Simulating Human Agropastoral Activities Using Hybrid Agent-Landscape ModelingM. 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 InformaticsFulton School of EngineeringArizona State University, Tempe, Arizona

Some Common Modeling ApproachesEntityRelationCellular AutomataSystem TheoryAutomataRelationsFunctions

Regular PatternsStructures

DE, DAE, ODE, PDEAIRules

AgentsUse term agent to refer to autonomous software constructMay have some apriority knowledge of worldPrecepts provide input to update world knowledgeLogic to react to or create plan using precepts and current stateGoal-based behaviorEffectors to interact with / change environment

Agent ModelsA variety of construction approaches existModels can be conceptualized and formalized as discrete units with continuous or discrete time baseModels can be conceptualized and formalized as continuous phenomena with continuous time baseMost approaches are closely related to concept of objects having autonomous and reactive capabilitiesDo 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)

Landscape ModelsModels may be described as continuous and discrete processesData ModelsCellular AutomataA collection of FSMsCells are connected to one another using regular patternsEach FSM may have a unique representation ODEsA collection of equations where one independent variable changes as a function of its derivate and other variablesPDEsA collection of relations involving multiple independent variables and their partial derivatives

Data Landscape ModelsA variety of representations are defined for representing data setsData sets can represent arbitrary relationships among data values as computed by a set of functionsSpatial/temporal relationshipsArbitrary ordering of data sets can be defined (each data set or layer is independent)

GRASS / GIS ModelsData management toolModels are constructed only when the relations between the data sets are defined.

F: set of algebraic functionsX: set of arrays or matricesscript: ordered execution of f, given x; where f F and x X

GRASS (Landscape Models)

Dynamic Landscape ModelGRASSODEsPDEsAccount for concepts of state and timeCA

System Model ComposabilityMonolithic vs. Multi-ModelThe landscape model may itself become a multi-modeli.e., may contain independent models that interactHomogeneous vs. Heterogeneous Difficult to use a single-modeling formalism to efficiently construct both the landscape and human modelsMultiple models within the landscape model may increase the inefficiencyUsing disparate model types creates its own challenge; namely, the interaction between the modelsHowever, it may allow an opportunity to manage disparities within the landscape model itself

Modeling ApproachesA modeling formalism consists of a model specification and interaction algorithmMono-FormalismDecomposition (or hierarchical composition) of a model into (from) parts can be carried out systematicallySuper-FormalismOne model is encapsulated within another and its interface is not exposed to other models within the system.Encapsulating model handles data mappingMeta-FormalismTwo disparate formalisms are mapped to a third, common formalism and made to interactPoly-FormalismDisparate 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 schemesIncreases flexibility

Interface vs. InteractionSoftware Level vs. Model LevelCreating an interface between the two models allows them to communicateTight couplingOne (or both) models require detailed knowledge of the other to manage data transformationsChange 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 couplingInteraction 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.

MEDLAND ApproachLandscape model and human model are equally important.Model and simulate agent and landscape dynamics separately and synthesize them to understand their complex interactions.Landscape ModelGRASS / GISHuman ModelABM / DEVS

Landscape ModelEnvironment and climate elementse.g., soil, slope, and precipitation dataMulti-model SystemEnvironment / climate dynamicse.g., climate and erosion models will be independent but may interfaceChanging landscape impacts the human modele.g., decreased soil quality produces a reduced crop yield

Human ModelA human household is represented as an agentHouseholds are grouped into villagesHouseholds have goals, requirements based upon population, and the ability to manage some resourcesHousehold actions impact the environmente.g., deforesting to plant crop increases soil erosion

Model System

Interaction ModelDescribe relations between agents and landscape dynamics in a well-defined and flexible fashionModel specification Software developmentUser-friendly interface for simulation executionCombined landscape and agent dynamicsDistributed and web-based simulation and modeling

StatusDevised agent-model using a top-down approach resulting in high-level systemAgents (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.

Current WorkDeveloping bottom-up agent designStart with as much detail as possible; knowingly abstract away details to appropriate levelDevise interaction modelGoal is to facilitate project research by allowing as much flexibility as possible between the models and providing for rapid modification of model settings.

Demonstrationsoil qualityland ownersland useDEVS simulation tool