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Challenges in Economics

  • Dirk Helbing
Chapter
Part of the Understanding Complex Systems book series (UCS)

Abstract

In the same way as the Hilbert Program was a response to the foundational crisis of mathematics [1], this article tries to formulate a research program for the socio-economic sciences. The aim of this contribution is to stimulate research in order to close serious knowledge gaps in mainstream economics that the recent financial and economic crisis has revealed. By identifying weak points of conventional approaches in economics, we identify the scientific problems which need to be addressed. We expect that solving these questions will bring scientists in a position to give better decision support and policy advice. We also indicate, what kinds of insights can be contributed by scientists from other research fields such as physics, biology, computer and social science. In order to make a quick progress and gain a systemic understanding of the whole interconnected socio-economic-environmental system, using the data, information and computer systems available today and in the near future, we suggest a multi-disciplinary collaboration as most promising research approach.

Keywords

Public Good Financial Market Business Cycle Economic System Invisible Hand 
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.

Notes

Acknowledgements

The authors are grateful for partial financial support by the ETH Competence Center “Coping with Crises in Complex Socio-Economic Systems” (CCSS) through ETH Research Grant CH1-01 08-2 and by the Future and Emerging Technologies programme FP7-COSI-ICT of the European Commission through the project Visioneer (grant no.: 248438). They would like to thank for feedbacks on the manuscript by Kenett Dror, Tobias Preis and Gabriele Tedeschi as well as for inspiring discussions during a Visioneer workshop in Zurich from January 13 to 15, 2010, involving, besides the authors, Stefano Battiston Guido Caldarelli, Anna Carbone, Giovanni Luca Ciampaglia, Andreas Flache, Imre Kondor, Sergi Lozano, Thomas Maillart, Amin Mazloumian, Tamara Mihaljev, Alexander Mikhailov, Ryan Murphy, Carlos Perez Roca, Stefan Reimann, Aki-Hiro Sato, Christian Schneider, Piotr Swistak, Gabriele Tedeschi, and Jiang Wu. Last but not least, we are grateful to Didier Sornette, Frank Schweitzer and Lars-Erik Cederman for providing some requested references.

References

  1. 1.
    P. Mancosu (ed.) From Brouwer to Hilbert: The Debate on the Foundations of Mathematics in the 1920s. (Oxford University Press, New York, 1998)Google Scholar
  2. 2.
    Paul Krugman, “How did economists get it so wrong?”, The New York Times (September 2, 2009), see http://www.nytimes.com/2009/09/06/magazine/06Economic-t.html
  3. 3.
    D. Colander et al., The Financial Crisis and the Systemic Failure of Academic Economics (Dahlem Report, Univ. of Copenhagen Dept. of Economics Discussion Paper No. 09–03, 2009), see http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1355882
  4. 4.
  5. 5.
    DeLisle Worrell, What’s wrong with economics. Address of the Governor of the Central Bank of Barbados on June 30, 2010, see http://www.bis.org/review/r100713c.pdf
  6. 6.
    D. Helbing, The FuturIcT knowledge accelerator: Unleashing the power of information for a sustainable future, see http://arxiv.org/abs/1004.4969 and http://www.futurict.eu
  7. 7.
    Bjorn Lomborg, Global Crises, Global Solutions: Costs and Benefits. (Cambridge University Press, Cambridge, 2nd ed., 2009)Google Scholar
  8. 8.
    R.H. Nelson, Economics as Religion: from Samuelson to Chicago and Beyond. (Pennsylvania State University, 2001)Google Scholar
  9. 9.
    F.A. Von Hayek, Individualism and Economic Order. (University of Chicago Press, 1948)Google Scholar
  10. 10.
    F.A. Von Hayek, The Counter Revolution of Science. (The Free Press of Glencoe, London, 1955)Google Scholar
  11. 11.
    M. Friedman, The Case of flexible exchange rates, in: Essays in Positive Economics. (University of Chicago Press, Chicago, 1953)Google Scholar
  12. 12.
    R. Lucas, Adaptive behavior and economic theory, J. Bus. 59, S401–S426 (1986)Google Scholar
  13. 13.
    G. Gigerenzer, R. Selten (eds.) Bounded Rationality. The Adaptive Toolbox. (MIT Press, Cambridge, MA, 2001)Google Scholar
  14. 14.
    H. Gintis, The Bounds of Reason: Game Theory and the Unification of the Behavioral Sciences. (Princeton University Press, Princeton, 2009)Google Scholar
  15. 15.
    F. Caccioli, M. Marsili, On information efficiency and financial stability. Preprint http://arxiv.org/abs/1004.5014
  16. 16.
    H.A. Simon Models of Man. (Wiley, New York, 1957)Google Scholar
  17. 17.
    H.A. Simon, Models of Bounded Rationality. Vol. 1 (MIT Press, Boston, 1984)Google Scholar
  18. 18.
    G. Gigerenzer, P.M. Todd, and the ABC Research Group, Simple Heuristics That Make Us Smart. (Oxford University Press, Oxford, 2000)Google Scholar
  19. 19.
    For a collection of cognitive biases see http://en.wikipedia.org/wiki/List_of_cognitive_biases
  20. 20.
    D. Kahneman, P. Slovic, A. Tversky, Judgment under Uncertainty: Heuristics and Biases. (Cambridge University Press, Cambridge, MA, 1982)Google Scholar
  21. 21.
    V.I. Yukalov, D. Sornette, Physics of risk and uncertainty in quantum decision making, European Physical Journal B 71, 533–548 (2009)Google Scholar
  22. 22.
    E. Fehr, K.M. Schmidt, A theory of fairness, competition, and cooperation. Q. J. Econ. 114(3), 817–868 (1999)Google Scholar
  23. 23.
    J. Henrich, R. Boyd, S. Bowles, C. Camerer, Foundations of Human Sociality: Economic Experiments and Ethnographic Evidence from Fifteen Small-Scale Societies. (Oxford University, Oxford, 2004)Google Scholar
  24. 24.
    E. Hoffman, K. McCabe, V.L. Smith, Social distance and other-regarding behavior in dictator games. Am. Econ. Rev. 86(3), 653–660 (1996)Google Scholar
  25. 25.
    R.O. Murphy, K. Ackermann, Measuring social value orientation. Judgement and decision making 6(8), 771–781 (2011)Google Scholar
  26. 26.
    Ö.B. Bodvarsson, W.A. Gibson, Economics and restaurant gratuities: Determining tip rates. Am. J. Econ. Soc. 56(2), 187–203 (1997)Google Scholar
  27. 27.
    J. Elster, The Cement of Society: A Study of Social Order. (Cambridge University Press, Cambridge, 1989)Google Scholar
  28. 28.
    C. Horne, The Rewards of Punishment. A Relational Theory of Norm Enforcement. (Stanford University Press, Stanford, 2009)Google Scholar
  29. 29.
    D. Helbing, W. Yu, K.-D. Opp, H. Rauhut, The emergence of homogeneous norms in heterogeneous populations. Santa Fe Working Paper 11-01-001 (2011), see http://www.santafe.edu/media/workingpapers/11-01-001.pdf, last accessed on March 6, 2012
  30. 30.
    D. Centola, R. Willer, M. Macy, The emperor’s dilemma: A computational model of self-enforcing norms. Am. J. Soc. 110, 1009–1040 (2005)Google Scholar
  31. 31.
    J.M. Epstein, R.A. Hammond, Non-explanatory equilibria: An extremely simple game with (mostly) unattainable fixed points. Complexity 7(4), 18–22 (2002)Google Scholar
  32. 32.
    C. Borgs, J. Chayesa, N. Immorlicab, A.T. Kalaia, V. Mirroknia, C Papadimitriou, The myth of the folktheorem, Games and Economic Behavior 70(1), 34–43 (2010)Google Scholar
  33. 33.
    H.G. Schuster, Deterministic Chaos. (Wiley VCH, Weinheim, 2005)Google Scholar
  34. 34.
    For example, three-body planetary motion has deterministic chaotic solutions, although it is a problem in classical mechanics, where the equations of motion optimize a Lagrangian functional.Google Scholar
  35. 35.
    K. Gödel, On Formally Undecidable Propositions of Principia Mathematica and Related Systems. (Basic, New York, 1962)Google Scholar
  36. 36.
    A.M. Turing, On computable numbers, with an application to the Entscheidungsproblem. Proc. Lond. Math. Soc. 2.42, 230–265 (1936)Google Scholar
  37. 37.
    E. Fama, The Behavior of stock market prices. J. Bus. 38 34–105 (1965)Google Scholar
  38. 38.
    Wikipedia article on Adam Smith, see http://en.wikipedia.org/wiki/Adam_Smith, downloaded on July 14, 2010.
  39. 39.
    A. Smith (1776), An Inquiry into the Nature and Causes of the Wealth of Nations. (University of Chicago Press, 1977)Google Scholar
  40. 40.
    A. Smith, The Theory of Moral Sentiments (1759)Google Scholar
  41. 41.
    M.A. Nowak, K. Sigmund, Evolution of indirect reciprocity. Nature 437, 1291–1298 (2005)Google Scholar
  42. 42.
    M.J. Mauboussin, Revisiting market efficiency: The stock market as a complex adaptive system. J. Appl. Corp. Fin. 14, 47–55 (2005)Google Scholar
  43. 43.
    J. Stiglitz, There is no invisible hand. The Guardian (December 20, 2002), see http://www.guardian.co.uk/education/2002/dec/20/highereducation.uk1
  44. 44.
    D. Helbing, T. Vicsek, Optimal self-organization. New J. Phys. 1, 13.1–13.17 (1999)Google Scholar
  45. 45.
    D. Helbing, Traffic and related self-driven many-particle systems. Rev. Mod. Phys. 73, 1067–1141 (2001)Google Scholar
  46. 46.
    A. Johansson, D. Helbing, H.Z. A-Abideen, S. Al-Bosta, From crowd dynamics to crowd safety: A video-based analysis. Advances in Complex Systems 11(4), 497–527 (2008)Google Scholar
  47. 47.
    W. Michiels, S.-I. Niculescu, Stability and Stabilization of Time-Delay Systems. (siam—Society for Industrial and Applied Mathematics, Philadelphia, 2007)Google Scholar
  48. 48.
    D. Helbing, U. Witt, S. Lämmer, T. Brenner, Network-induced oscillatory behavior in material flow networks and irregular business cycles. Phys. Rev. E 70, 056118 (2004)Google Scholar
  49. 49.
    G. Hardin, The tragedy of the commons. Science 162, 1243–1248 (1968)Google Scholar
  50. 50.
    E. Fehr, S. Gächter, Altruistic punishment in humans. Nature 415, 137–140 (2002)Google Scholar
  51. 51.
    T. Preis, H.E. Stanley, Switching phenomena in a system with no switches. J. Stat. Phys. (JSTAT) 138, 431–446 (2010)Google Scholar
  52. 52.
    G.A. Akerlof, R.J. Shiller, Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism. (Princeton University Press, 2010)Google Scholar
  53. 53.
    J.D. Sterman, Testing behavioral simulation models by direct experiment. Manag. Sci. 33(12), 1572–1592 (1987)Google Scholar
  54. 54.
    D. Helbing, A mathematical model for behavioral changes by pair interactions. pp. 330–348 In: G. Haag, U. Mueller, and K.G. Troitzsch (eds.) Economic Evolution and Demographic Change. Formal Models in Social Sciences. (Springer, Berlin, 1992)Google Scholar
  55. 55.
    H.N. Agiza, G.I. Bischib, M. Kopel, Multistability in a dynamic Cournot game with three oligopolists. Math. Comput. Simulat. 51, 63–90 (1999)Google Scholar
  56. 56.
    D. Helbing, A. Szolnoki, M. Perc, G. Szabo, Evolutionary establishment of moral and double moral standards through spatial interactions. PLoS Comput. Biol. 6(4), e1000758 (2010)Google Scholar
  57. 57.
    A.J. Lotka Elements of Mathematical Biology. (Dover, New York, 1956)Google Scholar
  58. 58.
    E. Hopf, Abzweigungen einer periodischen Lösung von einer stationären Lösung eines Differentialgleichungssystems. Math. Naturwiss. Klasse 94, 1ff (1942)Google Scholar
  59. 59.
    D. Helbing, Dynamic decision behavior and optimal guidance through information services: Models and experiments. pp. 47–95 In: M. Schreckenberg, R. Selten (eds.) Human Behaviour and Traffic Networks (Springer, Berlin, 2004)Google Scholar
  60. 60.
    H. Gintis, The dynamics of general equilibrium. Econ. J. 117, 1280–1309 (2007)Google Scholar
  61. 61.
    C.H. Hommes, Modeling the stylized facts in finance through simple nonlinear adaptive systems. Proc. Natl. Acad. Sc. USA (PNAS) 99, Suppl. 3, 7221–7228 (2002)Google Scholar
  62. 62.
    The quotes were presented by Jorgen Vitting Andersen in his talk “Predicting moments of crisis in physics and finance” during the workshop “Windows to Complexity” in Münster, Germany, on June 11, 2010Google Scholar
  63. 63.
    Turing A.M., The chemical basis of morphogenesis. Phil. Trans. Roy. Soc. Lond. B 237, 37–72 (1952)Google Scholar
  64. 64.
    J.D. Murray, Lectures on Nonlinear Differential Equation-Models in Biology. (Clanderon Press, Oxford, 1977)Google Scholar
  65. 65.
    W. Weidlich, M. Munz, Settlement formation I: A dynamic theory, Ann. Reg. Sci. 24, 83–106 (2000); Settlement formation II: Numerical simulation, Ann. Reg. Sci. 24, 177–196 (2000)Google Scholar
  66. 66.
    D. Helbing, T. Platkowski, Self-organization in space and induced by fluctuations. Int. J. Chaos Theor. Appl. 5(4), 47–62 (2000)Google Scholar
  67. 67.
    J. Mimkes, Stokes integral of economic growth: Calculus and the Solow model. Physica A 389(8), 1665–1676 (2010)Google Scholar
  68. 68.
    O.J. Blanchard, L.H. Summers, Hysteresis and the European unemployment problem. NBER Macroecon. Annu. 1, 15–78 (1986)Google Scholar
  69. 69.
    M. Cross, H. Greenside, Pattern Formation and Dynamics in Nonequilibrium Systems. (Cambridge University, 2009)Google Scholar
  70. 70.
    D. Sornette, Critical Phenomena in Natural Sciences: Chaos, Fractals, Selforganization and Disorder. (Springer, Berlin, 2006)Google Scholar
  71. 71.
    A. Bunde, J. Kropp, H.-J. Schellnhuber (eds.) The Science of Disasters. (Springer, Berlin, 2002)Google Scholar
  72. 72.
    W. Horsthemke, R. Lefever, Noise-Induced Transitions: Theory and Applications in Physics, Chemistry, and Biology. (Springer, Berlin, 1983)Google Scholar
  73. 73.
    P. Reimann, Brownian motors: noisy transport far from equilibrium. Phys. Rep. 361, 57–265 (2002)Google Scholar
  74. 74.
    E.C. Zeeman (eds.) Catastrophe Theory (Addison-Wesley, London, 1977)Google Scholar
  75. 75.
    H.E. Stanley, Introduction to Phase Transitions and Critical Phenomena. (Oxford University, 1987)Google Scholar
  76. 76.
    D. Helbing, S. Lämmer, Managing complexity: An introduction. Pages 1–16 In: D. Helbing (eds.) Managing Complexity: Insights, Concepts, Applications. (Springer, Berlin, 2008)Google Scholar
  77. 77.
    D. Helbing, Systemic risks in society and economics. SFI Working Paper 09-12-044, see http://www.santafe.edu/media/workingpapers/09-12-044.pdf
  78. 78.
    J. Lorenz, S. Battiston, F. Schweitzer Systemic risk in a unifying framework for cascading processes on networks. Eur. Phys. J. B 71(4), 441–460 (2009)Google Scholar
  79. 79.
    P. Bak, How Nature Works: The Science of Self-Organized Criticality. (Springer, Berlin, 1999)Google Scholar
  80. 80.
    Knowledge@Wharton, Why economists failed to predict the financial crisis (May 13, 2009), see http://knowledge.wharton.upenn.edu/article.cfm?articleid=2234 or http://www.ftpress.com/articles/article.aspx?p=1350507
  81. 81.
    W. Weidlich, Physics and social science—the approach of synergetics. Phys. Rep. 204(1), 1–163 (1991)Google Scholar
  82. 82.
    T. Puu, Nonlinear Economic Dynamics (Lavoisier, 1991); Attractors, Bifurcations, & Chaos. Nonlinear Phenomena in Economics (Springer, Berlin, 2003)Google Scholar
  83. 83.
    H.W. Lorenz, Nonlinear Dynamical Equations and Chaotic Economy. (Springer, Berlin, 1993)Google Scholar
  84. 84.
    P. Krugman, The Self-Organizing Economy. (Blackwell, 1996)Google Scholar
  85. 85.
    F. Schweitzer (ed.) Self-Organization of Complex Structures: From Individual to Collective Dynamics (CRC Press, 1997); Modeling Complexity in Economic and Social Systems (World Scientific, 2002)Google Scholar
  86. 86.
    R.H. Day, Complex Economic Dynamics. (MIT Press, Vol. 1: 1998; Vol. 2: 1999)Google Scholar
  87. 87.
    W. Weidlich, Sociodynamics: A Systematic Approach to Mathematical Modelling in the Social Sciences. (CRC Press, Boca Raton, 2000)Google Scholar
  88. 88.
    J.D. Sterman, Business Dynamics: Systems Thinking and Modeling for a Complex World. (McGraw Hill, 2000)Google Scholar
  89. 89.
    W.A. Brock, Growth Theory, Non-Linear Dynamics and Economic Modelling. (Edward Elgar, 2001)Google Scholar
  90. 90.
    S.Y. Auyang, Foundations of Complex-System Theories. (Cambridge University, 1998)Google Scholar
  91. 91.
    M. Salzano, D. Colander (eds.) Complexity Hints for Economic Policy. (Springer, Berlin, 2007)Google Scholar
  92. 92.
    D. Delli Gatti, E. Gaffeo, M. Gallegati, G. Giulioni, A. Palestrini, Emergent Economics. (Springer, Berlin, 2008)Google Scholar
  93. 93.
    M. Faggini, T. Lux (eds.) Coping with the Complexity of Economics. (Springer, Berlin, 2009)Google Scholar
  94. 94.
    J.B. Rosser, Jr., K.L. Cramer, J. Madison (eds.) Handbook of Research on Complexity. (Edward Elgar Publishers, 2009)Google Scholar
  95. 95.
    M. Olson, The rise and decline of nations: economic growth, stagflation, and social rigidities. (Yale University Press, New Haven, 1982)Google Scholar
  96. 96.
    R. Axelrod, The Evolution of Cooperation. (Basic Books, New York, 1984)Google Scholar
  97. 97.
    J.C. Harsanyi, R. Selten, A General Theory of Equilibrium Selection. (MIT Press, Cambridge, MA, 1988)Google Scholar
  98. 98.
    D. Helbing, S. Lozano, Phase transitions to cooperation in the prisoner’s dilemma. Phys. Rev. E 81(5), 057102 (2010)Google Scholar
  99. 99.
    M.A. Nowak, R.M. May, Evolutionary games and spatial chaos. Nature 359, 826–829 (1992)Google Scholar
  100. 100.
    F.C. Santos, M.D. Santos, J.M. Pacheco, Social diversity promotes the emergence of cooperation in public goods games. Nature 454, 213–216 (2008)Google Scholar
  101. 101.
    D. Helbing, W. Yu, The outbreak of cooperation among success-driven individuals under noisy conditions. Proc. Natl. Acad Sci. USA (PNAS) 106(8), 3680–3685 (2009)Google Scholar
  102. 102.
    E. Ostrom, Governing the Commons. The Evolution of Institutions for Collective Action. (Cambridge University, New York, 1990)Google Scholar
  103. 103.
    C.P. Roca, S. Lozano, A. Arenas, A. Sanchez, Topological traps control flow on real networks: The case of coordination failures, PLoS One 5(12), e15210 (2010)Google Scholar
  104. 104.
    D. Helbing, Modelling supply networks and business cycles as unstable transport phenomena. New J. Phys. 5, 90.1–90.28 (2003)Google Scholar
  105. 105.
    M. Eigen, The selforganization of matter and the evolution of biological macromolecules. Naturwissenschaften 58, 465–523 (1971)Google Scholar
  106. 106.
    J. Schumpeter, The Theory of Economic Development (Harvard University Press, Cambridge, 1934)Google Scholar
  107. 107.
    Ken Arrow, In: D. Colander, R.P.F. Holt, J. Barkley Rosser (eds.) The Changing Face of Economics. Conversations with Cutting Edge Economists (The University of Michigan Press, Ann Arbor, 2004), p. 301.Google Scholar
  108. 108.
    M. Gallegati, A. Kirman, Beyond the Representative Agent. (Edward Elgar, Cheltenham, 1999)Google Scholar
  109. 109.
    A. Kirman, Economics with Heterogeneous Interacting Agents. (Springer, Berlin, 2001).Google Scholar
  110. 110.
    M. Aoki, Modeling Aggregate Behavior and Fluctuations in Economics. (Cambridge University, Cambridge, 2002)Google Scholar
  111. 111.
    D. Helbing, Collection of papers on An Analytical Theory of Traffic Flows in Eur. Phys. J. B, see http://www.soms.ethz.ch/research/traffictheory
  112. 112.
    W.B. Arthur, S.N. Durlauf, D. Lane (eds.) The Economy as An Evolving Complex System II. (Santa Fe Institute Series, Westview Press, 1997)Google Scholar
  113. 113.
    L.E. Blume, S.N. Durlauf, (eds.) The Economy as an Evolving Complex System III. (Oxford University, Oxford, 2006)Google Scholar
  114. 114.
    U. Witt, Evolutionary economics. pp. 67–73 In: S.N. Durlauf, L.E. Blume (eds.) The New Palgrave Dictionary of Economics. (Palgrave Macmillan, 2008)Google Scholar
  115. 115.
    R.M. May, S.A. Levin, G. Sugihara, Ecology for bankers. Nature 451, 894–895 (2008)Google Scholar
  116. 116.
    E.D. Beinhocker, Origin of Wealth: Evolution, Complexity, and the Radical Remaking of Economics. (Harvard Business Press, 2007)Google Scholar
  117. 117.
    R.A. Young, R.L. Giese (eds.), Introduction to Forest Ecosystem Science and Management. (Wiley, New York, 2002)Google Scholar
  118. 118.
    H. Meinhardt, Models of Biological Pattern Formation. (Academic Press, London, 1982)Google Scholar
  119. 119.
    M. Caramia, P. Dell’Olmo, Multi-objective Management in Freight Logistics: Increasing Capacity, Service Level and Safety with Optimization Algorithms. (Springer, New York, 2008)Google Scholar
  120. 120.
    Jean-Philippe Rennard (eds.) Handbook of Research on Nature-Inspired Computing for Economics and Management. (IGI Global, 2006)Google Scholar
  121. 121.
    S. Lämmer, D. Helbing, Self-control of traffic lights and vehicle flows in urban road networks. J. Stat. Mech. (JSTAT), P04019 (2008)Google Scholar
  122. 122.
    D. Helbing et al., Biologistics and the struggle for efficiency: Concepts and perspectives. Adv. Comp. Syst. 12(6), 533–548 (2009)Google Scholar
  123. 123.
    D. Helbing, M. Moussaid, Analytical calculation of critical perturbation amplitudes and critical densities by non-linear stability analysis of a simple traffic flow model. Eur. Phys. J. B 69(4), 571–581 (2009)Google Scholar
  124. 124.
    K. Windt, T. Philipp, F. Böse, Complexity cube for the characterization of complex production systems. Int. J. Comput. Integrated Manuf. 21(2), 195–200 (2007)Google Scholar
  125. 125.
    D. Helbing, S. Lämmer, Verfahren zur Koordination konkurrierender Prozesse oder zur Steuerung des Transports von mobilen Einheiten innerhalb eines Netzwerkes (Method for coordination of concurrent processes for control of the transport of mobile units within a network), Patent WO/2006/122528 (2006)Google Scholar
  126. 126.
    I. Kondor, S. Pafka, G. Nagy, Noise sensitivity of portfolio selection under various risk measures. J. Bank. Fin. 31(5), 1545–1573 (2007)Google Scholar
  127. 127.
    D. Dorner, The Logic of Failure: Recognizing and Avoiding Error in Complex Situations. (Basic, New York, 1997)Google Scholar
  128. 128.
    K. Kempf, Complexity and the enterprise: The illusion of control. pp. 57–87 In: D. Helbing (eds.) Managing Complexity: Insights, Concepts, Applications (Springer, Berlin, 2008)Google Scholar
  129. 129.
    D. Sornette, Nurturing breakthroughs: lessons from complexity theory. J. Econ. Interaction and Coordination 3, 165–181 (2008)Google Scholar
  130. 130.
    C.A.E. Goodhart, Monetary Relationships: A View from Threadneedle Street. (Papers in Monetary Economics, Reserve Bank of Australia, 1975); For applications of Le Chatelier’s principle to economics see also P. A. Samuelson, Foundations of Economic Analysis. (Harvard University, 1947)Google Scholar
  131. 131.
    A.P. Fiske, Structures of Social Life: The Four Elementary Forms of Human Relations. (The Free Press, 1993)Google Scholar
  132. 132.
    E.G. Clary et al., Understanding and assessing the motivations of volunteers: a functional approach. J. Pers. Soc. Psychol. 74(6), 1516–1530 (1998)Google Scholar
  133. 133.
    B.S. Frey, Happiness: A Revolution in Economics. (MIT Press, Cambridge, MA and London, UK, 2008)Google Scholar
  134. 134.
    J. Surowiecki, The Wisdom of Crowds: Why the Many Are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations. (Doubleday, 2004)Google Scholar
  135. 135.
    C. Blum, D. Merkle (eds.), Swarm Intelligence. Introduction and Applications. (Springer, Berlin, 2008)Google Scholar
  136. 136.
    B.S. Frey, Economics as a Science of Human Behaviour: Towards a New Social Science Paradigm. (Springer, Berlin, 1999)Google Scholar
  137. 137.
    L.M.A. Bettencourt, J. Lobo, D. Helbing, C. Khnert, G.B. West, Growth, innovation, scaling and the pace of life in cities. Proc. Natl. Acad. Sci. USA (PNAS) 104, 7301–7306 (2007)Google Scholar
  138. 138.
    R.R. McDaniel, Jr. D.J. Driebe (eds.), Uncertainty and Surprise in Complex Systems. (Springer, Berlin, 2005)Google Scholar
  139. 139.
    A.-L. Barabasi, Scale-free networks: A decade and beyond. Science 325, 412–413 (2009)Google Scholar
  140. 140.
    F. Schweitzer et al., Economic networks: The new challenges. Science 325, 422–425 (2009)Google Scholar
  141. 141.
    D. Lazer et al., Computational social science. Science 323, 721–723 (2009)Google Scholar
  142. 142.
    E. Bruckner, W. Ebeling, M.A.J. Montano, A. Scharnhorst, Hyperselection and innovation described by a stochastic model of technological evolution; pP. 79–90 In: L.L. Leydesdorff, P. van den Besselaar (eds.) Evolutionary Economics and Chaos Theory: New Directions in Technology Studies. (Pinter, London, 1994)Google Scholar
  143. 143.
    R.N. Mantegna, H.E. Stanley, Introduction to Econophysics: Correlations and Complexity in Finance. (Cambridge University Press, Cambridge, 2000).Google Scholar
  144. 144.
    D. Challet, M. Marsili, Y.-C. Zhang, Minority Games: Interacting Agents in Financial Markets. (Oxford University, Oxford, New York, 2005)Google Scholar
  145. 145.
    J.-P. Bouchaud, Economics needs a scientific revolution. Nature 455, 1181 (2008)Google Scholar
  146. 146.
    J.D. Farmer, D. Foley, The economy needs agent-based modelling. Nature 460, 685–686 (2009)Google Scholar
  147. 147.
    M. Buchanan, Meltdown modelling. Nature 460, 680–682 (2009)Google Scholar
  148. 148.
    E.W. Montroll, W.W. Badger, Introduction to Quantitative Aspects of Social Phenomena. (Gordon and Breach, 1974)Google Scholar
  149. 149.
    Y.-C. Zhang, Econophysics Forum, see http://www.unifr.ch/econophysics
  150. 150.
    B.M. Roehner, Fifteen years of econophysics: worries, hopes and prospects (2010), see http://arxiv.org/abs/1004.3229
  151. 151.
    M. Gallegati, S. Keen, T. Lux, P. Omerod, Worrying trends in econophysics. Physica A 370, 1–6 (2006)Google Scholar
  152. 152.
    B.K. Chakrabarti, A. Chakraborti, A. Chatterjee (eds.) Econophysics and Sociophysics: Trends and Perspectives. (Wiley VCH, Weinheim, 2006)Google Scholar
  153. 153.
    Aims and Scope of the Physics of Socio-Economic Systems Division of the German Physical Society, see http://www.dpg-physik.de/dpg/gliederung/fv/soe/aims.html. For past events see http://www.dpg-physik.de/dpg/gliederung/fv/soe/veranstaltungen/vergangene.html
  154. 154.
    D. Helbing, Pluralistic modeling of complex systems. Preprint http://arxiv.org/abs/1007.2818 (2010)
  155. 155.
    L. Tesfatsion, K.L. Judd (eds.), Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics. (North-Holland, Amsterdam, 2006)Google Scholar
  156. 156.
    J.R. Harrison, Z. Lin, G.R. Carroll, K.M. Carley, Simulation modeling in organizational and management research. Acad. Manag. Rev. 32(4), 1229–1245 (2007)Google Scholar
  157. 157.
    J.P. Davis, K.M. Eisenhardt, C.B. Bingham, Developing theory through simulation methods. Acad. Manag. Rev. 32(2), 480–499 (2007)Google Scholar
  158. 158.
    T. Schelling, Micromotives and Macrobehavior. (Norton, New York, 1978)Google Scholar
  159. 159.
    R. Hegselmann, A. Flache, Understanding complex social dynamics: A plea for cellular automata based modelling. J. Artif. Soc. Soc. Simul. 1, no. 3, see http://www.soc.surrey.ac.uk/JASSS/1/3/1.html
  160. 160.
    N. Gilbert, S. Bankes, Platforms and methods for agent-based modeling. PNAS 99(3), 7197–7198 (2002)Google Scholar
  161. 161.
    M.W. Macy, R. Willer, From factors to actors: Computational sociology and agent-based modeling. Annu. Rev. Sociol. 28, 143–166 (2002)Google Scholar
  162. 162.
    R.K. Sawyer, Artificial societies. Multiagent systems and the micro-macro link in sociological theory. Socio. Meth. Res. 31(3), 325–363 (2003)Google Scholar
  163. 163.
    J.M. Epstein, Generative Social Science: Studies in Agent-Based Computational Modeling. (Princeton University, 2007)Google Scholar
  164. 164.
    J.H. Kagel, A.E. Roth, The Handbook of Experimental Economics. (Princeton University, 1997)Google Scholar
  165. 165.
    D. Helbing, Managing Complexity: Concepts, Insights, Applications. (Springer, Berlin, 2008)Google Scholar
  166. 166.
    L. Hurwicz, S. Reiter, Designing Economic Mechanisms. (Cambridge University, Cambridge, 2006)Google Scholar
  167. 167.
    W.G. Sullivan, E.M. Wicks, C.P. Koelling, Engineering Economy. (Prentice Hall, Upper Saddle River, NJ, 2008)Google Scholar
  168. 168.
    D. Helbing, Quantitative Sociodynamics. (Kluwer Academic, Dordrecht, 1995)Google Scholar
  169. 169.
    C. Castellano, S. Fortunato, V. Loreto, Statistical physics of social dynamics. Rev. Mod. Phys. 81, 591–646 (2009)Google Scholar
  170. 170.
    D. Helbing, W. Yu (2010) The future of social experimenting. Proc. Natl. Acad. Sci. USA (PNAS) 107(12), 5265–5266 (2010); see http://www.soms.ethz.ch/research/socialexperimentingforalongerversion.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Dirk Helbing
    • 1
  1. 1.CLU E1ETH ZurichZurichSwitzerland

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