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Checking Simulations: Detecting and Avoiding Errors and Artefacts

  • José M. GalánEmail author
  • Luis R. Izquierdo
  • Segismundo S. Izquierdo
  • José I. Santos
  • Ricardo del Olmo
  • Adolfo López-Paredes
Chapter
Part of the Understanding Complex Systems book series (UCS)

Abstract

The aim of this chapter is to simulations. The reader with a set of concepts and a range of suggested activities that will enhance his or her ability to understand agent-based simulations. To do this in a structured way, we review the main concepts of the methodology (e.g. we provide precise definitions for the terms “error” and “artefact”) and establish a general framework that summarises the process of designing, implementing, and using agent-based models. Within this framework we identify the various stages where different types of assumptions are usually made and, consequently, where different types of errors and artefacts may appear. We then propose several activities that can be conducted to detect each type of error and artefact.

Keywords

Accessory assumptions Agent-based modelling artefact Computer modelling Computer scientist Computer simulation Core assumption Error Formal language Inference engine Modeller Modelling Modelling roles Programmer Re-implementation Replication Simulation Social process Symbolic system Thematician Validation Verbal argumentation Verification 

Notes

Acknowledgements

The authors have benefited from the financial support of the Spanish Ministry of Education and Science (projects CSD2010-00034, DPI2004-06590, DPI2005-05676, and TIN2008-06464-C03-02) and of the Junta de Castilla y León (projects BU034A08 and VA006B09). We are also very grateful to Nick Gotts, Gary Polhill, Bruce Edmonds, and Cesáreo Hernández for many discussions on the philosophy of modelling.

References

  1. Axelrod, R. M. (1997a). Advancing the art of simulation in the social sciences. In R. Conte, R. Hegselmann, & P. Terna (Eds.), Simulating social phenomena. (Lecture Notes in Economics and Mathematical Systems, 456) (pp. 21–40). Berlin: Springer.CrossRefGoogle Scholar
  2. Axelrod, R. M. (1997b). The dissemination of culture: A model with local convergence and global polarization. Journal of Conflict Resolution, 41(2), 203–226.CrossRefGoogle Scholar
  3. Axtell, R. L. (2000). Why agents? On the varied motivations for agent computing in the social sciences. In C. M. Macal & D. Sallach (Eds.), Proceedings of the workshop on agent simulation: applications, models, and tools (pp. 3–24). Argonne National Laboratory: Argonne, IL.Google Scholar
  4. Axtell, R. L., & Epstein, J. M. (1994). Agent based modeling: Understanding our creations. The Bulletin of the Santa Fe Institute, 1994, 28–32.Google Scholar
  5. Bigbee, T., Cioffi-Revilla, C., & Luke, S. (2007). Replication of sugarscape using MASON. In T. Terano, H. Kita, H. Deguchi, & K. Kijima (Eds.), Agent-based approaches in economic and social complex systems IV: Post-proceedings of the AESCS international workshop 2005 (pp. 183–190). Tokyo: Springer.CrossRefGoogle Scholar
  6. Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences of the United States of America, 99(2), 7280–7287.CrossRefGoogle Scholar
  7. Castellano, C., Marsili, M., & Vespignani, A. (2000). Nonequilibrium phase transition in a model for social influence. Physical Review Letters, 85(16), 3536–3539.CrossRefGoogle Scholar
  8. Christley, S., Xiang, X., & Madey, G. (2004). Ontology for agent-based modeling and simulation. In C. M. Macal, D. Sallach, & M. J. North (Eds.), Proceedings of the agent 2004 conference on social dynamics: interaction, reflexivity and emergence. Chicago, IL: Argonne National Laboratory and The University of Chicago. http://www.agent2005.anl.gov/Agent2004.pdf.
  9. Cioffi-Revilla, C. (2002). Invariance and universality in social agent-based simulations. Proceedings of the National Academy of Sciences of the United States of America, 99(3), 7314–7316.CrossRefGoogle Scholar
  10. Conlisk, J. (1996). Why bounded rationality? Journal of Economic Literature, 34(2), 669–700.Google Scholar
  11. David, N., Fachada, N., & Rosa, A. C. (2017). Verifying and validating simulations. doi: https://doi.org/10.1007/978-3-319-66948-9_9.
  12. Drogoul, A., Vanbergue, D., & Meurisse, T. (2003). Multi-agent based simulation: Where are the agents? In J. S. Sichman, F. Bousquet, & P. Davidsson (Eds.), Proceedings of MABS 2002 multi-agent-based simulation. (Lecture Notes in Computer Science, 2581) (pp. 1–15). Bologna: Springer.Google Scholar
  13. Edmonds, B. (2001). The use of models: making MABS actually work. In S. Moss & P. Davidsson (Eds.), Multi-agent-based simulation. (Lecture notes in artificial intelligence, 1979) (pp. 15–32). Berlin: Springer.CrossRefGoogle Scholar
  14. Edmonds, B. (2005). Simulation and complexity: How they can relate. In V. Feldmann & K. Mühlfeld (Eds.), Virtual worlds of precision: Computer-based simulations in the sciences and social sciences (pp. 5–32). Lit-Verlag: Münster.Google Scholar
  15. Edmonds, B. (2017). Different modelling purposes. doi: https://doi.org/10.1007/978-3-319-66948-9_4.
  16. Edmonds, B., & Hales, D. (2003). Replication, replication and replication: Some hard lessons from model alignment. Journal of Artificial Societies and Social Simulation, 6(4). http://jasss.soc.surrey.ac.uk/6/4/11.html.
  17. Edmonds, B., & Hales, D. (2005). Computational Simulation as Theoretical Experiment. Journal of Mathematical Sociology, 29, 1–24.CrossRefGoogle Scholar
  18. Edwards, M., Huet, S., Goreaud, F., & Deffuant, G. (2003). Comparing an individual-based model of behaviour diffusion with its mean field aggregate approximation. Journal of Artificial Societies and Social Simulation, 6(4). http://jasss.soc.surrey.ac.uk/6/4/9.html.
  19. Epstein, J. M. (1999). Agent-based computational models and generative social science. Complexity, 4(5), 41–60.MathSciNetCrossRefGoogle Scholar
  20. Epstein, J. M. (2008). Why model?. Journal of Artificial Societies and Social Simulation, 11(4), 12. http://jasss.soc.surrey.ac.uk/11/4/12.html.
  21. Epstein, J. M., & Axtell, R. L. (1996). Growing artificial societies: Social science from the bottom up. Cambridge, MA: Brookings Institution Press/MIT Press.Google Scholar
  22. Fensel, D. (2001). Ontologies: A silver bullet for knowledge management and electronic commerce. Berlin: Springer.CrossRefzbMATHGoogle Scholar
  23. Galán, J. M., et al. (2009). Errors and artefacts in agent-based modelling. Journal of Artificial Societies and Social Simulation, 12(1). http://jasss.soc.surrey.ac.uk/12/1/1.html.
  24. Galán, J. M., & Izquierdo, L. R. (2005). Appearances can be deceiving: lessons learned re-implementing Axelrod’s ‘evolutionary approach to norms’. Journal of Artificial Societies and Social Simulation, 8(3). http://jasss.soc.surrey.ac.uk/8/3/2.html
  25. Gilbert, N. (1999). Simulation: A new way of doing social science. The American Behavioral Scientist, 42(10), 1485–1487.Google Scholar
  26. Gilbert, N. (2007). Agent-based models. London: Sage Publications.Google Scholar
  27. Gilbert, N., & Terna, P. (2000). How to build and use agent-based models in social science. Mind & Society, 1(1), 57–72.CrossRefGoogle Scholar
  28. Gilbert, N., & Troitzsch, K. G. (1999). Simulation for the social scientist. Buckingham: Open University Press.Google Scholar
  29. Gotts, N. M., Polhill, J. G. & Adam, W. J. (2003, 18–21 September). Simulation and analysis in agent-based modelling of land use change. Online proceedings of the first conference of the European Social Simulation Association, Groningen, The Netherlands, http://www.uni-koblenz.de/~essa/ESSA2003/gotts_polhill_adam-rev.pdf.
  30. Gruber, T. R. (1993). A translation approach to portable ontology specifications. Knowledge Acquisition, 5(2), 199–220.CrossRefGoogle Scholar
  31. Hare, M., & Deadman, P. (2004). Further towards a taxonomy of agent-based simulation models in environmental management. Mathematics and Computers in Simulation, 64(1), 25–40.MathSciNetCrossRefzbMATHGoogle Scholar
  32. Hernández, C. (2004). Herbert A. Simon, 1916-2001, y el Futuro de la Ciencia Económica. Revista Europea De Dirección y Economía De La Empresa, 13(2), 7–23.Google Scholar
  33. Heywood, J. G., Masuda, K., Rautmann, R., & Solonnikov, V. A. (Eds.). (1990). The Navier-Stokes equations: Theory and numerical methods; Proceedings of a conference held at Oberwolfach, FRG, Sept. 18–24, 1988. (Lecture Notes in Mathematics, 1431). Berlin: Springer.zbMATHGoogle Scholar
  34. Holland, J. H., & Miller, J. H. (1991). Artificial adaptive agents in economic theory. American Economic Review, 81(2), 365–370.Google Scholar
  35. Izquierdo, L. R., & Polhill, J. G. (2006). Is your model susceptible to floating point errors? Journal of Artificial Societies and Social Simulation, 9(4). http://jasss.soc.surrey.ac.uk/9/4/4.html.
  36. Kleijnen, J. P. C. (1995). Verification and validation of simulation models. European Journal of Operational Research, 82(1), 145–162.MathSciNetCrossRefzbMATHGoogle Scholar
  37. Kleindorfer, G. B., O'Neill, L., & Ganeshan, R. (1998). Validation in simulation: Various positions in the philosophy of science. Management Science, 44(8), 1087–1099.CrossRefzbMATHGoogle Scholar
  38. Klemm, K., Eguíluz, V., Toral, R., & San Miguel, M. (2003a). Role of dimensionality in Axelrod’s model for the dissemination of culture. Physica A, 327, 1–5.MathSciNetCrossRefzbMATHGoogle Scholar
  39. Klemm, K., Eguíluz, V., Toral, R., & San Miguel, M. (2003b). Global culture: A noise-induced transition in finite systems. Physical Review E, 67(4), 045101.CrossRefGoogle Scholar
  40. Klemm, K., Eguíluz, V., Toral, R., & San Miguel, M. (2003c). Nonequilibrium transitions in complex networks: A model of social interaction. Physical Review E, 67(2), 026120.CrossRefGoogle Scholar
  41. Klemm, K., Eguíluz, V., Toral, R., & San Miguel, M. (2005). Globalization, polarization and cultural drift. Journal of Economic Dynamics & Control, 29(1–2), 321–334.CrossRefzbMATHGoogle Scholar
  42. Kluver, J., & Stoica, C. (2003). Simulations of group dynamics with different models. Journal of Artificial Societies and Social Simulation, 6(4). http://jasss.soc.surrey.ac.uk/6/4/8.html.
  43. Leombruni, R., & Richiardi, M. (2005). Why are economists sceptical about agent-based simulations? Physica A, 355, 103–109.MathSciNetCrossRefGoogle Scholar
  44. Moss, S. (2001). Game theory: Limitations and an alternative. Journal of Artificial Societies and Social Simulation, 4(2). http://jasss.soc.surrey.ac.uk/4/2/2.html.
  45. Moss, S. (2002). Agent based modelling for integrated assessment. Integrated Assessment, 3(1), 63–77.CrossRefGoogle Scholar
  46. Moss, S., Edmonds, B., & Wallis, S. (1997). Validation and verification of computational models with multiple cognitive agents (Report no. 97–25). Manchester: Centre for Policy Modelling, http://cfpm.org/cpmrep25.html.
  47. Ostrom, T. (1988). Computer simulation: The third symbol system. Journal of Experimental Social Psychology, 24(5), 381–392.CrossRefGoogle Scholar
  48. Parunak, H. V. D., Savit, R., & Riolo, R. L. (1998). Agent-based modeling vs. equation-based modeling: A case study and users’ guide. In J. S. Sichman, R. Conte, & N. Gilbert (Eds.), Multi-agent systems and agent-based simulation. (Lecture notes in artificial intelligence 1534) (pp. 10–25). Berlin: Springer.CrossRefGoogle Scholar
  49. Pavón, J. & Gómez-Sanz, J. (2003). Agent oriented software engineering with INGENIAS. In V. Marik, J. Müller & M. Pechoucek (Eds.), Multi-agent systems and applications III, 3rd international central and eastern European conference on multi-agent systems, CEEMAS. (Lecture notes in artificial intelligence, 2691) (pp. 394–403); Berlin, Heidelberg: Springer.Google Scholar
  50. Pignotti, E., Edwards, P., Preece, A., Polhill, J.G. & Gotts, N.M. (2005). Semantic support for computational land-use modelling. Proceedings of the 5th international symposium on cluster computing and the grid (CCGRID 2005) (pp. 840–847). Piscataway, NJ: IEEE Press.Google Scholar
  51. Polhill, J. G. & Gotts, N. M. (2006, August 21–25). A new approach to modelling frameworks. Proceedings of the first world congress on social simulation. (Vol. 1, pp. 215–222), Kyoto, Japan.Google Scholar
  52. Polhill, J. G., & Izquierdo, L. R. (2005). Lessons learned from converting the artificial stock market to interval arithmetic. Journal of Artificial Societies and Social Simulation, 8(2). http://jasss.soc.surrey.ac.uk/8/2/2.html.
  53. Polhill, J. G., Izquierdo, L. R., & Gotts, N. M. (2005). The ghost in the model (and other effects of floating point arithmetic). Journal of Artificial Societies and Social Simulation, 8(1). http://jasss.soc.surrey.ac.uk/8/1/5.html.
  54. Polhill, J. G., Izquierdo, L. R., & Gotts, N. M. (2006). What every agent based modeller should know about floating point arithmetic. Environmental Modelling & Software, 21(3), 283–309.CrossRefGoogle Scholar
  55. Riolo, R. L., Cohen, M. D., & Axelrod, R. M. (2001). Evolution of cooperation without reciprocity. Nature, 411, 441–443.CrossRefGoogle Scholar
  56. Sakoda, J. M. (1971). The checkerboard model of social interaction. Journal of Mathematical Sociology, 1(1), 119–132.CrossRefGoogle Scholar
  57. Salvi, R. (2002). The Navier-Stokes equation: Theory and numerical methods. (Lecture notes in pure and applied mathematics). New York: Marcel Dekker.Google Scholar
  58. Sansores, C., & Pavón, J. (2005, November 14–18). Agent-based simulation replication: A model driven architecture approach. In A. F. Gelbukh, A. de Albornoz, & H. Terashima-Marín (Eds.), Proceedings of MICAI 2005: Advances in artificial intelligence, 4th Mexican international conference on artificial intelligence. (Lecture notes in computer science, 3789) (pp. 244–253), Monterrey, Mexico. Berlin, Heidelberg: Springer.Google Scholar
  59. Sansores, C., Pavón, J., & Gómez-Sanz, J. (2006, July 25). Visual modeling for complex agent-based simulation systems. In J. S. Sichman & L. Antunes (Eds.), Multi-agent-based simulation VI, International workshop, MABS 2005, revised and invited papers. (Lecture notes in computer science, 3891) (pp. 174–189), Utrecht, The Netherlands. Berlin, Heidelberg: Springer.Google Scholar
  60. Sargent, R. G. (2003). Verification and validation of simulation models. In S. Chick, P. J. Sánchez, D. Ferrin, & D. J. Morrice (Eds.), Proceedings of the 2003 winter simulation conference (pp. 37–48). Piscataway, NJ: IEEE.Google Scholar
  61. Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1(2), 47–186.CrossRefzbMATHGoogle Scholar
  62. Schelling, T. C. (1978). Micromotives and macrobehavior. New York: Norton.Google Scholar
  63. Schmeiser, B. W. (2001, December 09–12). Some myths and common errors in simulation experiments. In B. A. Peters, J. S. Smith, D. J. Medeiros, & M. W. Rohrer (Eds.), Proceedings of the winter simulation conference (Vol. 1, pp. 39–46), Arlington, VA.Google Scholar
  64. Takadama, K., Suematsu, Y. L., Sugimoto, N., Nawa, N. E., & Shimohara, K. (2003). Cross-element validation in multiagent-based simulation: Switching learning mechanisms in agents. Journal of Artificial Societies and Social Simulation, 6(4). http://jasss.soc.surrey.ac.uk/6/4/6.html.
  65. Taylor, A. J. (1983). The verification of dynamic simulation models. Journal of the Operational Research Society, 34(3), 233–242.CrossRefGoogle Scholar
  66. Xu, J., Gao, Y. & Madey, G. (2003, April 13–15). A docking experiment: swarm and repast for social network modeling. In Seventh annual swarm researchers conference (SwarmFest 2003. Notre Dame, IN.Google Scholar
  67. Yilmaz, L. (2006). Validation and verification of social processes within agent-based computational organization models. Computational & Mathematical Organization Theory, 12(4), 283–312.CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • José M. Galán
    • 1
    Email author
  • Luis R. Izquierdo
    • 1
  • Segismundo S. Izquierdo
    • 2
  • José I. Santos
    • 1
  • Ricardo del Olmo
    • 1
  • Adolfo López-Paredes
    • 2
  1. 1.Department of Civil EngineeringUniversidad de BurgosBurgosSpain
  2. 2.Departamento de Organización de Empresas y C.I.M.Universidad de ValladolidValladolidSpain

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