1. Simulating Human Agropastoral Activities Using Hybrid Agent- Landscape Modeling M. Barton School of Human Evolution and Social Change College of Liberal Arts and Sciences Arizona State University, Tempe, Arizona URL: http://www.asu.edu/clas/shesc/projects/medland/ G. Mayer and H. Sarjoughian Arizona Center for Integrative Modeling & Simulation School of Computing and Informatics Fulton School of Engineering Arizona State University, Tempe, Arizona

2. Some Common Modeling Approaches Entity Relation Cellular AutomataSystem TheoryAutomata Relations Functions Regular Patterns Structures DE, DAE, ODE, PDE AI Rules

3. Agents Use term “agent” to refer to autonomous software construct –May have some apriority knowledge of world –Precepts provide input to update world knowledge –Logic to react to or create plan using precepts and current state Goal-based behavior –Effectors to interact with / change environment

4. Agent Models A variety of construction approaches exist –Models can be conceptualized and formalized as discrete units with continuous or discrete time base –Models can be conceptualized and formalized as continuous phenomena with continuous time base Most approaches are closely related to concept of “objects” having autonomous and reactive capabilities –Do not quantify time, instead depend on ordered events (weak representation of time) e.g., SWARM (Object Orientation) –Explicitly account for time (strong representation of time) e.g., DEVS (Systems Theory)

5. Landscape Models Models may be described as continuous and discrete processes –Data Models –Cellular Automata A collection of FSMs Cells are connected to one another using regular patterns Each FSM may have a unique representation –ODEs A collection of equations where one independent variable changes as a function of its derivate and other variables –PDEs A collection of relations involving multiple independent variables and their partial derivatives

6. Data Landscape Models A variety of representations are defined for representing data sets –Data sets can represent arbitrary relationships among data values as computed by a set of functions –Spatial/temporal relationships –Arbitrary ordering of data sets can be defined (each data set or layer is independent)

7. GRASS / GIS Models Data management tool Models are constructed only when the relations between the data sets are defined. F: set of algebraic functions X: set of arrays or matrices script: ordered execution of f, given x; where f  F and x  X

8. GRASS (Landscape Models) functions relations GUI scripts data arrays/matrices

9. Dynamic Landscape Model GRASS ODEs PDEs Account for concepts of state and time CA

10. System Model Composability Monolithic vs. Multi-Model –The landscape model may itself become a multi-model i.e., may contain independent models that interact Homogeneous vs. Heterogeneous Difficult to use a single-modeling formalism to efficiently construct both the landscape and human models –Multiple models within the landscape model may increase the inefficiency Using disparate model types creates its own challenge; namely, the interaction between the models –However, it may allow an opportunity to manage disparities within the landscape model itself

11. Modeling Approaches A modeling formalism consists of a model specification and interaction algorithm Mono-Formalism –Decomposition (or hierarchical composition) of a model into (from) parts can be carried out systematically Super-Formalism –One model is encapsulated within another and its interface is not exposed to other models within the system. –Encapsulating model handles data mapping Meta-Formalism –Two disparate formalisms are mapped to a third, common formalism and made to interact Poly-Formalism –Disparate formalisms interact with a third formalism. This third formalism contains details on model composability and execution for each model to support this interaction. –Data interaction, data transformation, control schemes Increases flexibility

12. Interface vs. Interaction Software Level vs. Model Level Creating an interface between the two models allows them to communicate –Tight coupling One (or both) models require detailed knowledge of the other to manage data transformations Change to one impacts the other. –When simulation (and visualization) is considered, it becomes more encapsulation of one model by the other – assuming system / visualization control is given to only one model. Creating an interaction (using an interaction model) provides more efficient communication. –Loose coupling Interaction model maintains the specifics of each model allowing the individual models to be revised with minimal impact to the other. –Provides central location for initialization, control, and visualization of simulation. –Interaction model may also be used to facilitate integration of multiple models within the environment model.

13. MEDLAND Approach Landscape model and human model are equally important. Model and simulate agent and landscape dynamics separately and synthesize them to understand their complex interactions. Landscape Model GRASS / GIS Human Model ABM / DEVS

14. Landscape Model Environment and climate elements –e.g., soil, slope, and precipitation data Multi-model System –Environment / climate dynamics –e.g., climate and erosion models will be independent but may interface Changing landscape impacts the human model –e.g., decreased soil quality produces a reduced crop yield

15. Human Model A human household is represented as an agent Households are grouped into villages Households have goals, requirements based upon population, and the ability to manage some resources Household actions impact the environment –e.g., deforesting to plant crop increases soil erosion

16. Model System agent Agent DEVS Models landscape Cellular GRASS Models Data Transformation + Control Model interactions

17. Interaction Model Describe relations between agents and landscape dynamics in a well-defined and flexible fashion –Model specification –Software development User-friendly interface for simulation execution –Combined landscape and agent dynamics Distributed and web-based simulation and modeling

18. Status Devised agent-model using a top-down approach resulting in high-level system –Agents (households) contain a population. –Population creates need for food and provides labor force. –Agent must evaluate surrounding land and manage it to meet survival and growth goals. –Agent may cultivate, fallow, and release land. Created an interface between the DEVS agents and GRASS landscape.

19. Current Work Developing bottom-up agent design –Start with as much detail as possible; knowingly abstract away details to appropriate level Devise interaction model –Goal is to facilitate project research by allowing as much flexibility as possible between the models and providing for rapid modification of model settings.

20. Demonstration wild cultivated fallow goodpoor soil quality land owners land use household 1 household N household 2 … DEVS simulation tool