MASS: From Social Science to Environmental Modelling Hazel Parry

Outline Background Connections between social and ecological modelling Advantages and logic of using MASS in ecology Multi-agent systems as a unifying methodology for environmental modelling in geography?

Established modelling techniques in ecology and physical geography Differential Equations Lotka-Volterra (predator-prey): Navier-Stokes equations (fluid-flow): Horton equation (infiltration):

Complexity Neither random nor regular, when it is hard to formulate overall behaviour of a system, despite individual-scale information. Self-organization The process by which autonomous agents interact in a seemingly chaotic manner, resulting in global order. Emergence Simple units, when combined, form a more complex whole. For example, ecosystems are a synergy of individuals. “The ecosystem is greater than the sum of its parts” (Odum). Complex systems Made up of agents interacting in a non-linear fashion. The agents are capable of generating emergent behavioural patterns, of deciding upon rules and of relying upon local data. Background: The complexity paradigm

Social vs. Economic vs. Ecological ‘worlds’ Social SciencesEconomicsEcology SocietyEconomic InteractionEcosystem World of (social) interactionsGame/PuzzleWorld of (ecological) interactions InterdependenceInteractionInterdependence/ interaction Dependence, valueUtilityDependence, utility, need ActionStrategy/ MoveAction Dependence theoryGame TheoryEcosystem Theory Interference, Influence, Exchange StrategyCompetition, predation, parasitism

Object based models in ecology and social science Individual-based models Large collection of interacting organisms. Cellular Automata Cells on a grid of specific dimension, undergo transition by global rules. Multi-agent simulation Intelligent agents, with ability to learn about their environment and adapt their behaviour accordingly.

Cellular Automata ‘discrete models of spatio-temporal dynamics obeying local laws’ (Randy Gimblett, 2002, pp2) Grid-based formed by identical cells Interaction of cell with its neighbours Time advances in steps State of cell determined by global rules

Example - diffusion t=0t=1 Von Neumann

Cellular Automata in ecology Le Page and Bousquet Cellular Automata model for the spread of forest fire

Cellular Automata in physical geography Murray-Paola model of sediment transport in rivers Baas Model of sand dune landscape formation

Multi-Agent Systems and Simulation (MASS) Similar to CA Less rigid structure Interactions between distant individuals at a variety of scales Facilitate investigation of lower level mechanisms leading to global structural and dynamical features y x Neighbourhood defined by nearest neighbours Agent Location (x,y)

MASS: a logical ecological modelling strategy

The advantages of a MASS approach Reduced ‘randomness’ Increased flexibility Increased realism – perception, communication, rationality, goals, interactions, autonomy, mobility and collaboration all possible. Can handle complex systems Agents have the capacity to evolve or adapt their behaviour. Don’t need to ‘throw the baby out with the bath water’! Integration of landscape models with ecological and social models

A unifying methodology? Environmental management needs to be more integrated and flexible. Ecological models benefit from an integral dynamic environmental model to produce realistic simulations. They also benefit from a consideration of the social structure and dynamics where decisions impact the entire system. For example: SIMDELTA MODULUS

SIMDELTA Biotopes Shoals of fish Fishermen The artificial world of SIMDELTA (Bosquet and Cambier) Dynamics of fish population Biological and topological factors affecting the evolution of the fish Decision making of the fishermen Village

Discussion Contributions of social science to agent- based simulation in ecology. Potential to use multi-agent simulation in other areas of physical geography. Multi-agent systems as a unifying methodology for environmental modelling in geography?