From social simulation to integrative system design

Review

Abstract.

The purpose of this White Paper of the EU Support Action “Visioneer” (see www.visioneer.ethz.ch) is to address the following goals:
  1. 1.

    Develop strategies to build up social simulation capacities.

     
  2. 2.

    Suggest ways to build up an “artificial societies” community that aims at simulating real and alternative societies by means of supercomputers, grid or cloud computing.

     
  3. 3.

    Derive proposals to establish centers for integrative systems design.

     

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    D. Helbing, S. Balietti, Fundamental and real world challenges in economics (print) (2010)Google Scholar
  2. 2.
    D. Helbing, S. Balietti, Formulating grand fundamental challenges, http://www.visioneer.ethz.chGoogle Scholar
  3. 3.
    D. Helbing, S. Balietti, How to create an Innovation Accelerator. , Eur. Phys. J. Special Topics 195, 101 (2011)http://www.visioneer.ethz.chCrossRefADSGoogle Scholar
  4. 4.
    D. Helbing, S. Balietti, From Social Data Mining to Forecasting Socio-Economic Crises Eur. Phys. J. Special Topics 195, 3 (2011)http://www.visioneer.ethz.chCrossRefADSGoogle Scholar
  5. 5.
    D. Helbing, Pluralistic modeling of complex systems, CCSS-10-009 (2010), http://ssrn.com/abstract=1646314(2010)Google Scholar
  6. 6.
    R.E. Shannon, In WSC ’98: Proceedings of the 30th conference on Winter simulation (Los Alamitos, CA, USA, 1998) (IEEE Computer Society Press), pp. 7–14Google Scholar
  7. 7.
    G.H. Orcutt, Rev. Econ. Stat. 58, 773 (1957)Google Scholar
  8. 8.
    N. Gilbert, K.G. Troitzsch, Simulation for the Social Scientist (Open University Press, 2005)Google Scholar
  9. 9.
    S. Weinberg, New York Rev. Books 49, 16 (2002)Google Scholar
  10. 10.
    S. Wolfram, A New Kind of Science, English (Wolfram Media, 2002), http://www.wolframscience.comGoogle Scholar
  11. 11.
    I. Foster, Nature 440, 419 (2006)ADSCrossRefGoogle Scholar
  12. 12.
    C.S. Taber, R.J Timpone, Comput. Model. Sage, 1996Google Scholar
  13. 13.
    H.J. Aaron, J. Econom. Perspect. 8, 3 (1994), http://ideas.repec.org/a/aea/jecper/v8y1994i2p3-21.htmlCrossRefGoogle Scholar
  14. 14.
    A.P. Kirman, J. Econom. Perspect. 6, 117 (1992), http://ideas.repec.org/a/aea/jecper/v6y1992i2p117-36.htmlGoogle Scholar
  15. 15.
    D. Helbing, Managing Complexity: Insights, Concepts, Applications, 1st edn. (Springer, 2007)Google Scholar
  16. 16.
    D. Helbing, Systemic risks in society and economics, Sante Fe Institute, working paper 09-12-044 (2009), http://www.santafe.edu/research/working-papers/abstract/9596e5a57d1f9b7e8fcc289f118555ce/Google Scholar
  17. 17.
    H.V.D. Parunak, Ann. Opera. Res. 75, 69 (1997)MATHCrossRefGoogle Scholar
  18. 18.
    Earth simulator: UK-japan climate collaboration, http://www.earthsimulator.org.uk/Google Scholar
  19. 19.
    K. Nagel, Parall. Comput. 27, 1611 (2001)MATHCrossRefGoogle Scholar
  20. 20.
    M. Rickert, NagelK, Future Gener. Comput. Syst. 17, 637 (2001)CrossRefGoogle Scholar
  21. 21.
    K. Nagel, R.J. Beckman, C.L. Barrett, Transims for urban planning, LA-UR-98-4389 (1999)Google Scholar
  22. 22.
    K. Nagel, C.L. Barrett, Using microsimulation feedback for trip adaptation for realistic traffic in dallas (1997)Google Scholar
  23. 23.
    P.M. Simon, J. Esser, K. Nagel, Int. J. Modern Physics C 10, 941 (1999)ADSCrossRefGoogle Scholar
  24. 24.
    B. Raney, N. Cetin, A. Vollmy, M. Vrtic, K. Axhausen, K. Nagel, Networks Spatial Econom. 3, 23 (2003)CrossRefGoogle Scholar
  25. 25.
    M. Balmer, K.W. Axhausen, K. Nagel, An agent based demand modeling framework for large scale micro-simulations (ETH Zurich, Switzerland 2005)Google Scholar
  26. 26.
    D. Charypar, K. Nagel, Transportation 32, 369 (2005)CrossRefGoogle Scholar
  27. 27.
    K.W. Axhausen, T. Garling, Transport Rev. 12, 323 (1992)CrossRefGoogle Scholar
  28. 28.
    D. Helbing, K. Nagel, Contemp. Phys. 45, 405 (2004)ADSCrossRefGoogle Scholar
  29. 29.
    V. Killer, et al., 50th European Regional Science Association (ERSA) (Sweden, 2010)Google Scholar
  30. 30.
    G. Laemmel, D. Grether, K. Nagel, Transportation Research Part C: Emerging Technologies (2009)Google Scholar
  31. 31.
    H. Taubenboeck, et al., Natur. Haz. Earth Syst. Sci. 9, 1509 (2009)ADSCrossRefGoogle Scholar
  32. 32.
    J. Min, et al., IIE Trans. 39, 57 (2007)MathSciNetCrossRefGoogle Scholar
  33. 33.
    C. Barrett, S. Eubank, M. Marathe, Interactive Computation, edited by D. Goldin, S.A. Smolka, P. Wegner (Springer, Berlin, Heidelberg, 2006), pp. 353–392Google Scholar
  34. 34.
    National infrastructure simulation and analysis center (NISAC), http://www.lanl.gov/programs/nisac/Google Scholar
  35. 35.
    W. Kermack, A. Mckendrick, Bull. Math. Biol. 53, 33 (1991)Google Scholar
  36. 36.
    W. Kermack, A. Mckendrick, Bull. Math. Biol. 53, 57 (1991)Google Scholar
  37. 37.
    W. Kermack, A. McKendrick, Bull. Math. Biol. 53, 89 (1991)Google Scholar
  38. 38.
    V. Colizza, A. Barrat, M. Bartholemy, A. Vespignani, Proc. Nation. Acad. Sci. USA 103, 2015 (2006)ADSCrossRefGoogle Scholar
  39. 39.
    J.M. Epstein, Nature 460, 687 (2009)ADSCrossRefGoogle Scholar
  40. 40.
    L. Hufnagel, D. Brockmann, T. Geisel, Proc. Nation. Acad. Sci. USA 101, 15124 (2004), http://www.pnas.org/content/101/42/15124.full.pdf+html, http://www.pnas.org/content/101/42/15124.abstractADSCrossRefGoogle Scholar
  41. 41.
    S. Eubank, et al., Nature 429, 180 (2004)ADSCrossRefGoogle Scholar
  42. 42.
    Artificial stock market, http://sourceforge.net/projects/artstkmkt/Google Scholar
  43. 43.
    U-mart, http://www.u-mart.org/Google Scholar
  44. 44.
    Eurace, http://www.eurace.org/Google Scholar
  45. 45.
    D. Helbing, W. Yu, Proc. Nation. Acad. Sci. 106, 3680 (2009)ADSCrossRefGoogle Scholar
  46. 46.
    D. Helbing, A. Johansson, Cooperation, norms, and conflict: A unified approach. SFI Working Paper (2009)Google Scholar
  47. 47.
    D. Helbing, et al., PLoS Comput Biol. 6, e1000758 (2010)MathSciNetCrossRefGoogle Scholar
  48. 48.
    A. Cangelosi, D. Parisi, Simulating the evolution of language (Springer-Verlag, 2002)Google Scholar
  49. 49.
    R. Dunbar, Grooming, gossip, and the evolution of language (Harvard Univ Pr, 1998)Google Scholar
  50. 50.
    M.A. Nowak, D.C. Krakauer, Proc. Nation. Acad. Sci. USA 96, 8028 (1999)ADSCrossRefGoogle Scholar
  51. 51.
    J.M. Epstein, R. Axtell, Growing artificial societies (Cambridge, MA, 1996)Google Scholar
  52. 52.
    Parallel discrete-event simulation of population dynamics (2008), pp. 1047–1054Google Scholar
  53. 53.
    Prace: Partnership for advanced computing in europe, http://www.prace-project.eu/Google Scholar
  54. 54.
    D. Helbing, H. Ammoser, C. Kuehnert, Chap. Disasters as extreme events and the importance of network interactions for disaster response management, edited by S. Albeverio, V. Jentsch, H. Kantz (Berlin: Springer, 2005), pp. 319–348Google Scholar
  55. 55.
    J. Sterman, Business dynamics: systems thinking and modeling for a complex world (Irwin, McGraw-Hill, 2000)Google Scholar
  56. 56.
    Transims: Transportation analysis and simulation system, http://www.transims-opensource.net/Google Scholar
  57. 57.
    Matsims: Multi-agent transport simulation toolkit, http://www.matsim.org/Google Scholar
  58. 58.
    Essa: European social simulation association, http://www.essa.eu.org/Google Scholar
  59. 59.
    D. Helbing, W. Yu, PNAS 12, 5265 (2010)ADSCrossRefGoogle Scholar
  60. 60.
    A. Tversky, D. Kahneman, Science 211, 453 (1981)MathSciNetADSCrossRefMATHGoogle Scholar
  61. 61.
    Add-ons for firefox, https://addons.mozilla.org/en-US/firefox/Google Scholar
  62. 62.
    Drupal, an open source content management system. modules page, http://drupal.org/project/ModulesGoogle Scholar
  63. 63.
    Apple web apps store, http://www.apple.com/webapps/Google Scholar
  64. 64.
    Google android market, http://www.android.com/marketGoogle Scholar
  65. 65.
    Swarm, http://www.swarm.org/Google Scholar
  66. 66.
    Repast, recursive porous agent simulation toolkit, http://repast.sourceforge.net/Google Scholar
  67. 67.
    Netlogo, http://ccl.northwestern.edu/netlogo/Google Scholar
  68. 68.
    Sesam, shell for simulated agent systems, http://www.simsesam.de/Google Scholar
  69. 69.
    Wikipedia: Comparison of agent-based modeling software, http://en.wikipedia.org/wiki/Comparison_of_agent-based_modeling_softwareGoogle Scholar
  70. 70.
    Matlab, http://www.mathworks.com/Google Scholar
  71. 71.
    J.M. Epstein, J. Artific. Soc. Simul. 11, 12 (2008)Google Scholar
  72. 72.
    S. Laemmer, D. Helbing, JSTAT, P04019 (2008)Google Scholar
  73. 73.
    D. Helbing, S. Laemmer, http://www.patent-de.com/20100805/DE102005023742B4.html 2010Google Scholar
  74. 74.
    D. Helbing, M. Christen, Mit rauschen und reibung gegen finanzielle blasen (submitted) (Wirtschaftswoche, 2010)Google Scholar
  75. 75.
    D. Helbing, A. Deutsch, S Diez, K Peters, Y Kalaidzidis, K. Padberg-Gehle, S. Laemmer, A. Johansson, G. Breier, F. Schulze, M. Zerial, Adv. Compl. Syst. 12, 533 (2009)CrossRefGoogle Scholar
  76. 76.
    A. Kesting, M. Treiber, M. Schonhof, D. Helbing, Transport. Res. Part C: Emerg. Technol. 16, 668 (2008)CrossRefGoogle Scholar
  77. 77.
    D. Helbing, Chap. Dynamic decision behavior and optimal guidance through information services: Models and experiments, edited by M. Schreckenberg, R. Selten (Springer, Berlin, 2004), pp. 47–95Google Scholar
  78. 78.
    N. Wiener, Cybernetics, Second Edition: or the Control and Communication in the Animal and the Machine (The MIT Press, 1965)Google Scholar
  79. 79.
    B. Fabien, Analytical System Dynamics: Modeling and Simulation (Springer, 2008)Google Scholar
  80. 80.
    S. Skogestad, I. Postlethwaite, Multivariable Feedback Control: Analysis and Design, 2nd edn. (Wiley-Interscience, 2005)Google Scholar
  81. 81.
    A.L. Fradkov, I.V. Miroshnik, V.O. Nikiforov, Nonlinear and Adaptive Control of Complex Systems (Springer, 1999)Google Scholar
  82. 82.
    J.H. Kagel, The Handbook of Experimental Economics (Princeton University Press, 2004)Google Scholar
  83. 83.
    N. Bardsley, R. Cubitt, G. Loomes, P. Moffatt, C. Starmer, R. Sugden, Experimental Economics: Rethinking the Rules (Princeton University Press, Princeton, NJ, 2009)Google Scholar
  84. 84.
    D. Friedman, A. Cassar, Economics Lab: An Intensive Course in Experimental Economics (London and New York: Routledge, 2004)Google Scholar
  85. 85.
    D. Friedman, S. Sunder, Experimental Methods: A Primer for Economists (Cambridge University Press, 1994)Google Scholar
  86. 86.
    F. Guala, The Methodology of Experimental Economics (Cambridge University Press, 2005), http://ideas.repec.org/b/cup/cbooks/9780521618618.htmlGoogle Scholar
  87. 87.
    M.J. Salganik, P.S. Dodds, D.J. Watts, Science 311, 854 (2006)ADSCrossRefGoogle Scholar
  88. 88.
    M. Szell, S. Thurner, Social Networks 32, 313 (2010)CrossRefGoogle Scholar
  89. 89.
    W.S. Bainbridge, Science 317, 472 (2007)ADSCrossRefGoogle Scholar
  90. 90.
    N.F. Johnson, et al., Phys. Rev. E 79, 066117 (2009)ADSCrossRefGoogle Scholar
  91. 91.
    D.B. Fogel, Evolutionary computation 1: basic algorithms and operators (2000), p. 1Google Scholar
  92. 92.
    D.B. Fogel, Evolutionary computation: toward a new philosophy of machine intelligence (Wiley-IEEE Press, 2006)Google Scholar
  93. 93.
    S. Haykin, Neural networks: a comprehensive foundation (Prentice Hall PTR Upper Saddle River, NJ, USA, 1994)Google Scholar
  94. 94.
    M.T. Hagan, H.B Demuth, M. Beale, et al., Neural network design (PWS Pub, 1996)Google Scholar
  95. 95.
    C.A. Janeway, et al., Immunobiology: the immune system in health and disease (Churchill Livingstone, 2001)Google Scholar
  96. 96.
    R.M. May, Stability and Complexity in Model Ecosystems (Princeton Landmarks in Biology, Princeton University Press, 2001)Google Scholar
  97. 97.
    R. May, A. McLean, Theoretical Ecology: Principles and Applications, 3rd edn. (Oxford University Press, USA, 2007)Google Scholar
  98. 98.
    D. Helbing, M. Moussaid, Tech. Rep. [arXiv:0807.4006], Comments: For related work see http://www.soms.ethz.ch/ 2008Google Scholar
  99. 99.
    D. Helbing, Rev. Mod. Phys. 4, 1067 (2000) (cond-mat/0012229)MathSciNetGoogle Scholar
  100. 100.
    Why economists failed to predict the financial crisis, http://www.ftpress.com/articles/article.aspx?p=1350507Google Scholar
  101. 101.
    D. Keim, IEEE Trans. Visual. Comp. Graph. 8, 1 (2002)CrossRefGoogle Scholar
  102. 102.
    A. Kageyama, N. Ohno, Proc. ISSS-7 (2005), p. 127Google Scholar
  103. 103.
    A. Jacobs, ACM Queue 7, 6 (2009)CrossRefGoogle Scholar
  104. 104.
    B. Shneiderman, Visual Languages, IEEE Symposium on 0, 336 (1996)CrossRefGoogle Scholar
  105. 105.
    R.M. Pickett, G.G. Grinstein, Proc. IEEE Conf. on Systems, Man and Cybernetics (Piscataway, NJ, 1988), pp. 514–519Google Scholar
  106. 106.
    H. Chernoff, J. Amer. Statistical Association 68, 361 (1973)CrossRefGoogle Scholar
  107. 107.
    E.A. Bier, et al., Proc. SIGGRAPH, Anaheim (CA, 1993), p. 7380Google Scholar
  108. 108.
    A. Inselberg, B. Dimsdale, Proc. Visualization (San Francisco, CA, 1990), pp. 361–370Google Scholar
  109. 109.
    G.G. Robertson, J.D. Mackinlay, S.K. Card, Proc. Human Factors in Computing Systems CHI 91 Conf. (New Orleans, LA, 1991), pp. 189–194Google Scholar
  110. 110.
    J. Lamping, R. Rao, P. Pirolli, Proc. Human Factors in Computing Systems Conf. (1995), p. 401408Google Scholar
  111. 111.
    J.J. van Wijk, H. van de Wetering, Proceedings 1999 IEEE Symposium on Information Visualization (1999), p. 7378Google Scholar
  112. 112.
    M. Bruls, K. Huizing, J.J. Van Wijk, Proceedings of Joint Eurographics and IEEE TCVG Symposium on Visualization (IEEE Press, 2000), p. 3342Google Scholar
  113. 113.
    F. Frankel, R. Reid, Nature, 455, 30 (2008)Google Scholar
  114. 114.
    Sciencesim, http://www.sciencesim.comGoogle Scholar
  115. 115.
    S. Bryson, Comm. ACM 39, 5 (1996)CrossRefGoogle Scholar
  116. 116.
    J.J. LaViola, et al., Trends in Interactive Visualization, edited by R. Liere, T. Adriaansen, E. Zudilova-Seinstra (Springer, London, 2009), pp. 1–26Google Scholar
  117. 117.
    D. Germans, H. Spoelder, L. Renambot, et al., IPT/EGVE (2001)Google Scholar
  118. 118.
    F. Araki, S. Kawahara, N. Ohno, Chap. Study of Large-Scale Data Visualization (2008), pp. 273–277Google Scholar
  119. 119.
    Vfive: Virtual reality visualization software for cave system, http://www.jamstec.go.jp/esc/research/Perception/vfive.en.htmlGoogle Scholar
  120. 120.
    Top 500 supercomputer sites, http://www.top500.orgGoogle Scholar

Copyright information

© EDP Sciences and Springer 2011

Authors and Affiliations

  1. 1.ETH Zurich, CLUZurichSwitzerland
  2. 2.Santa Fe InstituteSanta FeUSA

Personalised recommendations