University of North Texas ~ 04.04.2003 Agent Based Modeling of Human and Natural Systems and their interactions Authors: Armin R. Mikler ([email protected]),[email protected]

University of North Texas ~ 04.04.2003

Agent Based Modeling of Human and Natural Systems and their interactions

Authors: Armin R. Mikler ([email protected]),Baird Callicott ([email protected]),Michael Monticino ([email protected]),Saqib Khalil ([email protected])

Presentation: Saqib Khalil (SwarmFest 2003, Notre Dame, IN)

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Research funded by Grant CNH BCS-0216722 from the National Science Foundation, Biocomplexity in the Environment (BE), Coupled Natural and Human Systems (CNHS) program.


Overview

This paper provides an overview of the simulation of interacting models that combine forest landscape dynamics with human values and decision making.

It also explores mathematically complex interaction and feedback relationships of integrated models.

We attempt to generalize results by applications of models to several hierarchical scales across scales and cultures. Network

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We focus on the human systems side of this research.

We explore our choice of Agent Based Modeling for simulating human behavior.

We introduce Agent Based Modeling (ABM) and compare and contrast it with Equation Based Modeling (EBM).

Finally, we describe a toy model that serves as a prototype for the larger human system.


Understanding Biocomplexity

A new area of multi- and interdisciplinary scientific research focusing on the interactions of dynamic living systems.

Coupled Natural and Human Systems (CNHS) Dynamic interactions between biotic communities,

their associated ecosystems, and their human inhabitants.

Two major role players Human beings and their natural environments. Research seeks to understand important interactions

between these two components. The goal is to be able to predict effects of these

interactions on both players.NetworkResearchLaboratory


The Goal of Coupled Natural and Human Systems Research

Build reliable computer models to understand and predict the effects of human decisions on vegetation cover. the effects of changed vegetation cover on ecosystem

function. the feedback looped effects of changed vegetation

cover and ecosystem function on subsequent human decisions.

The simulation of these dynamics will be useful for Environmentalists, Geographers, Policy makers, etc. It will enable them to anticipate the future

environmental and social consequences and therefore to make better informed choices. Network

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Our Approach

We model the anthropogenic disturbance and stressors with multi-agent simulation methods and utility functions.

In the figures below,HS and NS stand for the human and

natural systems.I1 through I4 represent the different

interfaces between the human and natural systems. Network

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Our Approach (Cont.)


Agent

I1 I2

I3I4

AgentAgent

AgentAgentAgent

Ui= f(x,y,z)

Uj= g(x,z)

HS NS


Our Approach (Cont.)


Agent

I1 I2

I3I4

AgentAgent

AgentAgentAgent

Ui= f(x,y,z)

Uj= g(x,z)

HS• Parameters• Functions• Vectors

And/or

Agent

NS

• Parameters• Functions• Vectors


Modeling Coupled Natural and Human Systems

For modeling forest dynamics and ecosystem functions, we have chosen hydrological models (such as FACET and MOSAIC).

For modeling the human systems (that will be linked to both the land-cover and hydrological models), we have chosen to use agent based models (in particular, Swarm).



Agent Based Modeling vs. Equation Based Modeling

Equation Based Model (EBM): identifies system variables. evaluates or integrates sets

of equations.

Agent Based Model (ABM): A new approach to system

modeling and simulation. An agent-based model is one

in which the basic unit of activity is the agent.

Usually a model will contain many agents (at least tens, occasionally many thousands) and its outcomes are determined by the interactions of the agents.

EBM: for systems that can be modeled centrally (self-control). Example: ODE model of a

supply chain at processes level.

ABM: for domains characterized by a high degree of localization and distribution. Example: Urban Planning

(STREETS Model).Supply chain with different components (Component Level).Network

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Agent Based Modeling vs. Equation Based Modeling (Cont.)

Integration of EBM analysis tools into ABM – A powerful way to study selection in systems with complex dynamics.Example: Individual components

(Agents) may be modeled through EBM.



Design/Implementation Cycle: Biocomplexity


Identify players. (Agent objects)

Extract actions. (Behavioral model)

Derive preferences and utilities. (Functional model)

Identify player interaction with environment.

Identify player interaction with players.

ABMEBM

ABM


Agents in context of ABM

Agents are any component in an ABM that have: Internal data representations (memory or

state). Means for modifying their internal data

representations (perceptions). Means for modifying their environment

(behaviors).


BEHAVIORPERCEPTIONS

AGENT


ABM Implementation

Swarm:Swarm is a software package for multi-agent simulation of complex systems, originally developed at

the Santa Fe Institute.

Swarm Features: In the Swarm system the basic

unit of simulation is the swarm, a collection of agents executing a schedule of actions.

Swarm supports hierarchical modeling approaches whereby agents can be composed of swarms of other agents in nested structures.


Swarm provides object oriented libraries of reusable components for building models and analyzing, displaying, and controlling experiments on those models.Observer Swarm

Model Swarm

Creates

Schedules

Collects Info.

Data/Methods

OOP


A Simple Scenario


Two agents: A1 & A2 A1 issues lumber cutting

permits (government). A2 is a lumber company.

Goals: A1 tries to control forest

fragmentation as prescribed by the government guidelines and regulations.

A2 tries to increase the number of permits so it can maximize it’s personal revenue.

Clearly, different agent types.

Conflicting Goals: A1 tries to minimize and

control forest fragmentation by issuing a limited number of permits. At the same time, it wants to maximize employment of the region for acquiring local support.

If A1 issues more permits, A2 can hire more people and consequently, there will be less unemployment but at the same time, there will be more forest fragmentation.


Players and Interaction


HS NS

Agent1

Agent2

fragmentation

cut trees

cutting permits

government

minimizeunemployment

maximizeemployment

minimizefragmentation

invest

marketsell trees

$$$

Landscape Model

Hydrological Model

Front stand Model

GIS/RS Info

Climate & Natural

Disturbance Scenarios


A Mathematical Model


Landowners (An Abstract Model)

A formal mathematical model in which we strive to depict an agent’s (landowner’s) actions.

ActionsLet, A (a1, a2, a3,…, an) represent

the set of actions available to the landowner. For example, an individual

can perform no action, protest individually, or organize into associations.

Environment Landowners are assumed to

view the state of their vicinity (environment) by evaluating it with respect to a set of factors (F1, F2, F3,…, Fn). For example, one factor

is the intensity of residential development in the neighborhood.

Each factor is assumed to have a finite number of possible levels, represented by (f1, f2, f3,…, fn). For example, high

density development (several houses per acre), moderate density (golf course style developments), or low density (multi-acre residences).


A Mathematical Model (Cont.)


Utility Function The preferences and value

trade-offs of landowners for different states is expressed by a utility function.

We employed a conjoint analysis survey method.

These utility functions are dependent on Environmental state. Potential costs of taking a

given action.Let, u (ai, f1,…, fn) denote the utility

of an action-state pair.

If u (ai, f1,…, fn) > u (aj, g1,…,gn), then the landowner prefers action-state pair u (ai, f1,…, fn) over u (aj, g1,…,gn).

Probability Given a perceived threat to

the environment (t), and a potential action (a) in response to that threat, a landowner constructs a probability measure, denoted by, p (t, a).

Final expected utility of an action:u (a | t) = {f1,….,fn} u (ai, f1,…, fn) *

p (t, a) (f1, f2, f3,…, fn)

The action with the highest expected utility is selected by the landowner.


Assigning Preferences (Implementation)

The preferences and value trade-offs of an agent are usually represented by a preference structure.

In the introductory implementation, we create a preference structure for an agent.

Preference is a loosely defined concept, so to choose one behavior over others, a numerical utility function is usually defined.

The randomness of the code allows for greater flexibility when creating preference structures for different agent types.



Assigning Preferences (Sample Code)


// AgentPreference.java// Inputs the preference structure of an agent

import swarm.Globals;import swarm.defobj.Zone;import swarm.objectbase.SwarmObjectImpl;import java.io.*;import java.util.*;

public class AgentPreference extends SwarmObjectImpl

{ // some variables private int totalElements; private int preferredElements; private Vector preferenceTable;

// constructor public AgentPreference(int totalElements) { this.totalElements = totalElements; // randomizing the preferences preferredElements = 1 + (int)

(Math.random() * totalElements);

// giving weight to each element int totalWeight = 100; int eachWeight =

totalWeight/preferredElements;

//initializing the preferences preferenceTable = new Vector(); // PreferenceStructure class defined

below PreferenceStructure ps; for(int i = 0; i<totalElements; i++) { ps = new PreferenceStructure(i, 0); preferenceTable.addElement(ps); }

for(int i = 0; i <preferredElements; i++) { if(i == preferredElements-1) { ps = new PreferenceStructure(i,

totalWeight); preferenceTable.set(i,ps); continue; }

Code Section 1


Assigning Preferences (Sample Code) (Cont.)


// randomly adding weight to each elementint thisWeight = 1 + (int) (Math.random() *

eachWeight * 1.5);ps = new PreferenceStructure(i, thisWeight);preferenceTable.set(i,ps);

totalWeight = totalWeight - thisWeight;eachWeight = totalWeight/(preferredElements

- (i+1)); }}

public String toString(){ return totalElements + ":" +

preferredElements + ":" + preferenceTable.toString();

}

public Vector getPreferenceTable(){ return preferenceTable;}

}

// class for storing the preferencesclass PreferenceStructure{ private int elementNumber; private int preferenceValue;

public PreferenceStructure(int elementNumber, int preferenceValue)

{ this.elementNumber = elementNumber; this.preferenceValue =

preferenceValue; }

public String toString() { return elementNumber + ":" +

preferenceValue; }

public int getPreferenceValue() { return preferenceValue; }}

Code Section 2


Summary and Future Work

Agent Based Modeling allows researchers to investigate complex adaptive systems and allows them to create artificial worlds that model activity in the natural world.

In this paper, we have presented a toy model of how an agent makes decisions.

Our initial implementation includes assignment of preferences.

Currently, we consider one type of agent (landowner). In future, we plan to add more agent types.

We would like to thank all project participants for their valuable comments and feedback during preparation of this paper. Network

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References

Cardelli, L., 1995, "A Language with Distributed Scope".

Middleton, S. E., "Interface agents: A review of the field", Technical Report Number: ECSTR-IAM01-001, University of Southampton.

Minar, N., Burkhart, R., Langton C., Askenazi M., 1996, "The Swarm Simulation System: A Toolkit for Building Multi-agent Simulations".

Schelhorn, T., O'Sullivan, D., Haklay, M. and Thurstain-Goodwin, M., 1999, "STREETS: an agent-based pedestrian model".

Langton, C., Ropella, G. Swarm Development Group. 2000. Felton, CA. (July 18, 2002); http://www.swarm.org.

Jennings, N., Wooldridge, M., 1998. Agent Technology, Foundations, Applications, and Markets. Springer-Verlag Berlin Heidelberg.

Johnson, P., Lancaster, A., 2000. Swarm User Guide. Swarm Development Group.

Parunak, H., Savit, R., Riolo, R. Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users' Guide. 1998. Proceedings of Workshop on Modeling Agent Based Systems (MABS98), Paris.



References

Acevedo, M. F., Ablan, M., Urban, D. L., and Pamarti, S. 2001. Estimating parameters of forest patch transition models from gap models. Environmental Modeling and Software 16: 649-658.

Acevedo, M. F., Urban, D. L., and Shugart, H. H. 1996. Models of Forest Dynamics based on roles of tree species. Ecological Modeling. 87/1-3:267-284.

Acevedo, M. F., Pamarti, S., Ablan, M., Urban, D. L., and Mikler, A. 2001. Modeling forest landscapes: parameter estimation from gap models over heterogeneous terrain. Simulation 77:53-68.

Acevedo, M. F., Urban, D. L., and Ablan, M. 1995. Transition and gap models of forest dynamics. Ecological Applications. 5(4):1040-1055.

Anderson, J., 2002. An Agent-Based Event Driven Foraging Model. Natural Resource Modeling, Volume 15, Number 1.



Contact

Saqib Khalil• [email protected]

• http://students.csci.unt.edu/~khalil• http://www.geog.unt.edu/biocomplexity



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University of North Texas ~ 04.04.2003 Agent Based Modeling of Human and Natural Systems and their interactions Authors: Armin R. Mikler ([email protected]),[email protected]