NEXT GENERATION MODEL MANAGEMENT AND INTEGRATION

1. NEXT GENERATION MODEL MANAGEMENTAND INTEGRATION Prof. Daniel Dolk CSM Workshop August 2006

2. OVERVIEW • Retrospective of model management • Elements of NGMMI • Emergence of computational modeling and computational experimentation in scientific inquiry • Virtual environments in the form of Society of Simulations “It is possible to view every process that occurs in nature or elsewhere as a computation” – Stephen Wolfram “Information is static…computation is dynamic” – Rudy Rucker

3. SCIENTIFIC METHOD, 15TH-20TH CENTURIES Theory Experiment Design Analysis and Explanation

4. SCIENTIFIC METHOD, 1950 - PRESENT Modeling & Simulation Analysis & Explanation Analysis Design Design Theory Design Experiment Validation

5. MODEL MANAGEMENT REDUX • “The process of representing, solving, analyzing and integrating analytical models (primarily in the information and management sciences (OR/MS)) in computer executable form” -- Dolk • Initially conceived as a modeling counterpart to data management • Models as a corporate resource • Models, like data, need to be systematically managed • Rich vein of OR/MS-based models and solvers • Model management system • Part of the troika of DSS • Data, models, dialogue (later, knowledge) • Models as the lynchpin for decision-making (Simon et al)

6. MODEL MANAGEMENT REDUX:MODEL MANAGEMENT SYSTEM “If DBMS, why not MMS?” Desirable Requirements/Features of an MMS: • Support entire modeling life cycle • Uniform representation of models • Modeling languages • Portfolio of cross-paradigm OR/MS models • Library of solvers • Model integration • Separation of models and data • Separation of models and solvers • Leverage RDBMS for data manipulation Central issue: Model Representation

7. MODEL MANAGEMENT REDUX:MODEL REPRESENTATION Theoretical driver: Is there a way to represent models that is comparable in power to the relational theory representation of data? • Relational (Blanning) • Object-oriented (Dolk) • Structured modeling (Geoffrion) • Logic modeling (Bhargava, Kimbrough) • Graph grammars (C. Jones) • Metagraphs (Blanning, Basu)

8. FEEDMIX MODEL SM GENUS GRAPH NUTR MATERIAL NUTR_ MATERIAL MIN Q UCOST ANALYSIS TOTCOST T:NLEVEL NLEVEL

9. SM ELEMENTAL DETAIL TABLES FOR FEEDMIX MODEL

10. STRUCTURED MODELING CONTRIBUTIONS • Contributions • Formal semantic ontology for models • Math models as conceptual models • Full language (SML) and implementation (FW/SM) • Model reuse and integration • Multiple modes of model representation

11. LIMITATIONS OF STRUCTURED MODELING “Why isn’t structured modeling (or equivalent) used as a matter of course in OR/MS modeling endeavors?” • Endogenous factors: • No graph-driven implementation • Static vs. dynamic representations • Complexity of indexing semantics • Exogenous factors: • Math as legal tender: OR analysts are overwhelmingly mathematicians • Overhead of conceptual modeling: Even database analysts resist conceptual modeling • Models don’t command the same respect as data • UML has become the lingua franca of conceptual modeling • Internet and distributed computing • (“It can be done” & “It should be done”) ~=> (“It will be done”)

12. CONTRIBUTIONS OF 1ST GENERATION MODEL MANAGEMENT • Decoupling of models, solvers and data • “run-time” binding of data and solver to model representation • Model representation formalisms • Structured modeling (Geoffrion); meta-modeling (Blanning and Basu); graph grammars (Jones); logic modeling (Bhargava and Kimbrough); object-oriented (Dolk) • Modeling languages (AMPL: Fourer and Gay) • Model integration • Dimensional analysis (Bradley and Clemence) • Semantic consistency (Bhargava and Kimbrough) • Relational data systems for managing data • Model composition (Dolk and Kottemann; Geoffrion)

13. LIMITATIONS OF 1st GENERATION MODEL MANAGEMENT • Decision-makers are, on average, “model averse” • Never really a market for the MMS • Cross-paradigm myopia in the OR/MS community • “The spreadsheet is the MMS” • Result: a fully functional MMS was never implemented • Data more important than models • No comprehensive, integrating theory (as in relational data world) • Internet shifted attention from static representations to dynamic, distributed resources

14. MODEL MANAGEMENT AND THE INTERNET • Internet shifted the focus on many different levels: • from stand-alone machine centric (static) to distributed network-centric (dynamic) • from top down to bottom up • from MMS as single monolithic system to MMS as dynamic, configurable S/W components • from S/W as commodity to S/W as service • from individual problem solving to collaborative problem solving • AME

15. ELEMENTS OF NEXT GENERATION MODEL MANAGEMENT There is still a need for model management, but this seems to go largely unrecognized. • Model management as an exemplar of knowledge management rather than an extension of data management • Models recast in the context of Knowledge and Knowledge Flow enablers, or • Models in the context of the Pentagram Creative Space (Involvement, Imagination, Intervention, Integration, Intelligence) (Nakamori) • “Model dynamics” (?) rather than “model management” • Decision as a process (decision supply chain) rather than a point estimate • Collaborative decision-making vs. individual decision-making • Shift from analytical modeling to computational modeling and virtual environments • Concept of complexity has changed radically • Evolutionary biology has replaced physics as the scientific paradigm of interest in the social sciences • Ascendancy of network “science” and agent technology • Model integration in one form as a Society of Simulations

16. KNOWLEDGE FLOWS INTHE MODELING LIFECYCLE Problem Identification Model Versioning And Security Model Formulation Model Implementation Model Maintenance Model Solution Model Validation Model Interpretation

17. COMPUTATIONAL SCIENCE • Computational science involves using computers to study scientific problems and complements the areas of theory and experimentation in traditional scientific investigation. • Computational science seeks to gain understanding of science principally through the use and analysis of computational models, often on high performance computers. • Computational modeling and simulation is being accepted as a third methodology in engineering and scientific research that fills a gap between physical experiments and analytical approaches. • Experiments traditionally performed in a laboratory, wind tunnel, or the field are being augmented or replaced by computational experimentation (simulations). • These simulations provide both qualitative and quantitative insights into many phenomena that are too complex to be dealt with by analytical methods (e.g., organizational dynamics) or too expensive or dangerous to study by experiments (e.g., bioterrorist attacks, nuclear repository integrity).

18. ASPECTS OF COMPUTATIONAL MODELING • Procedural as separate from equational or axiomatic • E.g., cellular automata, Monte Carlo simulations for solving systems of PDEs numerically • Constructivist, or very nearly so, in nature • “if you can’t build it, you don’t understand it” (Langton) • Artifact-building vs. theory-building • Emergent behavior vs. hierarchical decomposition & recomposition • Types of models • “what is”; descriptive (ex: discrete event simulation) • “what should be”; prescriptive (ex: optimization) • “what will be”; predictive (ex: econometric forecasting) • “what could be”; constructive (ex: artificial life)

19. EXAMPLES OF COMPUTATIONAL MODELING FOR SOME REFERENCE DISCIPLINES • Biology: DNA and the genome; artificial life [Keller 2002] • Physics: numerical analysis of systems of PDEs • Mathematics: Mathematica [Wolfram 2002] • Finance: options pricing

20. COMPUTATIONAL MODELING in the INFORMATION and SOCIAL SCIENCES • Computational models of human behavior • How do we construct agents? • Computational models of cognition [Edelman 1987] • Experimental economics • Economic decision-making under uncertainty (Tversky & Kahneman) • Organization science: Computational organizations [Prietula & Carley 1994; Levitt 2004] • Economics: evolutionary economics [Nelson and Winter 2002]; synthetic economies [Epstein & Axtell 1996]; • Network “science”: [Barabasi 2002]; social network analysis [Wassermann 1994]

21. COMPUTATIONAL EXPERIMENTATION • Computational experimentation as an alternative or augmentation to analytical / laboratory and field experimentation from [Nissen and Buettner 2004]

22. Computational Modeling Virtual Environments linked via Network interfaces with shared semantics Design Analysis Computational Experimentation Hypothesis Generation Analysis Design Design Theory Experiment -Live: Laboratory and Field Design Analysis, Confirmation/Refutation COMPUTATIONAL MODELING AND VIRTUAL ENVIRONMENTS

23. MODEL DYNAMICS AND VIRTUAL ENVIRONMENTS • LEAD(Linked Environments for Atmospheric Discovery) • Collaboration among meteorologists, computer scientists, educational experts • Objective: • Respond to weather phenomena in real time • Execute multi-model simulations of weather forecasts distributed on the Grid • Adapt computing resources dynamically • Services: • Workflow system: dynamic control of experiments • Metadata catalog for managing experimental results • Notification system as a communications layer

24. SOCIETY of SIMULATIONS APPROACH TO LINKING VIRTUAL ENVIRONMENTS • Problem: How do you link local virtual environments (models) developed with local semantics into a global virtual environment (integrated model) with a common semantics? (This is the problem of the Semantic web; also, to a large degree, the aggregation problem) • A Society of Simulations is analogous to a society of people, as both are loosely coupled constructs in which independent individuals contribute toward a single societal identity. A society is an organized group of individuals who associate for common purposes. • Likewise, autonomous simulations in a Society of Simulations work together to achieve the common goal of modeling the system.

25. SOCIETY of SIMULATIONS COMPONENTS • Members: Stand-alone simulations or models (ABS, DES, SD, OR/MS, etc), built specifically for a Society, other components such as visualizations and user interfaces • Shared Reality: stores the shared aspects of a Member’s model(s) • Liaisons: links Members with Shared Reality

26. MORE ELEMENTS OF NEXT GENERATION MODEL MANAGEMENT • Solver environments • Combining information systems and model development techniques • Meta-heuristic environments • Grid computing • Network science • Data (structured + semi- / unstructured) • Search engine technology • Advanced data/text/image mining • Semantic Web • Dynamically configurable and executable models a la Google type interfaces • Application areas • Supply chain management • Services science (?), management and engineering: Web services, service-oriented architecture as IME • Computational economies, societies, organizations • Network science (social network analysis)

27. APPLICATION AREA: SEMANTIC WEB • 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 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 them? Can I see bank statement lines in a calendar? • Why not? Because we don't have a web of data. Because data is controlled by applications, and each application keeps it to itself. • 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 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 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 thing. ( http://www.w3.org/2001/sw/ ) ( http://www.scientificamerican.com/article.cfm?articleID=00048144-10D2-1C70-84A9809EC588EF21&pageNumber=1&catID=2 ) • Model Dynamics counterpart: Composing services to satisfy a user request is the same problem as composing models to solve a particular application. • Research areas: Ontologies, semantic resolution, dimensional consistency, logical vs physical integration

28. APPLICATION AREA:SERVICES MANAGEMENT AND ENGINEERING • “Services sciences, Management and Engineering hopes to bring together ongoing work in computer science, operations research, industrial engineering, business strategy, management sciences, social and cognitive sciences, and legal sciences to develop the skills required in a services-led economy.”http://www.research.ibm.com/ssme/

29. APPLICATION AREA:SERVICES MANAGEMENT AND ENGINEERING • “The science comes in through modeling. You model kernels of a work practice to gain insight and for the purposes of automation” Richard Newton, Dean of the College of Engineering at the University of California, Berkeley. • Modeling, simulation, abstraction, measurement and metrics, and process design and analysis will emerge as core disciplines of science-based services • Equipped with the right tools (e.g. dynamically reconfigurable architectures for “on demand” computing), nonprogrammers willbe able to design, model, and simulate business processes.

30. SOME RESEARCH AREAS FOR NGMMI • Computational models of human behavior • Experimental economics, cognitive science, psychology, decision science • Agent representations • Ontologies • Model assumptions, structural representations, dynamic representations, agent behavior • Model integration • How to integrate inter-paradigm models such as ABS, DES, Optimization, Forecasting, Soft vs. Crisp, Quantitative vs. Qualitative, etc., etc. models? How do you represent these models and how do you merge them semantically? (Ex: artificial labor market) • 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? • Ontology integration (meta-ontology) • Model validation, esp. for emergent (“what could be”) models • What is (are) the role(s) of “what could be” models in scientific inquiry? • Measurement of knowledge flows resulting from analytical/computational models • How useful are models, really? • “Good” vs. “Bad” models and their effects upon the Knowledge Base

31. Backup Slides

32. SOME REFERENCES • COMPUTATIONAL EXPERIMENTATION • Nissen, M. and Buettner, R. Computational experimentation with the Virtual Design Team: Bridging the chasm between laboratory and field research in C2. " Proceedings Command and Control Research and Technology Symposium, San Diego, CA, 2004. • Kevrekidis, I. Equation-Free Modeling for Complex Systems.Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2004 NAE Symposium on Frontiers of Engineering,69-76. • COMPUTATIONAL EXPLANATION • Keller, E.F. Making Sense of Life: Explaining Biological Development with Models, Metaphors, and Machines. Harvard University Press, Cambridge, MA, 2002. • Kimbrough, S. Computational Modeling and Explanation: Opportunities for the Information and Management Sciences. Computational Modeling and Problem Solving in the Networked World, Hemant K. Bhargava and Nong Ye, eds., Kluwer, Boston, MA, 31-57, 2003. • COMPUTATIONAL ORGANIZATIONS • Carley, K. M. & Prietula, M. J. (Eds.), 1994, Computational Organization Theory, Hillsdale, NJ: Lawrence Erlbaum Associates. • Levitt, R. E., (2004). Computational Modeling of Organizations Comes of Age. Journal of Computational & Mathematical Organization Theory, 10(2); 127-145, July 2004.

33. REFERENCES (cont’d) • EVOLUTIONARY ECONOMICS AND SYNTHETIC ECONOMIES • Chaturvedi, A., Mehta, S., Dolk, D., Ayer, R. Agent-based simulation for computational experimentation: developing an artificial labor market. European Journal of Operations Research 166:3, 694-716, 2005. • Epstein, J. and Axtell, R. Growing Artificial Societies: Social Science from the Bottom Up. The Brookings Institution and the MIT Press, Washington D.C. and Cambridge, MA, 1996. • Nelson, R. and Winter, S. An Evolutionary Theory of Economic Change. The Belknap Press of Harvard University Press, Cambridge MA, 1982. • MODEL MANAGEMENT • Basu, A. and Blanning, R. Model integration using metagraphs. Information Systems Research, 5:3; 195-218, 1994. • Bhargava, H. and Kimbrough, S. Model management: An embedded languages approach. Decision Support Systems, 10; 277-299, 1993. • Dolk, D. Model integration in the data warehouse era. European Journal of Operational Research, April 2000. • Geoffrion, A.M. An introduction to structured modeling. Management Science, 33: 5, 547-588, May 1987. • Jones, C. An introduction to graph based modeling systems, Part I: Overview. ORSA Journal on Computing, 136-151, 1990.

34. REFERENCES (cont’d) • NETWORK SCIENCE • Barabasi, A-L. Linked: How Everything is Connected to Everything Else and What It Means for Business, Science, and Everyday Life. Plume Press, 2003. • 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 Internet. Proc. Nat. Acad. Sci. USA. October 4, 2005. • SOCIAL NETWORK ANALYSIS • Wasserman, S. and Faust, K. (1994) Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press. • Krackhardt, K. and Hanson, J. Informal networks: The company behind the chart. Harvard Business Review, 103-111, July-August, 1993. • KNOWLEDGE MANAGEMENT AND DYNAMICS • Wierzbicki, A. and Nakamori, Y. (2006) Creative Space: Models of Creative Processes for the Knowledge Civilization Age. Springer Press. • Nissen, M. (2006) Harnessing Knowledge Dynamics: Principled Organizational Knowing. IRM Press.

35. SOCIETY of SIMULATIONS: MEMBER COMPONENT • Each simulation, or model, in a Society is an autonomously managed Member which cooperates with other Members to reach its personal goals. • In the process of meeting its personal goals, a Member contributes to societal goals. • Satisfaction of societal goals emerges as all Members progress towards their personal goals.

36. SOCIETY of SIMULATIONS: MEMBER COMPONENT • Members: • Inputs/Outputs: • Syntax: data structure and type • Granularity: spatial and temporal (can differ widely across different simulations • Semantics: the meaning of an input/output (e.g., A door in a building layout means a wooden obstacle to a FireSim and a removable blockage on an exit route to a HumanSim)

37. SOCIETY of SIMULATIONS: SHARED REALITY • Shared Reality: • Shared aspects of a Member’s models • Does not manage how the Members operate • Persistent information space • The intelligence for transforming information within Shared Reality into a form a consumer can digest and for synchronizing a consumer with produced data is pushed from the data exchange mechanism of Shared Reality onto the linkages (Liaisons) that connect the Members to Shared Reality. • Shared Reality is lightweight, in the sense that overheads increase less than linearly as the number of Members or the amount of data being exchanged increases. • Decouples the producers and consumers of data • Member’s design is separated from the data exchange mechanism. • Extensions to a Member’s design do not require changes to the design of Shared Reality.

38. SOCIETY of SIMULATIONS APPROACH • Liaisons: • Each Member in a Society accesses Shared Reality through a Member-specific Liaison • Liaison consists of the intelligence needed to interact with and control a Member and to interact with the rest of the Society. • Liaison is configured to use Member-specific mechanisms—initializations, inputs, outputs, and control mechanisms. • Same Member can be used in different Societies and be continuously developed without being forced to address Society-specific characteristics, enabling reuse and distributed development. • Liaison Tasks: • Synchronizes the Member with data the Member depends on • Starts, stops, restarts, and checkpoints a Member. • Gathers data from Shared Reality, transforms its syntax, converts its granularity, and translates its semantics. • Places the Member’s outputs into Shared Reality coupled with semantic information describing the syntax, granularity, and semantics of the data.

39. EXAMPLE: EVACUATION SOCIETY

40. BENEFITS of SoS APPROACH • Enables distributed development • Heterogeneity is supported by allowing independent development of Member designs • Autonomous management is enabled by linking Members to information instead of to other Members • Avoids publisher-subscriber dependence • Society of Simulations approach allows simulations to cooperate, yet remain autonomous, an inherently modular and scalable approach for linking heterogeneous simulations. • Example: Urban Resolve 2015 (15 simulations, 6 of which use SoS; 2000 players; 2 weeks duration) • SoS works primarily at the syntactic level; Next step: extend to the semantic level (Semantic Web)

41. STRUCTURED MODELING and the 21st CENTURY • UML, ERD still do not support decision models and OR/MS applications • OLAP Extension: SM and OLAP • Model Standardization: SM and XML • SM and Ontology • SM and KM: Wikipedia counterpart for models • Dynamic SM • SM and Computational Modeling: opportunities in the life sciences?