Combining Multi-Agent System modeling

and

System Dynamics modeling

Ernst Gebetsroither -

Austrian Institute of Technology

Outline

Different methodological approaches

System Dynamics approach

Multi-agent Systems modeling approach

Multi-paradigm simulation

Why?

History

Different combination types

Software tools

MASGISmo a java based multi-paradigm simulation platform

Still open questions and difficulties2

Source: Gilbert & Troitzsch, 1999

History of Simulation in the Social Sciences

System Dynamics (SD) modeling

Some “facts” for SD I

Top Down approach (macro simulation)

Equation Based Modeling (EBM)

Numerical solution of differential equations

Randomness normally not essential

Why macro simulation?

Missing data and information for micro simulation

• Missing information about rules and self-organization processes on

micro scales

Policy maker’s decisions are on an aggregate level (Top Down)

Often better analysed macro phenomena

Technical reasons (calculation time…)

...

Some “facts” for SD II

„Father“ of SD

Jay. W. Forrester from MIT (60ies)

This “shapes” the approach

Features

Quantitative simulation models

State functions, material-flows and information-flows are in the centre of

interest

Normally no detailed spatial relation is considered

Delays and feedback structure are very important (the system structure

shapes the behaviour)

Computer as tool necessary

Modular model building

Special modelling software was developed

• In the 60ies Dynamo (since 90ies Vensim, Stella, Powersim powerful

specialized SD software)

Lower evaluation effort; less data effort to parameterize

System Dynamics modeling procedure I

Verbal description („word model“)

of dynamical processes

Developing Causal loop diagram

Population

birth death

+

+

+

-

Stock-Flow diagram (SFD)

Populationbirths deaths

System Dynamics modeling procedure II

Multi-Agent Systems modeling

Some “facts” for MAS I

Bottom Up approach (micro simulation)

Rule Based Modeling

Only rarely differential equations are used; more often If-Then rules to

describe the Agents’ action

Randomness very essential

Why micro simulation?

More appropriate to simulate social behavior (Bottom Up)

More appropriate to analyze self-organization processes

Sometimes individual behavior is easier to analyze

Missing information about equations on macro scales

Structure is adaptive, emergence is an important characteristics

...

Some “facts” for MAS II

No unique root of MAS

even the definition what agents are is very heterogenious

or what an agent has to have

Features

Quantitative simulation models

State charts, UML, object oriented programming language

Agents’ communication is in the centre of interest

Spatial relation is often important

•To be at the “right” time at the “right” place in the “right” shape

Computer as tool necessary

Modular model building

Special modelling software was developed

• SWARM, RePast, Netlogo, Anylogic

Higher evaluation effort; higher data effort for parameterization

Multi-paradigm Simulation: Why? I

The different approaches are not appropriate for all purposes and objects.

Weakness of the individual approaches can be reduced within the combination

Results of Top Down decisions are often influenced by Bottom Up reactions (self-

organization)

Appropriate tools are available

Multi-paradigm Simulation: Why? II

Modeling method should be determined by the WHY and WHAT not by the HOW!

Source: Figure 1 from Lorenz and Jost, 2006

"Due to familiarisation and (early) association with a specific

modelling paradigm modellers tend overlook other paradigms or

simply are not able to adequately differentiate and apply alternative

approaches." (Lorenz & Jost, 2006)

Multi-paradigm Simulation: History

First important steps arround the year 2001

Important early works:

Scholl 2001:Agent-based and system dynamics modeling: A call for cross study and joint research. In the Proceedings of

34th Annual Hawaii International Conference on System Sciences (HICSS-34), Vol. 3, Maui, Hawaii.

Schieritz and Größler 2003:Schieritz, N., & Größler, A. (2003). Emergent Structures in Supply Chains A study Integrating Agent-Based

and System Dynamics Modeling. Proceedings of the 36th Hawaii International Conference on System

Sciences - 2003, 9.

Borshchev and Filippov, 2004:Borshchev, A., & Filippov, A. (2004). From System Dynamics and Discrete Event to Practical Agent Based

Modeling: Reasons, Techniques,The 22nd International Conference of the System Dynamics Society, July 25

- 29, 2004, Oxford, England.

No general framework how to combine the approaches exists

A first orientational framework for multi-paradigm

modeling

Source: Figure 3 from Lorenz and Jost, 2006

Different combination types

16

Comparison of combination types I

The unidirectional combination:

simplest possibility

step by step calculation, no influence of different time and space

concepts

can be modelled like two models running independently, only input data

exchange has to be managed

verification and validation is easier

The bidirectional combination:

most complex engagement of MAS and SD

with feedback emerg many consequences/difficulties

sensitive to the time-step because each system influences the other

not clear which is first, MAS or SD (circular references possible)

verification and validation because of the feedback to SD very

complicated (input from MAS to SD due to stochastic processes

different for each run) 17

Comparison of combination types II

The SD included in Agents combination:

for some properties such as time-step and data handling simpler than

bidirectional combination

simulation time/effort increases rapidly

differential equation on the individual level often more difficult to assume

than on the aggregate level

software demands very high

verification and evaluation only possible for the combination, no

individual models

18

Multi-paradigm Simulation: Simulation tools

Several tools have been developed during the last 5 -10 years.

Not all are very powerful/helpful!

A detailed description/comparison how to use can be found in:

Ernst Gebetsroither 2010: Combining Multi-Agent Systems Modelling and System Dynamics

Modelling in theory and practice. Dissertation at the Alpen-Adria Universität Klagenfurt,

Fakultät für Technische Wissenschaften, 1-152. (send me an e-mail if you are interested).

Simulation tools comparison

MASGISmo EIDE AnyLogic NetLogo RePastSymphony

open source planned yes no yes yes

advanced SD analysis yes no no no no

graphical building blocksonly for SD

yes only for EI

(MAS)yes yes

only for MAS, not

for SD

operating systemsWindows & Mac

independent

(JAVA needed)

independent (JAVA

needed)

independent (JAVA

needed)

independent

(JAVA needed)

GIS integration yes no yes, not in all

versions

Java extensions can

be integrated

Java extensions

can be integrated

GIS analysis yes no ? ? ?

Stand alone SD (for a built

MAS-SD model)yes yes no no no

advanced SD analysis (Causal

Tracing loop analysis...)yes no no no no

advanced MAS analysisno

yes but no spatial

referenceyes yes yes

MASGISmo developed during the years 2004 - 2010

A JAVA-programmed multi-paradigm simulation platform

MASGISmo a MAS SD Modeling platform

Multi-paradigm Agent-based System Dynamics GIS modelling platform

MASGISmo program-GUI developed with support of a FH Student - Dominic

Piegsa (2005-2006) at AIT and extended within several projects

MASGISmo 2006

MASGISmo 2008

MASGISmo: General connection scheme

Characteristics of MASGISmo I

Combination of:

RepastJ for MAS

Connection to SD Software Vensim via dynamic link libraries

• Was already developed 2004 as„GIS-SD connection“ (Vensim and

ARCMap)

Programmed in JAVA

Import and Export of GIS maps as ASCII grids

Programmed special GIS features as:

• zooming in and out, panning, layer selection, map-legends,

changing element colour and element value,

• a MiniView window (to show the zooming position)

• moving layers up and down do make them visible

• setting layers as visible or invisible

• retrieving information about each layer and its elements (e.g. about

the different land use areas)

Characteristics of MASGISmo II

Interactive features:

one-click adding of new elements or deleting of elements (streets,

buildings, settlements, …)

changing parameter values with sliders and input fields

popping up message boxes to show users important system parameter

and retrieve new input values, etc.

importing and exporting of new layers (maps such as ASCII grids)

saving and deleting layers

Analyse Tools:

snapshots at individual time steps of the whole area or of a selected

area,

parameter charts and tables,

automatically captured movies of the simulation,

automatically captured changes of parameters (made by the user)

dynamic calculation of differences between different time periods

MASGISmo: Advantages and Disadvantages I

Advantages:

fully programmed in Java

Java source code can be changed as well as the REPAST libraries as

they are open source

uses specialised SD software (Vensim)

the GUI, the graphical user interface can be changed according to the

customer’s needs

GIS visualisation and analysis features are integrated and can be

extended according to the model’s/user’s needs

GIS files can be imported/ exported as ASCII-grids

Eclipse framework can be used for programming and debugging

(verification)

Java programmed extensions can be easily integrated via jar libraries

29

MASGISmo: Advantages and Disadvantages II

Disadvantages:

comprehensive documentation is not available, Java API documentation

exists.

tutorials not available (currently under construction)

not often used or improved

MAS and SD parts are developed by different groups (Vensim and

Repast developers)

lack of community which uses the platform

no professional support by developers

30

Examples for multi-paradigm simulation

GIS based water resource management of the Dead Sea

region

(EU-project)Ernst Gebetsroither, Wolfgang Loibl, Rudolf Orthofer

Dead Sea project overview

Main problem: The Dead Sea water level is shrinking and

causing many problems (> 1m/yr) http://systemsresearch.ac.at/projects/dead_sea/

Combination of Top down and Bottom up approaches

(unidirectional)

System Dynamics approach (SD) used for modelling overall water

availability and price

MAS used to model the agent’s reaction

to SD input spatial changes

Used MAS-SD-GIS

VENSIM-SD model for the overall water flows

MASGISmo Model

Simulation results within different scenarios

DPP dead sea surface area

800

720

640

560

480

400

4 4 4 4 4 4 4 44

44

3 3 3 3 3 3 3 3 3 3 32 2 2 2 2 2 2 2 2 2 2 2

1 1 1 1 1 1 1 1 1 1 11

2005 2007 2009 2011 2013 2015 2017 2019 2021 2023 2025

Time (year)

DPP dead sea surface area : Unilateral Action SC1 km²1 1 1 1 1 1 1DPP dead sea surface area : Current Trend SC2 km²2 2 2 2 2 2DPP dead sea surface area : Demand Managed Basin SC3 km²3 3 3 3 3DPP dead sea surface area : Supply-driven Basin SC4 km²4 4 4 4 4 4

Land use change Simulation

Possible impacts of climate change to land use in an Austrian alpine

region

Provison project future.scape:mountain.scape

Simulation of impacts to forest and settlements

Land use change Simulation

© systems research

© systems research

© systems research

Still open questions and difficulties for multi-

paradigm simulations

A more unified “method” for building Multi-agent systems

Complex simulation analysis

MAS models have the same problem because of randomness

Lacking good tools

Model-Evaluation

Verification and Validation effort high

Effort depends on combination type

• A first heuristic is presented in Gebetsroither, 2010

Model sharing

Use & Understand the models

44

MASGISmo in action

45

Start

Video

http://systemsresearch.ac.at/exchange/gebetsroither/Tutorial_von_MASGIS/

Welcome.html

Acknowledgments:

Prof. Günther Ossimitz (University of Klagenfurt)

and

Prof. Peter Fleissner (TU Vienna)

Thanks for your attentions!

More Information can be found in den Conference paper

and in:

Ernst Gebetsroither 2010: Combining Multi-Agent Systems

Modelling and System Dynamics Modelling in theory and practice.

Dissertation at the Alpen-Adria Universität Klagenfurt, Fakultät für

Technische Wissenschaften, 1-152. (send me an e-mail if you are

interested).

Ernst.Gebetsroither@ait.ac.at

46

References

Borshchev, A.,& Filippov, A. (2004). From System Dynamics and Discrete Event to Practical Agent Based

Modeling: Reasons, Techniques,The 22nd International Conference of the System Dynamics Society, July 25 - 29,

2004, Oxford, England.

Ernst Gebetsroither (2010). Combining Multi-Agent Systems Modelling and System Dynamics Modelling in theory

and practice. Dissertation at the Alpen-Adria Universität Klagenfurt, Fakultät für Technische Wissenschaften, 1-

152.

Gilbert, N. & Troitzsch, K. G. (1999) Simulation for the Social Scientist. Open University Press, London.

Lorenz, T. & Jost, A. (2006). Towards an orientation-framework for multiparadigm modeling. Proceedings of the

24th international conference of the System Dynamics Society.

Schieritz, N., & Größler, A. (2003). Emergent Structures in Supply Chains A study Integrating Agent-Based and

System Dynamics Modeling. Proceedings of the 36th Hawaii International Conference on System Sciences - 2003,

9.

Scholl (2001). Agent-based and system dynamics modeling: A call for cross study and joint research. In the

Proceedings of 34th Annual Hawaii International Conference on System Sciences (HICSS-34), Vol. 3, Maui,

Hawaii.

47