Challenges (Introductory readings, Framing LUCC meetings) • Understanding complex models • Modeling cross-scale feedbacks and dynamics, land markets, and institutions • Modeling and verifying agent decision-making • Verification and validation of landscape outcomes (if possible) • Communication of model mechanisms and results (esp. to policy makers) Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

37. More challenges and areas of progress • Using survey data to develop/parameterize agent models • Identifying complementarities between statistical and ABM models • Combining ABM and lab experiments to understand agent behavior • Integrating ABM and GIS • Building a community modeling platform • Participatory modeling Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

38. Can Agent-Based Models of Land Use Bridge the Gap between Process and Pattern Based Models? Dawn Cassandra Parker Assistant Professor Dept. of Computational Social Science Center for Social ComplexityGeorge Mason University With contributions also from many others! Adapted from GLP workshop, “The design of integrative models of natural and social systems in land-use change, Macaulay Institute 1 March 2008 Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

39. Outline: • Identified advantages of and challenges for ABM/LUCC (2008) • As illustrated through some ongoing work: • Modeling joint effects of LUCC and land manager behavior (Residential development in the deciduous US) • Parameterizing models using qualitative and quantitative data (Smallholder agriculture in Uganda) Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

40. Why the interest in ABM? • “Change” of modelling approach? • ABM seen as a tool for building: • finer-scale process-based models • with more flexible representation compared to analytical or systems dynamic models • with the ability to incorporate theories and drivers from many social science perspectives • facilitating spatially explicit, fine scale, coupled models of human-environment interaction in the land system Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

41. Can ABM address these challenges? • Bridge the gap between theoretical/process-based and empirical/pattern-based modeling • Model 3-way feedbacks between land-use/management, land cover, and landscape function • Perceived hurdles • Empirical parameterization of theoretical processes • Data needs • Performance assessment standards (validation) Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

42. Other open (perhaps useful) questions: • What is the role for statistical models in parameterization, calibration, verification, and validation of ABMS? (See also Santa Barbara talk/position paper) • How can local-fine scale ABMs contribute to development of: • Regional and global LUCC models • Theory/frameworks of land-change science Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

43. For context: Two ongoing projects Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

44. Exploring changes in residential land use and land management via ABM land market models • Goal is to develop agent-based models of residential suburban and ex-urban land markets that: • Connect land market and land management behavior of residential agents • Allow exploration of the relative contributions of land-use vs.. land-management change (categorical change and change in intensity) to environmental changes • Apply models to the Potomac Gorge watershed (DC metro area) and Southeastern Michigan • Explore the value added of the land market component through comparison to comparable models without a land market component Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

45. Environmental impacts of residential development • Suburban and ex-urban development bring about environmental change • Evaluation of impacts requires understanding of: • Location and timing of land-use change • Characteristics of new or modified development • Land management behavior of new land managers • Three factors jointly determined via land market interactions Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

46. Residential development and water quality (Potomac Gorge) • Residential land use contributes to decreased water quality through increased nutrient loadings and changes in hydrology (flow) • Main research question: What linkages exist between residential land use in the Potomac Gorge watershed (DC area) and the degradation of water quality in tributary streams (a primary threat to rare and endangered species in the Gorge) • Pilot project in progress (nutrient modeling, land manager behavior model, participatory front-end) with proposed extensions (land market model, flow model) Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

47. Water quality changes: Sources and impacts Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

48. Residential development and carbon sequestration • Landscaping choices in existing and new developments may have dramatically different carbon profiles • Main research question: Will ex-urban development in Southeastern Michigan produce a landscape-scale source or sink of carbon, given observed landscaping strategies of developers and landscaping preferences of residential agents? • Collaboration with UM (Dan Brown et al.) to extend Project SLUCE Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

49. Residential development and landscape change Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University

50. Sources of environmental impacts: character/intensity of new land use Residential development is not homogeneous. Both water quality and carbon profiles will depend on: • Built/constructed environment: • Building footprints • Lot sizes and setbacks (roads and driveways) • Construction-stage BMPs (wet/dry detention basins; pavement types) • Landscaping choices • Tree removal/planting • Turf grass extent • Horticultural choices • Native vs. non-native • Annuals vs. perennials Spatial ABM H-E Interactions, Lecture 1 Dawn Parker, George Mason University