Combining Multi-Agent System modeling and System Dynamics modeling

Transcription

1 Combining Multi-Agent System modeling and System Dynamics modeling Ernst Gebetsroither - Austrian Institute of Technology

2 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 difficulties 2

3 History of Simulation in the Social Sciences Source: Gilbert & Troitzsch, 1999

4 System Dynamics (SD) modeling

5 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 )...

6 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

7 System Dynamics modeling procedure I Verbal description ( word model ) of dynamical processes Developing Causal loop diagram + Population - birth + + death

8 System Dynamics modeling procedure II Stock-Flow diagram (SFD) births Population deaths

9 Multi-Agent Systems modeling

10 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...

11 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

12 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 (selforganization) Appropriate tools are available

13 Multi-paradigm Simulation: Why? II Modeling method should be determined by the WHY and WHAT not by the HOW! "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) Source: Figure 1 from Lorenz and Jost, 2006

14 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 34 th 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 36 th Hawaii International Conference on System Sciences , 9. Borshchev and Filippov, 2004: Borshchev, A., & Filippov, A. (2004). From System Dynamics and Discrete Event to Practical Agent Based Modeling: Reasons, Techniques,The 22 nd International Conference of the System Dynamics Society, July 25-29, 2004, Oxford, England. No general framework how to combine the approaches exists

15 A first orientational framework for multi-paradigm modeling Source: Figure 3 from Lorenz and Jost, 2006

16 Different combination types 16

17 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

18 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

19 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, (send me an if you are interested).

20 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 blocks only for SD yes only for EI (MAS) yes yes only for MAS, not for SD operating systems Windows & 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) advanced SD analysis (Causal Tracing loop analysis...) yes yes no no no yes no no no no advanced MAS analysis no yes but no spatial reference yes yes yes

21 MASGISmo developed during the years A JAVA-programmed multi-paradigm simulation platform

22 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 ( ) at AIT and extended within several projects

23 MASGISmo 2006

24 MASGISmo 2008

25 MASGISmo: General connection scheme

26 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)

27 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

28 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

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

30 Examples for multi-paradigm simulation

31 GIS based water resource management of the Dead Sea region (EU-project) Ernst Gebetsroither, Wolfgang Loibl, Rudolf Orthofer

32 Dead Sea project overview Main problem: The Dead Sea water level is shrinking and causing many problems (> 1m/yr) 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

33 VENSIM-SD model for the overall water flows

34 MASGISmo Model

35 Simulation results within different scenarios 800 DPP dead sea surface area Time (year) DPP dead sea surface area : Unilateral Action SC DPP dead sea surface area : Current Trend SC DPP dead sea surface area : Demand Managed Basin SC DPP dead sea surface area : Supply-driven Basin SC km² km² km² km²

36 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

37 Land use change Simulation

38 systems research

39 systems research

40 systems research

41 Still open questions and difficulties for multiparadigm 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

42 MASGISmo in action Start Video Welcome.html Acknowledgments: Prof. Günther Ossimitz (University of Klagenfurt) and Prof. Peter Fleissner (TU Vienna) 45

43 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, (send me an if you are interested). 46

44 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, 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 , 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