Ontologies in Modeling and Simulation: An Epistemological Perspective

  • Marko Hofmann
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 44)

Abstract

Ontologies are formal specifications of concepts. They represent entities of a specific knowledge domain and the relationships that can hold between the entities. Ontologies are formal descriptions of the so called “body of knowledge” that composes a domain. Regardless of being implicitly or explicitly applied during the modeling, ontologies set the relation between formal signs used in computer simulations and “meaning” as a notion of human minds. Unfortunately, the essence of this relation is disputed, especially in modern epistemology, which deals with the “nature of knowledge” and the methods and limitations of gaining knowledge. Therefore, the chapter introduces first the debate which epistemological view is most appropriate for modeling and simulation. On the basis of this introduction ontologies are scrutinized with respect to their ability to capture knowledge. As a consequence of this analysis two main classes of ontologies for M&S are distinguished: Methodological and referential ontologies. Their values and limits are discussed in detail.

Keywords

Unify Modeling Language Discrete Event Simulation Domain Ontology Specific Knowledge Domain Winter Simulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Ahrweiler, P., Gilbert, N.: Café Nero: the evaluation of social simulation. Journal of Artificial Societies and Social Simulation 8(4) (2005)Google Scholar
  2. Barlas, Y., Carpenter, S.: Philosophical roots of model validation: Two paradgms. System Dynamics Review 6, 148–166 (1990)CrossRefGoogle Scholar
  3. Batty, M., Torrens, P.M.: Modelling and prediction in a complex world. Futures 37, 745–766 (2005)CrossRefGoogle Scholar
  4. Beck, H., Morgan, K., Jung, Y., Grunwald, S., Kwon, H.Y., Wu, J.: Ontology-based simulation in agricultural systems modeling. Agricultural Systems 103(7), 463–477 (2010)CrossRefGoogle Scholar
  5. Bell, D., Mustafee, N., de Cesare, S., Taylor, S.J.E., Lycett, M., Fishwick, P.A.: Ontology engineering for simulation component reuse. International Journal of Enterprise Information Systems 4(4), 47–61 (2008)CrossRefGoogle Scholar
  6. Benjamin, P., Menzel, C., Mayer, R.J.: Towards a method for acquiring CIM ontologies. International Journal of Computer Integrated Manufacturing 8(3), 225–234 (1995)CrossRefGoogle Scholar
  7. Benjamin, P., Patki, M., Mayer, R.: Using ontologies for simulation modeling. In: Perrone, L.F., Wieland, F.P., Liu, J., Lawson, B.G., Nicol, D.M., Fujimoto, R.M. (eds.) Proceedings of the 2006 Winter Simulation Conference, Monterey, pp. 1151–1159. IEEE (2006)Google Scholar
  8. Beven, K.: Towards a coherent philosophy for modelling the environment. Proceedings of the Royal Society London 458, 1–20 (2002)MathSciNetCrossRefGoogle Scholar
  9. Bharathy, G.K., Silverman, B.: Validating agent based social systems models. In: Proceedings of the 2010 Winter Simulation Conference, Piscataway, New Jersey, USA (2010)Google Scholar
  10. Brenner, T., Werker, C.: Policy Advice Derived from Simulation Models. Journal of Artificial Societies and Social Simulation 12(4) (2009)Google Scholar
  11. Byrne, D.: Simulation – A way forward? Sociological Research Online 2(2) (1997)Google Scholar
  12. Carrier, M.: The completeness of scientific theories. Kluwer, Dordrecht (1994)CrossRefGoogle Scholar
  13. Chen, W., Hirschheim, R.: A paradigmatic and methodological examination of information systems research from 1991 to 2001. Information Systems Journal 14(3), 197–235 (2004)CrossRefGoogle Scholar
  14. Christley, S., Xiang, X., Madey, G.: Ontology for agent-based modeling and simulation. In: Macal, C.M., Sallach, D., North, M.J. (eds.) Proceedings of the Agent 2004 Conference on Social Dynamics: Interaction, Reflexivity and Emergence, co-sponsored by Argonne National Laboratory and The University of Chicago, October 7-9 (2004), http://www.agent2005.anl.gov/Agent2004.pdf (accessed March 29, 2011)
  15. Churchman, C.W.: Reliability of Models in the Social Sciences. Interfaces 4(1) (1973)Google Scholar
  16. Cuske, C., Dickopp, T., Seedorf, S.: JOntoRisk: an ontology –based platform for knowledge-based simulation modeling in financial risk management. In: Proceedings of the European Simulation and Modeling Conference, ESM, Porto, Portugal, October 24-26 (2005)Google Scholar
  17. Davis, P.K.: Specifying the Content of Humble Social-Science Models, RAND-RP-1408-1 The Society for Modeling and Simulation International, San Diego, CA (2009), http://www.rand.org/pubs/reprints/RP1408-1.html (accessed March 29, 2011)
  18. Dery, R., Landry, M., Banville, C.: Revisiting the issue of model validation in OR: An epistemological view. European Journal of Operational Research, Special Issue on Model Validation 66, 168–183 (1993)Google Scholar
  19. Dessai, S., Hulme, M., Lempert, R., Pielke, R.: Climate prediction: a limit to adaption? In: Adger, W.N., Lorenzoni, I., O’Brian, K.L. (eds.) Adapting to Climate Change: Thresholds, Values, Governance. Cambridge University Press (2009)Google Scholar
  20. Ezzell, Z., Fishwick, P.A., Lok, B., Pitkin, A., Lampotang, S.: An ontology-enabled user interface for simulation model construction and visualization. Journal of Simulation (February 25, 2011) (advance online publication), doi: 10.1057/jos.2011.5 (accessed March 31, 2011)Google Scholar
  21. Fishwick, P.A., Miller, J.A.: Ontologies for Modeling and Simulation: Issues and Approaches. In: Ingalls, R.G., Rossetti, M.D., Smith, J.S., Peters, B.A. (eds.) Proceedings of the 2004 Winter Simulation Conference, pp. 259–264. IEEE, Washington (2004)Google Scholar
  22. Fensel, D.: Ontologies: A silver bullet for knowledge management and electronic commerce. Springer, Berlin (2004)MATHGoogle Scholar
  23. Feyerabend, P.K.: Against Method: Outline of an Anarchistic Theory of Knowledge. Humanities Press, London (1975); (reprinted, Verso, London, UK 1978)Google Scholar
  24. Fraassen, B.C.: The Scientific Image. Oxford University Press (1980)Google Scholar
  25. Frank, U., Squazzoni, F., Troitzsch, K.G.: EPOS-Epistemological Perspectives on Simulation: An Introduction. In: Squazzoni, F. (ed.) EPOS 2006. LNCS, vol. 5466, pp. 1–11. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  26. Frank, U., Troitzsch, K.G.: Epistemological Perspectives on Simulation. Journal of Artificial Societies and Social Simulation 8(4) (2005)Google Scholar
  27. Frigg, R., Reiss, J.: The philosophy of simulation: hot new issue or same old stew? Synthese 169, 593–613 (2009)MathSciNetCrossRefGoogle Scholar
  28. Garson, D.: Computerized Simulation in the Social Sciences: A Survey and Evaluation. Simulation Gaming 40 (2009)Google Scholar
  29. von Glasersfeld, E.: Radical constructivism: A way of knowing and learning. The Falmer Press (1995)Google Scholar
  30. von Glasersfeld, E.: The construction of knowledge: Contributions to conceptual Semantics. Intersystem Puplications (1997)Google Scholar
  31. Goldspink, C.: Methodological Implications of complex System Approaches to Sociality: Simulation as a foundation of knowledge. Journal of Artificial Societies and Social Simulation 5(1) (2002)Google Scholar
  32. Gruber, T.: A Translation Approach to Portable Ontology Specifications. Knowledge Acquisition - Special Issue: Current Issues in Knowledge Modeling 5(2), 199–220 (1993)Google Scholar
  33. Gruber, T.: Toward Principles for the Design of Ontologies Used for Knowledge Sharing. Int. Journal of Human-Computer Studies 43(5-6), 907–928 (1995)CrossRefGoogle Scholar
  34. Grüne-Yanoff, T.: The explanatory potential of artificial societies. Synthese 169 (2009)Google Scholar
  35. Grüne-Yanoff, T., Weirich, P.: The philosophy and epistemology of simulation: A review. Simulation & Gaming 41(1) (2010)Google Scholar
  36. Guizzardi, G., Wagner, G.: Using the Unified Foundational Ontology (UFO) as a Foundation for General Conceptual Modeling Languages. In: Poli, R. (ed.) Theory and Application of Ontologies. Springer, Heidelberg (2010a)Google Scholar
  37. Guizzardi, G., Wagner, G.: Towards an Ontological Foundation of Discrete Event Simulation. In: Johanson, B., Jain, S., Montoya-Torres, J., Hugan, J., Yücesan, E. (eds.) Proceedings of the 2010 Winter Simulation Conference, pp. 652–664. IEEE (2010b)Google Scholar
  38. Hermann, C.F.: Validation problems in games and simulations with special reference to models of international politics. Behavioral Science 12, 216–231 (1967)CrossRefGoogle Scholar
  39. Hesse, W.: Engineers discovering the “real world” – from model-driven to ontology based software engineering. In: Kaschek, R., Kop, C., Steinberger, C., Fliedl, G. (eds.) Proceedings of the 2nd International United Information Systems Conference on Information Systems and E-Business Technology, vol. 5(2), pp. 136–147. Springer (2008)Google Scholar
  40. Hofmann, M.: Introducing Pragmatics into Verification, Validation and Accreditation. In: Proceedings of the 2nd European Simulation Interoperability Workshop, Harrows. GB (2002a)Google Scholar
  41. Hofmann, M.: Validation: Real world system knowledge, types of validity and credibility levels. In: Proceedings of the 16 European Simulation Multiconference, Darmstadt, DE (2002b)Google Scholar
  42. Hofmann, M.: Challenges of Model Interoperability in Military Simulations. Simulation 80(12), 659–667 (2004)CrossRefGoogle Scholar
  43. Hofmann, M.: On the Complexity of Parameter Calibration in Simulation Models. Journal of Defense Modeling and Simulation 2(4) (2005)Google Scholar
  44. Hofmann, M., Hahn, H.: Is it appropriate to use the objective, rational decision-making framework as a foundation for the modelling of social systems? Information and Security 22 (2007)Google Scholar
  45. Hofmann, M., Palii, J., Mihelcic, G.: Epistemic and normative aspects of ontologies in modeling and simulation. Journal of Simulation 5(3), 135–146 (2011)CrossRefGoogle Scholar
  46. Hemez, F.M.: The myth of science-based predictive modeling. In: Foundations 2004 Workshop for Verification, Validation, and Accreditation in the 21st Century. Arizona State University, Tempe (2004)Google Scholar
  47. Humphreys, P.: The philosophical novelty of computer simulation methods. Synthese 169 (2009)Google Scholar
  48. Islam, A.S., Piasecki, M.: Ontology based web simulation system for hydrodynamic modeling. Simulation Modeling Practice and Theory 16(7), 754–767 (2008)CrossRefGoogle Scholar
  49. Janich, P.: Wozu Ontologie für Informatiker? Objektbezug durch Sprachkritik. In: Bauknecht, K., et al. (eds.) Informatik 2001 - Tagungsband der GI/OCG-Jahrestagung, Bd. II, pp. 765–769 (2001)Google Scholar
  50. Kiko, K., Atkinson, C.: A Detailed Comparison of UML and OWL, University of Mannheim, Germany (2008), http://madoc.bib.uni-mannheim.de/madoc/volltexte/2008/1898/pdf/TR2008_004.pdf (accessed March 28, 2011)
  51. Klein, M., Fensel, F.: Ontology versioning on the Semantic Web. In: Proceedings of the International Semantic Web Working Symposium, SWWS. Stanford University (2001)Google Scholar
  52. Klein, E.E., Herskovitz, P.J.: Philosophical Foundations of Computer Simulation Validation. Simulation & Gaming 36(3), 303–329 (2005)CrossRefGoogle Scholar
  53. Kleindorfer, G.B., O’Neill, L., Ganeshan, R.: Validation in simulation: Various positions in the philosophy of science. Management Science 44(8) (1998)Google Scholar
  54. Konikov, L.F., Bredehoeft, J.D.: Groundwater models cannot be validated. Adv. Water Resour. 15(75-83) (1992)Google Scholar
  55. Küppers, G., Lenhard, J.: Validation of simulation: Pattern in the social and natural sciences. Journal of Artificial Societies and Social Simulation 8(4) (2005)Google Scholar
  56. Lacy, L.W.: Interchanging discrete event simulation process-interaction models using the web ontology language – OWL, Dissertation. University of Central Florida (2006), http://etd.fcla.edu/CF/CFE0001353/Lacy_Lee_W_200612_PhD.pdf (accessed March 30, 2011)
  57. Lacy, L.W., Gerber, W.J.: Potential Modeling and Simulation Applications of the Web Ontology Language - OWL. In: Proceedings of the 2004 Winter Simulation Conference. IEEE, Piscataway (2004)Google Scholar
  58. Livet, P., Müller, J.P., Phan, D., Sanders, L.: Ontology, a Mediator for Agent-Based Modeling in Social Science. Journal of Artificial Societies and Social Simulation 13(1), 3 (2010), http://jasss.soc.surrey.ac.uk/13/1/3.html (accessed March 29, 2011)Google Scholar
  59. Luhmann, N.: Soziologie des Risikos, de Gruyter (1991)Google Scholar
  60. Miller, J.A., Baramidze, G.T., Sheth, A.P., Silver, G.A., Fishwick, P.A.: Investigating ontologies for Simulation Modeling. In: Proceedings of the 37th Annual Simulation Symposium, pp. 55–63 (2004)Google Scholar
  61. Miller, J.A., Baramidze, G.T., Sheth, A.P., Silver, G.A., Fishwick, P.A.: Ontologies for Modeling and Simulation: An Initial Framework (2008), http://www.cs.uga.edu/~jam/jsim/DeMO/paper/journal/final4/demojournal_B_1_7.pdf (accessed March 27, 2011)
  62. Miser, H.J.: A foundational concept of science appropriate for validation in operational research. European Journal of Operational Research, Special Issue on Model Validation 66, 204–215 (1993)Google Scholar
  63. Moss, S.: Alternative Approaches to the Empirical Validation of Agent-Based Models. Journal of Artificial Societies and Social Simulation 11(15) (2008)Google Scholar
  64. Naylor, T.H., Finger, J.M.: Verification of computer simulation models. Management Science 14(2) (1967)Google Scholar
  65. Neumann, M., Braun, A., Heinke, E.M., Saqalli, M., Srbljinovic, A.: Challenges in Modelling Social Conflicts: Grappling with Polysemy. Journal of Artificial Societies and Social Simulation 14(3) (2011)Google Scholar
  66. Niehaves, B., Becker, J., Klose, K.: A framework for epistemological persperctives on simulation. Journal of Artificial Societies and Social Simulation 8(4) (2005)Google Scholar
  67. Oreskes, N., Shrader-Frechette, K., Belitz, K.: Verification, validation, and confirmation of numerical models in the earth sciences. Science 263(4), 641–646 (1994)CrossRefGoogle Scholar
  68. Phan, D., Varenne, F.: Agent-Based Models and Simulations in Economics and Social Scienes. Journal of Artificial Societies and Social Simulation 13(1), 5 (2010)Google Scholar
  69. Pinker, S.: The stuff of thought: Language as a window into human nature. Viking (2007)Google Scholar
  70. Quine, W.O.: Word and Object. MIT Press (1960)Google Scholar
  71. Quine, W.O.: Ontological Relativity. Columbia University Press (1977)Google Scholar
  72. Richardson, K.A.: On the limits of bottom-up computer simulation: Towards a nonlinear modeling culture. In: Proceedings of the 36th Hawaii International Conference on System Science (2003)Google Scholar
  73. Rittle, A.: On the planning crisis: System analysis of the first and second generation. Bedriftsokonomen 8, 390–396 (1972)Google Scholar
  74. Rossiter, S., Noble, J., Bell, K.R.W.: Social Simulations: Improving Interdisciplinary Understanding of Scientific Positioning and Validity. Journal of Artificial Societies and Social Simulation 13(1) (2010)Google Scholar
  75. Ruphy, S.: Limits to Modeling: Balancing Ambition and Outcome in Astrophysics and Cosmology. Simulation Gaming 42(2), 177–194 (2011)CrossRefGoogle Scholar
  76. Salt, J.D.: The seven habits of highly defective simulation projects. Journal of Simulation 2(3), 155–161 (2008)CrossRefGoogle Scholar
  77. Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, S., Tarantola, S.S.: Global Sensitivity Analysis. The Primer. John Wiley and Sons, New Jersey (2008)MATHGoogle Scholar
  78. Silver, G.A., Lacy, L.W., Miller, J.A.: Ontology based representations of simulation models following the process interaction world view. In: Perrone, L.F., Wieland, F.P., Liu, J., Lawson, B.G., Nicol, D.M., Fujimoto, R.M. (eds.) Proceedings of the 2006 Winter Simulation Conference, Monterey, pp. 1168–1176. IEEE (2006)Google Scholar
  79. Silver, G.A., Bellipady, K.R., Miller, J.A., York, W.S., Kochut, K.J.: Supporting Interoperability Using the Discrete-Event Modeling Ontology (DeMO). In: Rossetti, M.D., Hill, R.R., Johansson, B., Dunkin, A., Ingalls, R.G. (eds.) Proceedings of the 2009 Winter Simulation Conference, Piscataway, New Jersey, pp. 1399–1410. IEEE (2009)Google Scholar
  80. Simon, H.: Models of Bounded Rationality, vol. 3. MIT Press (1997)Google Scholar
  81. Simpson, J.: Identity Crisis: Simulations and Models. Simulation & Gaming 42(195) (2011)Google Scholar
  82. Stanislaw, H.: Tests of computer simulation validity: What do they measure? Simulation & Games 17 (1986)Google Scholar
  83. Taylor, S.J.E., Bell, D., Mustafee, N., de Cesare, S., Lycett, M., Fishwick, P.: Semantic web services for simulation component reuse and interoperability: An ontology approach. In: Gunasekaran, A., Shea, T. (eds.) Organizational Advancements through Enterprise Information Systems: Emerging Applications and Developments. IGI Gobal, Inc., Hershey (2010)Google Scholar
  84. Tolk, A., Huiskamp, W., Schaub, H., Davis, P.K., Klein, G.L., Wall, J.A.: Towards methodological approaches to meet the challenges of human, social, cultural, and behavioral (HSCB) modeling. In: Proc. 2010 Winter Simulation Conference, pp. 912–924 (2010)Google Scholar
  85. Tolk, A., Jain, L.C.: Intelligent-based system engineering. ISRL, vol. 10. Springer (2011)Google Scholar
  86. Turnitsa, C., Padilla, J.J., Tolk, A.: Ontology for Modeling and Simulation. In: Johanson, B., Jain, S., Montoya-Torres, J., Hugan, J., Yücesan, E. (eds.) Proceedings of the 2010 Winter Simulation Conference, pp. 643–651. IEEE (2010)Google Scholar
  87. Vangheluwe, H.L.M.: DEVS as a Common Denominator for Multi-formalism Hybrid Systems Modelling. In: Proceedings of the 2000 IEEE International Symposium on Computer-Aided Control System Design, Anchorage, Alaska, USA, September 25-27 (2000)Google Scholar
  88. van Horn, R.L.: Validation of Simulation Results. Management Science 17(5), 247–258 (1971)MathSciNetCrossRefGoogle Scholar
  89. Varenne, F.: What does a computer simulation prove? Simulation in Industry. In: Proceedings of the 13th European Simulation Symposium, vol. 13, pp. 549–554 (2001)Google Scholar
  90. Wang, W.G., Tolk, A., Wang, W.P.: The levels of conceptual interoperability model: Applying systems engineering principles to M&S. In: Proceedings of the Spring Simulation Multiconference, SpringSim 2009, San Diego (2009)Google Scholar
  91. Weber, M.: The methodology of the social sciences (translated and edited by Shils, E.A., Finch, H.A.), New York (1949)Google Scholar
  92. Windrum, P., Fagiolo, G., Moneta, A.: Empirical validation of agent-based models: Alternatives and prospects. Journal of Artificial Societies & Social Simulation 10(2) (2008)Google Scholar
  93. Wittgenstein, L.: Logisch-philosophische Abhandlung, Tractatus logico-philosophicus. Suhrkamp, Frankfurt am Main (1921/1998)Google Scholar
  94. Wittgenstein, L.: Philosophical Investigations. Blackwell Publishing (1953/2001)Google Scholar
  95. Zeigler, B.P., Praehofer, B., Kim, T.G.: Theory of Modelling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems, 2nd edn. Academic Press (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marko Hofmann
    • 1
  1. 1.IT ISUniversity of the Federal Armed Forces MunichNeutraublingGermany

Personalised recommendations