NEXT GENERATION NEXT GENERATION MODEL MANAGEMENTMODEL MANAGEMENT

AND INTEGRATIONAND INTEGRATION

Prof. Daniel DolkProf. Daniel DolkCSM WorkshopCSM WorkshopAugust 2006August 2006

OVERVIEWOVERVIEW

• Retrospective of model management Retrospective of model management

• Elements of NGMMI Elements of NGMMI

• Emergence of computational modeling and Emergence of computational modeling and computational experimentation in computational experimentation in scientific inquiryscientific inquiry

• Virtual environments in the form of Society Virtual environments in the form of Society of Simulationsof Simulations

““It is possible to view every process that occurs in nature or elsewhere as a It is possible to view every process that occurs in nature or elsewhere as a computation” computation” – – Stephen WolframStephen Wolfram

““Information is static…computation is dynamic” – Rudy RuckerInformation is static…computation is dynamic” – Rudy Rucker

Theory Experiment

SCIENTIFIC METHOD, 15TH-20TH CENTURIES

Design

Analysis and Explanation

Theory Experiment

SCIENTIFIC METHOD, 1950 - PRESENT

Modeling &Simulation

Analysis &

Explanation

Analysis

Design

Design

Design

ValidationValidation

MODEL MANAGEMENT MODEL MANAGEMENT REDUXREDUX• ““The process of representing, solving, analyzing The process of representing, solving, analyzing

and integrating analytical models (primarily in the and integrating analytical models (primarily in the information and management sciences (OR/MS)) information and management sciences (OR/MS)) in computer executable form” -- Dolkin computer executable form” -- Dolk

• Initially conceived as a modeling counterpart to Initially conceived as a modeling counterpart to data managementdata management– Models as a corporate resourceModels as a corporate resource– Models, like data, need to be systematically managedModels, like data, need to be systematically managed– Rich vein of OR/MS-based models and solversRich vein of OR/MS-based models and solvers– Model management system Model management system

• Part of the troika of DSS Part of the troika of DSS – Data, models, dialogue (later, knowledge)Data, models, dialogue (later, knowledge)– Models as the lynchpin for decision-making (Simon et al)Models as the lynchpin for decision-making (Simon et al)

MODEL MANAGEMENT MODEL MANAGEMENT REDUX:REDUX:

MODEL MANAGEMENT MODEL MANAGEMENT SYSTEMSYSTEM““If DBMS, why not MMS?”If DBMS, why not MMS?”

Desirable Requirements/Features of an MMS:Desirable Requirements/Features of an MMS:

• Support entire modeling life cycleSupport entire modeling life cycle• Uniform representation of modelsUniform representation of models• Modeling languagesModeling languages• Portfolio of cross-paradigm OR/MS modelsPortfolio of cross-paradigm OR/MS models• Library of solversLibrary of solvers • Model integrationModel integration• Separation of models and dataSeparation of models and data• Separation of models and solversSeparation of models and solvers• Leverage RDBMS for data manipulationLeverage RDBMS for data manipulation

Central issue: Model RepresentationCentral issue: Model Representation

MODEL MANAGEMENT MODEL MANAGEMENT REDUX:REDUX:

MODEL REPRESENTATIONMODEL REPRESENTATIONTheoretical driver: Is there a way to Theoretical driver: Is there a way to

represent models that is comparable in represent models that is comparable in power to the relational theory power to the relational theory representation of data?representation of data?

•Relational (Blanning)Relational (Blanning)

•Object-oriented (Dolk)Object-oriented (Dolk)

•Structured modeling (Geoffrion)Structured modeling (Geoffrion)

•Logic modeling (Bhargava, Kimbrough)Logic modeling (Bhargava, Kimbrough)

•Graph grammars (C. Jones)Graph grammars (C. Jones)

•Metagraphs (Blanning, Basu)Metagraphs (Blanning, Basu)

FEEDMIX MODEL FEEDMIX MODEL SM GENUS GRAPHSM GENUS GRAPH

NUTR MATERIAL

MIN

ANALYSIS

UCOSTQ

T:NLEVEL

NUTR_MATERIAL

TOTCOST

NLEVEL

SM ELEMENTAL DETAIL SM ELEMENTAL DETAIL TABLES FOR FEEDMIX TABLES FOR FEEDMIX

MODELMODEL

STRUCTURED MODELING STRUCTURED MODELING CONTRIBUTIONSCONTRIBUTIONS

• ContributionsContributions– Formal semantic ontology for modelsFormal semantic ontology for models– Math models as conceptual modelsMath models as conceptual models– Full language (SML) and implementation Full language (SML) and implementation

(FW/SM)(FW/SM)– Model reuse and integrationModel reuse and integration– Multiple modes of model representationMultiple modes of model representation

LIMITATIONS OF LIMITATIONS OF STRUCTURED MODELINGSTRUCTURED MODELING

““Why isn’t structured modeling (or equivalent) used as a matter of Why isn’t structured modeling (or equivalent) used as a matter of course in OR/MS modeling endeavors?”course in OR/MS modeling endeavors?”

• Endogenous factors:Endogenous factors:– No graph-driven implementation No graph-driven implementation – Static vs. dynamic representationsStatic vs. dynamic representations– Complexity of indexing semanticsComplexity of indexing semantics

• Exogenous factors:Exogenous factors:– Math as legal tender: Math as legal tender: OR analysts are overwhelmingly mathematicians OR analysts are overwhelmingly mathematicians – Overhead of conceptual modeling: Even database analysts resist Overhead of conceptual modeling: Even database analysts resist

conceptual modelingconceptual modeling– Models don’t command the same respect as dataModels don’t command the same respect as data– UML has become the lingua franca of conceptual modelingUML has become the lingua franca of conceptual modeling– Internet and distributed computingInternet and distributed computing

• (“It can be done” & “It should be done”) ~=> (“It will be done”)(“It can be done” & “It should be done”) ~=> (“It will be done”)

CONTRIBUTIONS OF 1CONTRIBUTIONS OF 1STST GENERATION MODEL GENERATION MODEL

MANAGEMENTMANAGEMENT• Decoupling of models, solvers and dataDecoupling of models, solvers and data

– ““run-time” binding of data and solver to model run-time” binding of data and solver to model representation representation

• Model representation formalismsModel representation formalisms– Structured modeling (Geoffrion); meta-modeling (Blanning Structured modeling (Geoffrion); meta-modeling (Blanning

and Basu); graph grammars (Jones); logic modeling and Basu); graph grammars (Jones); logic modeling (Bhargava and Kimbrough); object-oriented (Dolk)(Bhargava and Kimbrough); object-oriented (Dolk)

– Modeling languages (AMPL: Fourer and Gay)Modeling languages (AMPL: Fourer and Gay)• Model integrationModel integration

– Dimensional analysis (Bradley and Clemence)Dimensional analysis (Bradley and Clemence)– Semantic consistency (Bhargava and Kimbrough)Semantic consistency (Bhargava and Kimbrough)– Relational data systems for managing dataRelational data systems for managing data– Model composition (Dolk and Kottemann; Geoffrion)Model composition (Dolk and Kottemann; Geoffrion)

LIMITATIONS OF 1LIMITATIONS OF 1stst GENERATION MODEL GENERATION MODEL

MANAGEMENTMANAGEMENT• Decision-makers are, on average, “model averse”Decision-makers are, on average, “model averse”• Never really a market for the MMSNever really a market for the MMS

– Cross-paradigm myopia in the OR/MS communityCross-paradigm myopia in the OR/MS community– ““The spreadsheet The spreadsheet isis the MMS” the MMS”– Result: a fully functional MMS was never implementedResult: a fully functional MMS was never implemented

• Data more important than modelsData more important than models• No comprehensive, integrating theory (as in No comprehensive, integrating theory (as in

relational data world)relational data world)• Internet shifted attention from static Internet shifted attention from static

representations to dynamic, distributed resourcesrepresentations to dynamic, distributed resources

MODEL MANAGEMENT AND MODEL MANAGEMENT AND THE INTERNETTHE INTERNET

• Internet shifted the focus on many Internet shifted the focus on many different levels:different levels:– from stand-alone machine centric (static) to from stand-alone machine centric (static) to

distributed network-centric (dynamic)distributed network-centric (dynamic)– from top down to bottom upfrom top down to bottom up– from MMS as single monolithic system to MMS from MMS as single monolithic system to MMS

as dynamic, configurable S/W componentsas dynamic, configurable S/W components– from S/W as commodity to S/W as servicefrom S/W as commodity to S/W as service– from individual problem solving to from individual problem solving to

collaborative problem solvingcollaborative problem solving• AMEAME

ELEMENTS OF ELEMENTS OF NEXT GENERATIONNEXT GENERATION

MODEL MANAGEMENT MODEL MANAGEMENT There is still a need for model management, but this seems to goThere is still a need for model management, but this seems to golargely unrecognized.largely unrecognized. • Model management as an exemplar of knowledge management rather Model management as an exemplar of knowledge management rather

than an extension of data managementthan an extension of data management– Models recast in the context of Models recast in the context of Knowledge Knowledge and and Knowledge Flow Knowledge Flow enablersenablers, or, or– Models in the context of the Pentagram Creative Space (Involvement, Models in the context of the Pentagram Creative Space (Involvement,

Imagination, Intervention, Integration, Intelligence) (Nakamori)Imagination, Intervention, Integration, Intelligence) (Nakamori)– ““Model dynamics” (?) rather than “model management”Model dynamics” (?) rather than “model management”– Decision as a process (Decision as a process (decision supply chain) decision supply chain) rather than a point estimaterather than a point estimate– Collaborative decision-making vs. individual decision-makingCollaborative decision-making vs. individual decision-making

• Shift from analytical modeling to computational modeling and virtual Shift from analytical modeling to computational modeling and virtual environments environments – Concept of complexity has changed radicallyConcept of complexity has changed radically– Evolutionary biology has replaced physics as the scientific paradigm of interest Evolutionary biology has replaced physics as the scientific paradigm of interest

in the social sciencesin the social sciences– Ascendancy of network “science” and agent technologyAscendancy of network “science” and agent technology– Model integration in one form as a Model integration in one form as a Society of SimulationsSociety of Simulations

KNOWLEDGE FLOWS INKNOWLEDGE FLOWS INTHE MODELING LIFECYCLETHE MODELING LIFECYCLE

Problem Identification

ModelFormulation

Model VersioningAnd Security

ModelSolution

ModelValidation

ModelImplementation

ModelMaintenance

ModelInterpretation

COMPUTATIONAL SCIENCECOMPUTATIONAL SCIENCE• Computational science involves using computers to study scientific Computational science involves using computers to study scientific

problems and complements the areas of theory and experimentation in problems and complements the areas of theory and experimentation in traditional scientific investigation. traditional scientific investigation.

• Computational science seeks to gain understanding of science principally Computational science seeks to gain understanding of science principally through the use and analysis of computational models, often on high through the use and analysis of computational models, often on high performance computers. performance computers.

• Computational modeling and simulation is being accepted as a third Computational modeling and simulation is being accepted as a third methodology in engineering and scientific research that fills a gap between methodology in engineering and scientific research that fills a gap between physical experiments and analytical approaches. physical experiments and analytical approaches.

• Experiments traditionally performed in a laboratory, wind tunnel, or the Experiments traditionally performed in a laboratory, wind tunnel, or the field are being augmented or replaced by computational experimentation field are being augmented or replaced by computational experimentation (simulations).(simulations).

• These simulations provide both qualitative and quantitative insights into These simulations provide both qualitative and quantitative insights into many phenomena that are too complex to be dealt with by analytical many phenomena that are too complex to be dealt with by analytical methods (e.g., organizational dynamics) or too expensive or dangerous to methods (e.g., organizational dynamics) or too expensive or dangerous to study by experiments (e.g., bioterrorist attacks, nuclear repository study by experiments (e.g., bioterrorist attacks, nuclear repository integrity). integrity).

ASPECTS OF ASPECTS OF COMPUTATIONAL COMPUTATIONAL

MODELINGMODELING• Procedural as separate from equational or axiomaticProcedural as separate from equational or axiomatic

– E.g., cellular automata, Monte Carlo simulations for solving E.g., cellular automata, Monte Carlo simulations for solving systems of PDEs numericallysystems of PDEs numerically

• Constructivist, or very nearly so, in natureConstructivist, or very nearly so, in nature– ““if you can’t build it, you don’t understand it” (Langton)if you can’t build it, you don’t understand it” (Langton)– Artifact-building vs. theory-buildingArtifact-building vs. theory-building

• Emergent behavior vs. hierarchical decomposition & Emergent behavior vs. hierarchical decomposition & recompositionrecomposition

• Types of modelsTypes of models– ““what is”; descriptive (ex: discrete event simulation)what is”; descriptive (ex: discrete event simulation)– ““what should be”; prescriptive (ex: optimization)what should be”; prescriptive (ex: optimization)– ““what will be”; predictive (ex: econometric forecasting)what will be”; predictive (ex: econometric forecasting)– ““what could be”; constructive (ex: artificial life)what could be”; constructive (ex: artificial life)

EXAMPLES OF EXAMPLES OF COMPUTATIONAL MODELING COMPUTATIONAL MODELING

FOR SOME REFERENCE FOR SOME REFERENCE DISCIPLINESDISCIPLINES

• Biology: DNA and the genome; artificial Biology: DNA and the genome; artificial life [Keller 2002]life [Keller 2002]

• Physics: numerical analysis of systems Physics: numerical analysis of systems of PDEsof PDEs

• Mathematics: Mathematica [Wolfram Mathematics: Mathematica [Wolfram 2002]2002]

• Finance: options pricingFinance: options pricing

COMPUTATIONAL MODELING in COMPUTATIONAL MODELING in the INFORMATION and SOCIAL the INFORMATION and SOCIAL

SCIENCESSCIENCES

• Computational models of human behaviorComputational models of human behavior– How do we construct agents?How do we construct agents?– Computational models of cognition [Edelman 1987]Computational models of cognition [Edelman 1987]– Experimental economicsExperimental economics– Economic decision-making under uncertainty (Tversky & Economic decision-making under uncertainty (Tversky &

Kahneman)Kahneman)• Organization science: Computational Organization science: Computational

organizations [Prietula & Carley 1994; Levitt 2004]organizations [Prietula & Carley 1994; Levitt 2004]• Economics: evolutionary economics [Nelson and Economics: evolutionary economics [Nelson and

Winter 2002]; synthetic economies [Epstein & Winter 2002]; synthetic economies [Epstein & Axtell 1996]; Axtell 1996];

• Network “science”: [Barabasi 2002]; social Network “science”: [Barabasi 2002]; social network analysis [Wassermann 1994]network analysis [Wassermann 1994]

COMPUTATIONAL COMPUTATIONAL EXPERIMENTATIONEXPERIMENTATION

• Computational experimentation as an alternative or augmentation to Computational experimentation as an alternative or augmentation to analytical / laboratory and field experimentationanalytical / laboratory and field experimentation

from [Nissen and Buettner from [Nissen and Buettner 2004]2004]

Theory

ComputationalExperimentation

Experiment-Live: Laboratory

and Field

ComputationalModeling

Virtual Environments linked via Network interfaces with shared semantics

Design Analysis

Design

COMPUTATIONAL MODELING AND VIRTUAL ENVIRONMENTS

Hypothesis Generation

Analysis

Design

Analysis, Confirmation/Refutation

Design

MODEL DYNAMICS AND MODEL DYNAMICS AND VIRTUAL ENVIRONMENTSVIRTUAL ENVIRONMENTS

• LEADLEAD (Linked Environments for Atmospheric (Linked Environments for Atmospheric Discovery)Discovery)– Collaboration among meteorologists, computer scientists, Collaboration among meteorologists, computer scientists,

educational expertseducational experts– Objective: Objective:

• Respond to weather phenomena in real timeRespond to weather phenomena in real time• Execute multi-model simulations of weather forecasts Execute multi-model simulations of weather forecasts

distributed on the Griddistributed on the Grid• Adapt computing resources dynamicallyAdapt computing resources dynamically

– Services:Services:• Workflow system: dynamic control of experimentsWorkflow system: dynamic control of experiments• Metadata catalog for managing experimental resultsMetadata catalog for managing experimental results• Notification system as a communications layerNotification system as a communications layer

SOCIETY of SIMULATIONS SOCIETY of SIMULATIONS APPROACH TO LINKING APPROACH TO LINKING

VIRTUAL ENVIRONMENTSVIRTUAL ENVIRONMENTS• Problem:Problem: How do you link local virtual environments How do you link local virtual environments

(models) developed with local semantics into a global (models) developed with local semantics into a global virtual environment (integrated model) with a virtual environment (integrated model) with a common semantics? (This is the problem of the common semantics? (This is the problem of the Semantic web; also, to a large degree, the Semantic web; also, to a large degree, the aggregation problem)aggregation problem)

• A Society of Simulations is analogous to a society of A Society of Simulations is analogous to a society of people, as both are loosely coupled constructs in people, as both are loosely coupled constructs in which independent individuals contribute toward a which independent individuals contribute toward a single societal identity. A society is an organized single societal identity. A society is an organized group of individuals who associate for common group of individuals who associate for common purposes. purposes.

• Likewise, autonomous simulations in a Society of Likewise, autonomous simulations in a Society of Simulations work together to achieve the common Simulations work together to achieve the common goal of modeling the system.goal of modeling the system.

SOCIETY of SIMULATIONS SOCIETY of SIMULATIONS COMPONENTSCOMPONENTS

• Members: Stand-alone simulations or Members: Stand-alone simulations or models (ABS, DES, SD, OR/MS, etc), built models (ABS, DES, SD, OR/MS, etc), built specifically for a Society, other specifically for a Society, other components such as visualizations and components such as visualizations and user interfaces user interfaces

• Shared Reality: stores the shared aspects Shared Reality: stores the shared aspects of a Member’s model(s)of a Member’s model(s)

• Liaisons: links Members with Shared Liaisons: links Members with Shared RealityReality

MORE ELEMENTS OF MORE ELEMENTS OF NEXT GENERATIONNEXT GENERATION

MODEL MANAGEMENT MODEL MANAGEMENT• Solver environmentsSolver environments

– Combining information systems and model development Combining information systems and model development techniquestechniques

– Meta-heuristic environmentsMeta-heuristic environments– Grid computingGrid computing– Network scienceNetwork science

• Data (structured + Data (structured + semi- / unstructuredsemi- / unstructured))– Search engine technologySearch engine technology– Advanced data/text/image miningAdvanced data/text/image mining– Semantic WebSemantic Web– Dynamically configurable and executable models a la Google type Dynamically configurable and executable models a la Google type

interfacesinterfaces• Application areasApplication areas

– Supply chain managementSupply chain management– Services science (?), management and engineering: Web services, Services science (?), management and engineering: Web services,

service-oriented architecture as IMEservice-oriented architecture as IME– Computational economies, societies, organizationsComputational economies, societies, organizations– Network science (social network analysis)Network science (social network analysis)

APPLICATION AREA: APPLICATION AREA: SEMANTIC WEBSEMANTIC WEB

• The Semantic Web is a web of data. There is lots of data we all use every The Semantic Web is a web of data. There is lots of data we all use every day, and its not part of the web. I can see my bank statements on the day, and its not part of the web. I can see my bank statements on the web, and my photographs, and I can see my appointments in a calendar. web, and my photographs, and I can see my appointments in a calendar. But can I see my photos in a calendar to see what I was doing when I took But can I see my photos in a calendar to see what I was doing when I took them? Can I see bank statement lines in a calendar?them? Can I see bank statement lines in a calendar?

• Why not? Because we don't have a web of data. Because data is Why not? Because we don't have a web of data. Because data is controlled by applications, and each application keeps it to itself.controlled by applications, and each application keeps it to itself.

• The Semantic Web is about two things. It is about common formats for The Semantic Web is about two things. It is about common formats for interchange of data, where on the original Web we only had interchange interchange of data, where on the original Web we only had interchange of documents. Also it is about language for recording how the data of documents. Also it is about language for recording how the data relates to real world objects. That allows a person, or a machine, to start relates to real world objects. That allows a person, or a machine, to start off in one database, and then move through an unending set of off in one database, and then move through an unending set of databases which are connected not by wires but by being about the same databases which are connected not by wires but by being about the same thing.thing. ( ( http://www.w3.org/2001/sw/http://www.w3.org/2001/sw/ ) )( ( http://www.scientificamerican.com/article.cfm?articleID=00048144-10D2-1C70-84http://www.scientificamerican.com/article.cfm?articleID=00048144-10D2-1C70-84A9809EC588EF21&pageNumber=1&catID=2A9809EC588EF21&pageNumber=1&catID=2 ) )

• Model Dynamics counterpart: Composing services to satisfy a user Model Dynamics counterpart: Composing services to satisfy a user request is the same problem as composing models to solve a particular request is the same problem as composing models to solve a particular application.application.

• Research areas: Ontologies, semantic resolution, dimensional Research areas: Ontologies, semantic resolution, dimensional consistency, logical vs physical integrationconsistency, logical vs physical integration

APPLICATION AREA:APPLICATION AREA:SERVICES MANAGEMENT SERVICES MANAGEMENT

AND ENGINEERINGAND ENGINEERING• ““Services sciences, Management and Services sciences, Management and

Engineering hopes to bring together ongoing Engineering hopes to bring together ongoing work in computer science, operations work in computer science, operations research, industrial engineering, business research, industrial engineering, business strategy, management sciences, social and strategy, management sciences, social and cognitive sciences, and legal sciences to cognitive sciences, and legal sciences to develop the skills required in a services-led develop the skills required in a services-led economy.”economy.”

http://www.research.ibm.com/ssme/http://www.research.ibm.com/ssme/

APPLICATION AREA:APPLICATION AREA:SERVICES MANAGEMENT SERVICES MANAGEMENT

AND ENGINEERINGAND ENGINEERING• ““The science comes in through modeling. You model The science comes in through modeling. You model

kernels of a work practice to gain insight and for the kernels of a work practice to gain insight and for the purposes of automation” Richard Newton, Dean of the purposes of automation” Richard Newton, Dean of the College of Engineering at the University of California, College of Engineering at the University of California, Berkeley. Berkeley.

• Modeling, simulation, abstraction, measurement and Modeling, simulation, abstraction, measurement and metrics, and process design and analysis will emerge as metrics, and process design and analysis will emerge as core disciplines of science-based servicescore disciplines of science-based services

• Equipped with the right tools (e.g. dynamically Equipped with the right tools (e.g. dynamically reconfigurable architectures for “on demand” computing), reconfigurable architectures for “on demand” computing), nonprogrammers willnonprogrammers willbe able to design, model, and simulate business processes. be able to design, model, and simulate business processes.

SOME RESEARCH AREAS SOME RESEARCH AREAS FOR NGMMIFOR NGMMI

• Computational models of human behaviorComputational models of human behavior– Experimental economics, cognitive science, psychology, decision scienceExperimental economics, cognitive science, psychology, decision science– Agent representationsAgent representations

• OntologiesOntologies– Model assumptions, structural representations, dynamic representations, Model assumptions, structural representations, dynamic representations,

agent behavioragent behavior• Model integrationModel integration

– How to integrate inter-paradigm models such as ABS, DES, Optimization, How to integrate inter-paradigm models such as ABS, DES, Optimization, Forecasting, Soft vs. Crisp, Quantitative vs. Qualitative, etc., etc. models? Forecasting, Soft vs. Crisp, Quantitative vs. Qualitative, etc., etc. models? How do you represent these models and how do you merge them How do you represent these models and how do you merge them semantically? (Ex: artificial labor market)semantically? (Ex: artificial labor market)

– How to integrate intra-paradigm models? E.g., how do you integrate an ABS How to integrate intra-paradigm models? E.g., how do you integrate an ABS whose agents are people with an ABS whose agents are strategies?whose agents are people with an ABS whose agents are strategies?

– Ontology integration (meta-ontology)Ontology integration (meta-ontology)• Model validation, esp. for emergent (“what could be”) modelsModel validation, esp. for emergent (“what could be”) models

– What is (are) the role(s) of “what could be” models in scientific inquiry?What is (are) the role(s) of “what could be” models in scientific inquiry?• Measurement of knowledge flows resulting from analytical/computational modelsMeasurement of knowledge flows resulting from analytical/computational models

– How useful are models, really? How useful are models, really? – ““Good” vs. “Bad” models and their effects upon the Knowledge BaseGood” vs. “Bad” models and their effects upon the Knowledge Base

Backup SlidesBackup Slides

SOME REFERENCESSOME REFERENCES• COMPUTATIONAL EXPERIMENTATIONCOMPUTATIONAL EXPERIMENTATION

– Nissen, M. and Buettner, R. Computational experimentation with the Virtual Nissen, M. and Buettner, R. Computational experimentation with the Virtual Design Team: Bridging the chasm between laboratory and field research in C2. Design Team: Bridging the chasm between laboratory and field research in C2. " Proceedings Command and Control Research and Technology Symposium" Proceedings Command and Control Research and Technology Symposium, , San Diego, CA, 2004.San Diego, CA, 2004.

– Kevrekidis, I. Equation-Free Modeling for Complex Systems.Kevrekidis, I. Equation-Free Modeling for Complex Systems. Frontiers of Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2004 NAE Engineering: Reports on Leading-Edge Engineering from the 2004 NAE Symposium on Frontiers of EngineeringSymposium on Frontiers of Engineering,, 69-76.69-76.

• COMPUTATIONAL EXPLANATIONCOMPUTATIONAL EXPLANATION– Keller, E.F. Keller, E.F. Making Sense of Life: Explaining Biological Development with Making Sense of Life: Explaining Biological Development with

Models, Metaphors, and Machines. Models, Metaphors, and Machines. Harvard University Press, Cambridge, MA, Harvard University Press, Cambridge, MA, 2002.2002.

– Kimbrough, S. Computational Modeling and Explanation: Opportunities for the Kimbrough, S. Computational Modeling and Explanation: Opportunities for the Information and Management Sciences. Information and Management Sciences. Computational Modeling and Problem Computational Modeling and Problem Solving in the Networked WorldSolving in the Networked World, Hemant K. Bhargava and Nong Ye, eds., , Hemant K. Bhargava and Nong Ye, eds., Kluwer, Boston, MA, 31-57, 2003.Kluwer, Boston, MA, 31-57, 2003.

• COMPUTATIONAL ORGANIZATIONSCOMPUTATIONAL ORGANIZATIONS– Carley, K. M. & Prietula, M. J. (Eds.), 1994, Carley, K. M. & Prietula, M. J. (Eds.), 1994, Computational Organization Computational Organization

TheoryTheory, Hillsdale, NJ: Lawrence Erlbaum Associates. , Hillsdale, NJ: Lawrence Erlbaum Associates. – Levitt, R. E., (2004). Computational Modeling of Organizations Comes of Age. Levitt, R. E., (2004). Computational Modeling of Organizations Comes of Age.

Journal of Computational & Mathematical Organization Theory, 10(2); Journal of Computational & Mathematical Organization Theory, 10(2); 127-145, 127-145, July 2004.July 2004.

REFERENCES (cont’d)REFERENCES (cont’d)

• EVOLUTIONARY ECONOMICS AND SYNTHETIC ECONOMIESEVOLUTIONARY ECONOMICS AND SYNTHETIC ECONOMIES– Chaturvedi, A., Mehta, S., Dolk, D., Ayer, R. Agent-based simulation for Chaturvedi, A., Mehta, S., Dolk, D., Ayer, R. Agent-based simulation for

computational experimentation: developing an artificial labor market. computational experimentation: developing an artificial labor market. European Journal of Operations Research 166:3,European Journal of Operations Research 166:3, 694-716, 2005. 694-716, 2005.

– Epstein, J. and Axtell, R. Epstein, J. and Axtell, R. Growing Artificial Societies: Social Science Growing Artificial Societies: Social Science from the Bottom Upfrom the Bottom Up. The Brookings Institution and the MIT Press, . The Brookings Institution and the MIT Press, Washington D.C. and Cambridge, MA, 1996. Washington D.C. and Cambridge, MA, 1996.

– Nelson, R. and Winter, S. Nelson, R. and Winter, S. An Evolutionary Theory of Economic Change.An Evolutionary Theory of Economic Change. The Belknap Press of Harvard University Press, Cambridge MA, 1982. The Belknap Press of Harvard University Press, Cambridge MA, 1982.

• MODEL MANAGEMENTMODEL MANAGEMENT– Basu, A. and Blanning, R. Model integration using metagraphs. Basu, A. and Blanning, R. Model integration using metagraphs.

Information Systems Research, 5:3; Information Systems Research, 5:3; 195-218, 1994.195-218, 1994.– Bhargava, H. and Kimbrough, S. Model management: An embedded Bhargava, H. and Kimbrough, S. Model management: An embedded

languages approach. languages approach. Decision Support Systems, 10;Decision Support Systems, 10; 277-299, 1993. 277-299, 1993.– Dolk, D. Model integration in the data warehouse era. Dolk, D. Model integration in the data warehouse era. European European

Journal of Operational Research, Journal of Operational Research, April 2000.April 2000.– Geoffrion, A.M. An introduction to structured modeling. Geoffrion, A.M. An introduction to structured modeling. Management Management

Science, 33: 5, Science, 33: 5, 547-588, May 1987.547-588, May 1987.– Jones, C. An introduction to graph based modeling systems, Part I: Jones, C. An introduction to graph based modeling systems, Part I:

Overview. Overview. ORSA Journal on ComputingORSA Journal on Computing, 136-151, 1990., 136-151, 1990.

REFERENCES (cont’d)REFERENCES (cont’d)

• NETWORK SCIENCENETWORK SCIENCE– Barabasi, A-L. Barabasi, A-L. Linked: How Everything is Connected to Everything Else Linked: How Everything is Connected to Everything Else

and What It Means for Business, Science, and Everyday Life. and What It Means for Business, Science, and Everyday Life. Plume Plume Press, 2003.Press, 2003.

– J.C. Doyle, D. Alderson, L. Li, S. Low, M. Roughan, S. Shalunov, R. J.C. Doyle, D. Alderson, L. Li, S. Low, M. Roughan, S. Shalunov, R. Tanaka, and W. Willinger. The "robust yet fragile" nature of the Tanaka, and W. Willinger. The "robust yet fragile" nature of the Internet. Internet. Proc. Nat. Acad. Sci. USA. Proc. Nat. Acad. Sci. USA. October 4, 2005.October 4, 2005.

• SOCIAL NETWORK ANALYSISSOCIAL NETWORK ANALYSIS– Wasserman, S. and Faust, K. (1994) Wasserman, S. and Faust, K. (1994) Social Network Analysis: Methods Social Network Analysis: Methods

and Applicationsand Applications. Cambridge: Cambridge University Press.. Cambridge: Cambridge University Press. – Krackhardt, K. and Hanson, J. Informal networks: The company behind Krackhardt, K. and Hanson, J. Informal networks: The company behind

the chart. Harvard Business Review, 103-111, July-August, 1993.the chart. Harvard Business Review, 103-111, July-August, 1993.

• KNOWLEDGE MANAGEMENT AND DYNAMICSKNOWLEDGE MANAGEMENT AND DYNAMICS– Wierzbicki, A. and Nakamori, Y. (2006) Wierzbicki, A. and Nakamori, Y. (2006) Creative Space: Models of Creative Space: Models of

Creative Processes for the Knowledge Civilization Age. Creative Processes for the Knowledge Civilization Age. Springer Press.Springer Press.– Nissen, M. (2006) Nissen, M. (2006) Harnessing Knowledge Dynamics: Principled Harnessing Knowledge Dynamics: Principled

Organizational Knowing. Organizational Knowing. IRM Press.IRM Press.

SOCIETY of SIMULATIONS: SOCIETY of SIMULATIONS: MEMBER COMPONENTMEMBER COMPONENT

• Each simulation, or model, in a Society is an Each simulation, or model, in a Society is an autonomously managed Member which autonomously managed Member which cooperates with other Members to reach its cooperates with other Members to reach its personal goals. personal goals.

• In the process of meeting its personal goals, In the process of meeting its personal goals, a Member contributes to societal goals. a Member contributes to societal goals.

• Satisfaction of societal goals emerges as all Satisfaction of societal goals emerges as all Members progress towards their personal Members progress towards their personal goals.goals.

SOCIETY of SIMULATIONS: SOCIETY of SIMULATIONS: MEMBER COMPONENTMEMBER COMPONENT

• Members: Members: – Inputs/Outputs: Inputs/Outputs:

• Syntax: data structure and typeSyntax: data structure and type• Granularity: spatial and temporal (can differ widely across different Granularity: spatial and temporal (can differ widely across different

simulationssimulations• Semantics: the meaning of an input/output (e.g., A door in a Semantics: the meaning of an input/output (e.g., A door in a

building layout means a wooden obstacle to a FireSim and a building layout means a wooden obstacle to a FireSim and a removable blockage on an exit route to a HumanSim)removable blockage on an exit route to a HumanSim)

SOCIETY of SIMULATIONS: SOCIETY of SIMULATIONS: SHARED REALITYSHARED REALITY

• Shared Reality:Shared Reality:– Shared aspects of a Member’s modelsShared aspects of a Member’s models– Does not manage how the Members operate Does not manage how the Members operate – Persistent information spacePersistent information space– The intelligence for transforming information within Shared The intelligence for transforming information within Shared

Reality into a form a consumer can digest and for Reality into a form a consumer can digest and for synchronizing a consumer with produced data is pushed from synchronizing a consumer with produced data is pushed from the data exchange mechanism of Shared Reality onto the the data exchange mechanism of Shared Reality onto the linkages (Liaisons) that connect the Members to Shared linkages (Liaisons) that connect the Members to Shared Reality. Reality.

– Shared Reality is lightweight, in the sense that overheads Shared Reality is lightweight, in the sense that overheads increase less than linearly as the number of Members or the increase less than linearly as the number of Members or the amount of data being exchanged increases. amount of data being exchanged increases.

– Decouples the producers and consumers of data Decouples the producers and consumers of data • Member’s design is separated from the data exchange mechanism. Member’s design is separated from the data exchange mechanism. • Extensions to a Member’s design do not require changes to the Extensions to a Member’s design do not require changes to the

design of Shared Reality. design of Shared Reality.

SOCIETY of SIMULATIONS SOCIETY of SIMULATIONS APPROACHAPPROACH

• Liaisons:Liaisons:– Each Member in a Society accesses Shared Reality through a Each Member in a Society accesses Shared Reality through a

Member-specific LiaisonMember-specific Liaison– Liaison consists of the intelligence needed to interact with and Liaison consists of the intelligence needed to interact with and

control a Member and to interact with the rest of the Society. control a Member and to interact with the rest of the Society. – Liaison is configured to use Member-specific mechanisms—Liaison is configured to use Member-specific mechanisms—

initializations, inputs, outputs, and control mechanisms. initializations, inputs, outputs, and control mechanisms. – Same Member can be used in different Societies and be Same Member can be used in different Societies and be

continuously developed without being forced to address Society-continuously developed without being forced to address Society-specific characteristics, enabling reuse and distributed specific characteristics, enabling reuse and distributed development.development.

– Liaison Tasks:Liaison Tasks:• Synchronizes the Member with data the Member depends on Synchronizes the Member with data the Member depends on • Starts, stops, restarts, and checkpoints a Member. Starts, stops, restarts, and checkpoints a Member. • Gathers data from Shared Reality, transforms its syntax, converts its Gathers data from Shared Reality, transforms its syntax, converts its

granularity, and translates its semantics.granularity, and translates its semantics.• Places the Member’s outputs into Shared Reality coupled with Places the Member’s outputs into Shared Reality coupled with

semantic information describing the syntax, granularity, and semantic information describing the syntax, granularity, and semantics of the data.semantics of the data.

EXAMPLE: EVACUATION EXAMPLE: EVACUATION SOCIETYSOCIETY

BENEFITS of SoS BENEFITS of SoS APPROACHAPPROACH• Enables distributed developmentEnables distributed development• Heterogeneity is supported by allowing independent Heterogeneity is supported by allowing independent

development of Member designs development of Member designs • Autonomous management is enabled by linking Members to Autonomous management is enabled by linking Members to

information instead of to other Members information instead of to other Members • Avoids publisher-subscriber dependenceAvoids publisher-subscriber dependence• Society of Simulations approach allows simulations to Society of Simulations approach allows simulations to

cooperate, yet remain autonomous, an inherently modular cooperate, yet remain autonomous, an inherently modular and scalable approach for linking heterogeneous and scalable approach for linking heterogeneous simulations.simulations.

• Example: Urban Resolve 2015 (15 simulations, 6 of which Example: Urban Resolve 2015 (15 simulations, 6 of which use SoS; 2000 players; 2 weeks duration)use SoS; 2000 players; 2 weeks duration)

• SoS works primarily at the syntactic level; Next step: SoS works primarily at the syntactic level; Next step: extend to the semantic level (Semantic Web)extend to the semantic level (Semantic Web)

STRUCTURED MODELING STRUCTURED MODELING and the 21and the 21stst CENTURY CENTURY

• UML, ERD still do not support decision UML, ERD still do not support decision models and OR/MS applications models and OR/MS applications

• OLAP Extension: SM and OLAPOLAP Extension: SM and OLAP• Model Standardization: SM and XMLModel Standardization: SM and XML• SM and OntologySM and Ontology• SM and KM: Wikipedia counterpart for modelsSM and KM: Wikipedia counterpart for models• Dynamic SM Dynamic SM • SM and Computational Modeling: SM and Computational Modeling:

opportunities in the life sciences?opportunities in the life sciences?