Skip to main content
SpringerLink
Log in

Randomness, Emergence and Causation: A Historical Perspective of Simulation in the Social Sciences

  • Conference paper
  • First Online:
Complexity and Emergence (CEIM 2018)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 383))

  • 463 Accesses

Abstract

This chapter is a review of a selection of simulation models, with special reference to the social sciences. Three critical aspects are identified—i.e. randomness, emergence and causation—that may help understand the evolution and the main characteristics of these simulation models. Several examples illustrate the concepts through a historical perspective.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    In English, “it is more effective [...] to schematize the phenomenon by isolating the actions that one wants to examine and assuming they behave independently, irrespectively of the others” (our translation).

  2. 2.

    In English: “So, in the end chance lies [...] in the eye of the observer” [44, p. 4].

  3. 3.

    The sentence is often misquoted replacing the obsolete “publick” with the more modern “public”.

  4. 4.

    The article containing this quotation became the Préface of the second edition of Les Règles de la méthode sociologique [39], and is generally quoted as such (despite the article is antecedent); the sentence is not in the first, 1895, edition. In English:

    The solidity of bronze lies neither in the copper, nor in the tin, nor in the lead which have been used to form it, which are all soft or malleable bodies. The solidity arises from the mixing of the two. The liquidity of water, its nutritive and other properties, are not in the two gases of which it is composed, but in the complex substance they form by coming together. [...Social facts] reside in the society itself that produces them and not in its parts, namely, its members. [40, pp. 39–40]

    It is difficult to say whether Durkheim was aware of Huxley’s example, but he was surely well acquainted with the work of Huxley’s friend, Herbert Spencer (see [45]), on social organisms. By the way, the metaphor of water is used in [45, p. 96] to describe the shorthand system developed by William George Spencer, Herbert Spencer’s father.

  5. 5.

    These pages by Mayr contain some mistakes. First, the book of Lloyd Morgan cited by Mayr is probably the one from 1923 [111], not from 1894, as emergence starts appearing in his work from 1912 (see [12, p. 59]). Second, the quotation just after that is not by Morgan but is taken from the book [121, p. 72] where it is used to illustrate the reasoning in [112, p. 59].

  6. 6.

    It is a bit odd to attach this aspect to agents, but we want to highlight that agents can be characterized as complex; see below and [42].

  7. 7.

    It is worth noting that ABM can also be backed by equations, better, by a mix of equations and object-based modeling. Actually, we are not aware of ABM that do not have any equation embedded in their coding. The difference of this approach is in the ability to mix and mash both object- and equation-based techniques.

  8. 8.

    We refer to [46, 47, 63, 77, 86] for a detailed discussion on the differences between ACE and DSGE.

References

  1. Abelson, R.P., Bernstein, A.: A computer simulation model of community referendum controversies. Public Opin. Q. 27(1), 93 (1963)

    Article  Google Scholar 

  2. Aggarwal, V.A., Siggelkow, N., Singh, H.: Governing collaborative activity: interdependence and the impact of coordination and exploration. Strateg. Manag. J. 32(7), 705–730 (2011)

    Article  Google Scholar 

  3. Anderson, P.W.: More is different. Science 177(4047), 393–396 (1972)

    Google Scholar 

  4. Axelrod, R.: The dissemination of culture: a model with local convergence and global polarization. J. Conflict Resolut. 41(2), 203–226 (1997)

    Article  Google Scholar 

  5. Axelrod, R., Tesfatsion, L.: Appendix A: a guide for newcomers to agent-based modeling in the social sciences. In: Tesfatsion, L., Judd, K.L. (eds.) Handbook of Computational Economics, vol. 2, pp. 1647–1659. Elsevier (2006)

    Google Scholar 

  6. Banks, J., Carson, J.S. II, Nelson, B.L., Nicol, D.M.: Discrete-Event System Simulation, 4th edn. Prentice-Hall International Series in Industrial and Systems Engineering. Pearson Prentice Hall, Upper Saddle River, NJ (2005)

    Google Scholar 

  7. Bardone, E., Secchi, D.: Inquisitiveness: distributing rational thinking. Team Perform. Manag. Int. J. 23(1/2), 66–81 (2017)

    Article  Google Scholar 

  8. Baumann, O., Schmidt, J., Stieglitz, N.: Effective search in rugged performance landscapes: a review and outlook. J. Manag. 45(1), 285–318 (2019)

    Google Scholar 

  9. Berlekamp, E.R., Conway, J.H., Guy, R.K.: Winning Ways for Your Mathematical Plays, 2nd edn. A.K. Peters, Natick, MA (2001)

    Google Scholar 

  10. Berto, F., Tagliabue, J.: Cellular automata. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University, Fall (2017)

    Google Scholar 

  11. Black, A.J., McKane, A.J.: Stochastic formulation of ecological models and their applications. Trends Ecol. Evol. 27(6), 337–345 (2012)

    Article  Google Scholar 

  12. Blitz, D.: Emergent Evolution: Qualitative Novelty and the Levels of Reality. Springer, Dordrecht (2010)

    Google Scholar 

  13. Bradbury, R.: A sound of thunder. Collier’s 28, 20–21, 60–61 (1952)

    Google Scholar 

  14. Breslin, D., Romano, D., Percival, J.: Conceptualizing and modeling multi-level organizational co-evolution. In: Secchi, D., Neumann, M. (eds.) Agent-Based Simulation of Organizational Behavior. New Frontiers of Social Science Research, pp. 137–157. Springer International Publishing, Cham (2016)

    Google Scholar 

  15. Brown, R.G.: Dieharder: A Random Number Test Suite (2019)

    Google Scholar 

  16. Buffon, G.-L.L.: Geometrie [Résolution des problémes qui regardent le jeu du franc-carreau]. Histoire de l’Académie Royale des Sciences, Année 1733, 43–45 (1735)

    Google Scholar 

  17. Buffon, G.-L.L.: Essais d’Arithmétique morale. In: Histoire Naturelle, Générale et Particulière, Supplément, Tome Quatrième, pp. 46–123. Imprimerie Royale, Paris (1777)

    Google Scholar 

  18. Bunge, M.: Mechanism and explanation. Philos. Soc. Sci. 27(4), 410–465 (1997)

    Article  Google Scholar 

  19. Burdick, E.: The 480. McGraw Hill, New York, NY (1964)

    Google Scholar 

  20. Caiani, A., Godin, A., Caverzasi, E., Gallegati, M., Kinsella, S., Stiglitz, J.E.: Agent based-stock flow consistent macroeconomics: towards a benchmark model. J. Econ. Dyn. Control 69, 375–408 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  21. Cohen, M.D., March, J.G., Olsen, J.P.: A garbage can model of organizational choice. Adm. Sci. Q. 17(1), 1 (1972)

    Article  Google Scholar 

  22. Colander, D., Howitt, P., Kirman, A., Leijonhufvud, A., Mehrling, P.: Beyond DSGE models: toward an empirically based macroeconomics. Am. Econ. Rev. 98(2), 236–240 (2008)

    Article  Google Scholar 

  23. Coleman, J.S.: Social theory, social research, and a theory of action. Am. J. Sociol. 91(6), 1309–1335 (1986)

    Google Scholar 

  24. Coleman, J.S.: Foundations of Social Theory. Belknap Press of Harvard University Press, Cambridge, MA (1990)

    Google Scholar 

  25. Conte, R.: Agent-based modeling for understanding social intelligence. Proc. Natl. Acad. Sci. 99(suppl 3), 7189–7190 (2002)

    Article  Google Scholar 

  26. Conte, R., Paolucci, M.: On agent-based modeling and computational social science. Front. Psychol. 5 (2014)

    Google Scholar 

  27. RAND Corporation (ed.): A Million Random Digits with 100,000 Normal Deviates. Free Press, Glencoe, IL (1955)

    Google Scholar 

  28. Courchamp, F., Jaric, I., Albert, C., Meinard, Y., Ripple, W.J., Chapron, G.: The paradoxical extinction of the most charismatic animals. PLOS Biol. 16(4), e2003997 (2018)

    Google Scholar 

  29. Cunningham, B.: The reemergence of ‘emergence’. Philos. Sci. 68, S62–S75 (2001)

    Article  Google Scholar 

  30. de Laplace, P.-S.: Théorie analytique des probabilités. Veuve Courcier, Paris (1812)

    MATH  Google Scholar 

  31. de Laplace, P.-S.: Essai philosophique sur les probabilités. Veuve Courcier, Paris (1814)

    MATH  Google Scholar 

  32. de Marchi, S., Page, S.E.: Agent-based models. Annu. Rev. Polit. Sci. 17(1), 1–20 (2014)

    Article  Google Scholar 

  33. De Morgan, A.: Supplement to the budget of paradoxes (No. IV). The Athenæum 2017, 835–836 (1866)

    Google Scholar 

  34. De Morgan, A.: A Budget of Paradoxes. Longmans, Green, and Co., London (1872)

    MATH  Google Scholar 

  35. Dieci, R., He, X.-Z.: Chapter 5: Heterogeneous agent models in finance. In: Hommes, C., LeBaron, B. (eds.) Handbook of Computational Economics, vol. 4, pp. 257–328. Elsevier (2018)

    Google Scholar 

  36. Doore, K., Fishwick, P.: Prototyping an analog computing representation of predator prey dynamics. In: Proceedings of the Winter Simulation Conference 2014, pp. 3561–3571a, Savannah, GA, December 2014. IEEE

    Google Scholar 

  37. Dorri, A., Kanhere, S.S., Jurdak, R.: Multi-agent systems: a survey. IEEE Access 6, 28573–28593 (2018)

    Article  Google Scholar 

  38. Durkheim, É.: De la Méthode Objective en Sociologie. Revue de Synthèse Historique II(1), 3–17 (1901)

    Google Scholar 

  39. Durkheim, É.: Les Règles de la méthode sociologique, revue et augmentée d’une préface nouvelle, 2nd edn. Bibliothèque de philosophie contemporaine, Alcan, Paris (1901)

    Google Scholar 

  40. Durkheim, É.: The Rules of Sociological Method. The Free Press, New York, NY (1982)

    Book  Google Scholar 

  41. Eckhardt, R.: Stan Ulam, John von Neumann, and the Monte Carlo method. Los Alamos Sci. 15(Special Issue), 131–137 (1987)

    Google Scholar 

  42. Edmonds, B., Moss, S.: From KISS to KIDS—an ‘Anti-simplistic’ modelling approach. In: Davidsson, P., Logan, B., Takadama, K. (eds.) Multi-Agent and Multi-Agent-Based Simulation, vol. 3415, pp. 130–144. Springer-Verlag, Berlin, Heidelberg (2005)

    Google Scholar 

  43. Ekeland, I.: Au hasard: la chance, la science et le monde. Seuil, Paris (1991)

    Google Scholar 

  44. Ekeland, I.: The Broken Dice, and Other Mathematical Tales of Chance. University of Chicago Press, Chicago, IL (1993)

    MATH  Google Scholar 

  45. Elwick, J.: Containing multitudes: Herbert Spencer, organisms social and orders of individuality. In: Francis M., Taylor, M.W. (eds.) Herbert Spencer: Legacies, pp. 89–110. Routledge, London (2014)

    Google Scholar 

  46. Fagiolo, G., Roventini, A.: Macroeconomic policy in DSGE and agent-based models Redux: new developments and challenges ahead. J. Artif. Soc. Soc. Simul. 20(1), 1 (2017)

    Article  Google Scholar 

  47. Farmer, J.D., Foley, D.: The economy needs agent-based modelling. Nature 460(7256), 685–686 (2009)

    Google Scholar 

  48. Figari, F., Paulus, A., Sutherland, H.: Chapter 24: Microsimulation and policy analysis. In: Atkinson, A.B., Bourguignon, F. (eds.) Handbook of Income Distribution, vol. 2B, pp. 2141–2221. Elsevier (2015)

    Google Scholar 

  49. Fioretti, G.: Agent-based simulation models in organization science. Organ. Res. Methods 16(2), 227–242 (2013)

    Article  Google Scholar 

  50. Fioretti, G.: Emergent organizations. In: Secchi, D., Neumann, M. (eds.) Agent-Based Simulation of Organizational Behavior. New Frontiers of Social Science Research, pp. 19–41. Springer International Publishing, Cham (2016)

    Chapter  Google Scholar 

  51. Fioretti, G., Lomi, A.: An agent-based representation of the garbage can model of organizational choice. J. Artif. Soc. Soc. Simul. 11(1), 1 (2008)

    Google Scholar 

  52. Fioretti, G., Lomi, A.: Passing the buck in the garbage can model of organizational choice. Comput. Math. Organ. Theory 16(2), 113–143 (2010)

    Article  Google Scholar 

  53. Forrester, J.W.: Principles of Systems. MIT Press, Cambridge, MA (1968)

    Google Scholar 

  54. Forrester, J.W.: Counterintuitive behavior of social systems. Technol. Forecast. Soc. Chang. 3, 1–22 (1971)

    Google Scholar 

  55. Forrester, J.W.: The beginning of system dynamics. McKinsey Q. 1995(4), 4–16 (1995)

    Google Scholar 

  56. Gardner, M.: The fantastic combinations of John Conway’s new solitaire game “life”. Sci. Am. 223(4), 120–123 (1970)

    Google Scholar 

  57. Gilbert, N., Pyka, A., Ahrweiler, P.: Innovation networks - a simulation approach. J. Artif. Soc. Soc. Simul. 4(3), 8 (2001)

    MATH  Google Scholar 

  58. Gilbert, N., Troitzsch, K.G.: Simulation for the Social Scientist, 2nd edn. Open University Press, Maidenhead, New York, NY (2005)

    Google Scholar 

  59. Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., Goss-Custard, J., Grand, T., Heinz, S.K., Huse, G., Huth, A., Jepsen, J.U., Jørgensen, C., Mooij, W.M., Müller, B., Pe’er, G., Piou, C., Railsback, S.F., Robbins, A.M., Robbins, M.M., Rossmanith, E., Rüger, N., Strand, E., Souissi, S., Stillman, R.A., Visser, R.V.U., DeAngelis, D.L.: A standard protocol for describing individual-based and agent-based models. Ecol. Model. 198(1–2), 115–126 (2006)

    Article  Google Scholar 

  60. Grimm, V., Revilla, E., Berger, U., Jeltsch, F., Mooij, W.M., Railsback, S.F., Thulke, H.-H., Weiner, J., Wiegand, T., DeAngelis, D.L.: Pattern-oriented modeling of agent-based complex systems: lessons from ecology. Science 310(5750), 987–991 (2005)

    Article  Google Scholar 

  61. Grow, A., Van Bavel, J. (eds.): Agent-Based Modelling in Population Studies. The Springer Series on Demographic Methods and Population Analysis, vol. 41. Springer International Publishing, Cham (2017)

    Google Scholar 

  62. Guerini, M., Moneta, A.: A method for agent-based models validation. J. Econ. Dyn. Control 82, 125–141 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  63. Guerini, M., Napoletano, M., Roventini, A.: No man is an Island: the impact of heterogeneity and local interactions on macroeconomic dynamics. Econ. Model. 68, 82–95 (2018)

    Article  Google Scholar 

  64. Hands, D.W.: Conundrums of the representative agent. Camb. J. Econ. 41(6), 1685–1704 (2017)

    Google Scholar 

  65. Hedström, P., Ylikoski, P.: Causal mechanisms in the social sciences. Ann. Rev. Sociol. 36(1), 49–67 (2010)

    Article  Google Scholar 

  66. Hedström, P., Ylikoski, P.: Analytical sociology and social mechanisms. In: Kaldis, B. (ed.) Encyclopedia of Philosophy and the Social Sciences, pp. 26–29. SAGE Publications, Cham (2013)

    Google Scholar 

  67. Hegselmann, R., Schelling, T.C., Sakoda, J.M.: The intellectual, technical, and social history of a model. J. Artif. Soc. Soc. Simul. 20(3) (2017)

    Google Scholar 

  68. Holland, J.H., Miller, J.H.: Artificial adaptive agents in economic theory. Am. Econ. Rev. 81(2), 365–370 (1991)

    Google Scholar 

  69. Hommes, C.H.: Chapter 23: Heterogeneous agent models in economics and finance. In: Tesfatsion, L., Judd, K.L. (eds.) Handbook of Computational Economics, vol. 2, pp. 1109–1186. Elsevier (2006)

    Google Scholar 

  70. Hommes, C.H., Wagener, F.: Chapter 4: Complex evolutionary systems in behavioral finance. In: Hens, T., Schenk-Hoppe, K. (eds.) Handbook of Financial Markets: Dynamics and Evolution, pp. 217–276. Elsevier (2009)

    Google Scholar 

  71. Huxley, T.H.: On the Physical Basis of Life. The College Courant, New Haven, CT (1869)

    Google Scholar 

  72. IUCN: Giraffa camelopardalis (amended version of 2016 assessment). In: The IUCN Red List of Threatened Species 2018: E.T9194A136266699. International Union for Conservation of Nature (2018)

    Google Scholar 

  73. Kauffman, S.A.: Cambrian explosion and Permian quiescence: implications of rugged fitness landscapes. Evol. Ecol. 3(3), 274–281 (1989)

    Article  Google Scholar 

  74. Kauffman, S.A., Weinberger, E.D.: The NK model of rugged fitness landscapes and its application to maturation of the immune response. J. Theor. Biol. 141(2), 211–245 (1989)

    Article  Google Scholar 

  75. Kirman, A.P.: The intrinsic limits of modern economic theory: the emperor has no clothes. Econ. J. 99(395), 126 (1989)

    Article  Google Scholar 

  76. Kirman, A.P.: Whom or what does the representative individual represent? J. Econ. Perspect. 6(2), 117–136 (1992)

    Article  Google Scholar 

  77. Kirman, A.P.: Ants and nonoptimal self-organization: lessons for macroeconomics. Macroecon. Dyn. 20(2), 601–621 (2016)

    Article  Google Scholar 

  78. Knudsen, T., Levinthal, D.A., Puranam, P.: Editorial: a model is a model. Strat. Sci. 4(1) (2019)

    Google Scholar 

  79. Kuperberg, M.: The two faces of emergence in economics. Sound. Interdiscip. J. 90(1/2), 49–63 (2007)

    Google Scholar 

  80. Kydland, F.E., Prescott, E.C.: Time to build and aggregate fluctuations. Econometrica 50(6), 1345 (1982)

    Article  MATH  Google Scholar 

  81. Lane, D.C.: The power of the bond between cause and effect: Jay Wright Forrester and the field of system dynamics. Syst. Dyn. Rev. 23(2–3), 95–118 (2007)

    Article  Google Scholar 

  82. Lawrence, P.R., Lorsch, J.W.: Differentiation and integration in complex organizations. Adm. Sci. Q. 12(1), 1–47 (1967)

    Article  Google Scholar 

  83. Lazzarini M.: Un’applicazione del calcolo della probabilità alla ricerca sperimentale di un valore approssimato di \(\pi \). Periodico di Matematica per l’insegnamento secondario IV(II), 140–143 (1901)

    Google Scholar 

  84. LeBaron, B.: Chapter 24: Agent-based computational finance. In: Tesfatsion, L., Judd, K.L. (eds.) Handbook of Computational Economics, vol. 2, pp. 1187–1233. Elsevier (2006)

    Google Scholar 

  85. LeBaron, B., Tesfatsion, L.: Modeling macroeconomies as open-ended dynamic systems of interacting agents. Am. Econ. Rev. 98(2), 246–250 (2008)

    Article  Google Scholar 

  86. Lengnick, M., Wohltmann, H.-W.: Agent-based financial markets and New Keynesian macroeconomics: a synthesis. J. Econ. Interac. Coord. 8(1), 1–32 (2013)

    Article  Google Scholar 

  87. Levinthal, D.A.: Adaptation on rugged landscapes. Manag. Sci. 43, 934–950 (1997)

    Article  MATH  Google Scholar 

  88. Long, J.B., Plosser, C.I.: Real business cycles. J. Polit. Econ. 91(1), 39–69 (1983)

    Article  Google Scholar 

  89. Lorenz, E.N.: Deterministic nonperiodic flow. J. Atmos. Sci. 20(2), 130–141 (1963)

    Article  MathSciNet  MATH  Google Scholar 

  90. Lotka, A.J.: Analytical note on certain rhythmic relations in organic systems. Proc. Natl. Acad. Sci. 6(7), 410–415 (1920)

    Article  Google Scholar 

  91. Lotka, A.J.: Elements of Physical Biology. Williams & Wilkins Company, Baltimore, MD (1925)

    Google Scholar 

  92. Macy, M.W., Willer, R.: From factors to actors: computational sociology and agent-based modeling. Ann. Rev. Sociol. 28(1), 143–166 (2002)

    Article  Google Scholar 

  93. Madsen, J.K., Bailey, R., Carrella, E., Koralus, P.: Analytic versus computational cognitive models: agent-based modeling as a tool in cognitive sciences. Current Dir. Psychol. Sci. 28(3), 299–305 (2019)

    Google Scholar 

  94. Maggi, E., Vallino, E.: Understanding urban mobility and the impact of public policies: the role of the agent-based models. Res. Transp. Econ. 55, 50–59 (2016)

    Article  Google Scholar 

  95. Maĭstrov, L.E.: Probability Theory: A Historical Sketch. Academic Press, New York, NY (1974)

    MATH  Google Scholar 

  96. Malerba, F., Nelson, R.R., Orsenigo, L., Winter, S.G.: History-friendly models of industry evolution: the computer industry. Ind. Corp. Change 8(1), 3–40 (1999)

    Google Scholar 

  97. Malerba, F., Nelson, R.R., Orsenigo, L., Winter, S.G.: History-friendly models: an overview of the case of the computer industry. J. Artif. Soc. Soc. Simul. 4(3) (2001)

    Google Scholar 

  98. Manzo, G.: Variables, mechanisms, and simulations: can the three methods be synthesized? A critical analysis of the literature. Rev. Fr. Sociol. 48(5), 35 (2007)

    Article  Google Scholar 

  99. March, J.G.: Exploration and exploitation in organizational learning. Organ. Sci. 2(1), 71–87 (1991)

    Article  Google Scholar 

  100. Marsaglia, G.: The Marsaglia random number CDROM including the DieHard battery of tests of randomness (1995)

    Google Scholar 

  101. Mäs, M., Flache, A., Takács, K., Jehn, K.A.: In the short term we divide, in the long term we unite: demographic crisscrossing and the effects of faultlines on subgroup polarization. Org. Sci. 24(3), 716–736 (2013)

    Google Scholar 

  102. Mayr, E.: The Growth of Biological Thought: Diversity, Evolution, and Inheritance. Harvard University Press, Cambridge, MA (1982)

    Google Scholar 

  103. McCloskey, D.N.: The Rhetoric of Economics. Rhetoric of the Human Sciences, 2nd edn. University of Wisconsin Press, Madison, WI (1998)

    Google Scholar 

  104. Meadows, D.H., Meadows, D.L., Randers, J., III Behrens, W.W. (eds.) The Limits to Growth: A Report for the Club of Rome’s Project on the Predicament of Mankind. Universe Books, New York, NY (1972)

    Google Scholar 

  105. Metropolis, N.: The beginning of the Monte Carlo method. Los Alamos Sci. 15(Special Issue), 125–130 (1987)

    Google Scholar 

  106. Metropolis, N., Ulam, S.: The Monte Carlo method. J. Amer. Statist. Assoc. 44, 335–341 (1949)

    Article  MathSciNet  MATH  Google Scholar 

  107. Mill, J.S.: A System of Logic, Ratiocinative and Inductive, vol. 1. John W. Parker, West Strand, London (1843)

    Google Scholar 

  108. Miller, J.H., Page, S.E.: Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton University Press, Princeton, NJ (2007)

    MATH  Google Scholar 

  109. Miller, K.D.: Agent-based modeling and organization studies: a critical realist perspective. Organ. Stud. 36(2), 175–196 (2015)

    Article  Google Scholar 

  110. Miller, K.D., Pentland, B.T., Choi, S.: Dynamics of performing and remembering organizational routines: performing and remembering organizational routines. J. Manage. Stud. 49(8), 1536–1558 (2012)

    Article  Google Scholar 

  111. Morgan, C.L.: Emergent Evolution: The Gifford Lectures, Delivered in the University of St. Andrews in the Year 1922. Williams and Norgate, London (1923)

    Google Scholar 

  112. Morgan, C.L.: The Emergence of Novelty. Williams & Norgate, London (1933)

    Google Scholar 

  113. Muth, J.F.: Rational expectations and the theory of price movements. Econometrica 29(3), 315 (1961)

    Article  Google Scholar 

  114. Nance, R.E.: A history of discrete event simulation programming languages. In: The Second ACM SIGPLAN Conference on History of Programming Languages, HOPL-II, pp. 149–175, New York, NY. ACM (1993)

    Google Scholar 

  115. Nance, R.E.: A history of discrete event simulation programming languages. In: History of Programming Languages—II, pp. 369–427. ACM, New York, NY (1996)

    Google Scholar 

  116. Ng, T., Wright, M.: Introducing the MONIAC: an early and innovative economic model. Reserve Bank of New Zealand: Bull. 70(4), 46–52 (2007)

    Google Scholar 

  117. Niazi, M., Hussain, A.: Agent-based computing from multi-agent systems to agent-based models: a visual survey. Scientometrics 89(2), 479–499 (2011)

    Article  Google Scholar 

  118. Nilsson, F., Darley, V.: On complex adaptive systems and agent-based modelling for improving decision-making in manufacturing and logistics settings: experiences from a packaging company. Int. J. Oper. Prod. Manag. 26(12), 1351–1373 (2006)

    Article  Google Scholar 

  119. Orcutt, G.H.: A new type of socio-economic system. Rev. Econ. Stat. 39(2), 116 (1957)

    Article  Google Scholar 

  120. Ostrom, E.: The ten most important books. Tidsskriftet Politik 4(7), 36–48 (2004)

    Google Scholar 

  121. Pantin, C.F.A.: Relations Between Sciences. Cambridge University Press, Cambridge (1968)

    Google Scholar 

  122. Pool, I.D.S., Abelson, R.P.: The simulmatics project. Publ. Opin. Quart. 25(2), 167, 22 (1961)

    Google Scholar 

  123. Pool, I.D.S., Abelson, R.P., Popkin, S.L.: Candidates, Issues and Strategies: A Computer Simulation of the 1960 and 1964 Presidential Elections. MIT Press, Cambridge, MA (1965)

    Google Scholar 

  124. Pyka, A., Fagiolo, G.: Agent-based modelling: a methodology for neo-schumpetarian economics. In: Elgar Companion to Neo-Schumpeterian Economics, pp. 467–488. Edward Elgar Publishing, Cheltenham (2007)

    Google Scholar 

  125. Railsback, S.F., Grimm, V.: Agent-Based and Individual-Based Modeling: A Practical Introduction. Princeton University Press, Princeton, NJ (2012)

    MATH  Google Scholar 

  126. Raub, W., Voss, T.: Micro-macro models in sociology: antecedents of Coleman’s diagram. In: Jann, B., Przepiorka, W. (eds.) Social Dilemmas, Institutions, and the Evolution of Cooperation. De Gruyter, Berlin, Boston, MA (2017)

    Google Scholar 

  127. Riedwyl, H.: Rudolf Wolf’s contribution to the Buffon needle problem (an early Monte Carlo experiment) and application of least squares. Am. Stat. 44(2), 138–139 (1990)

    MathSciNet  Google Scholar 

  128. Rosato, A., Prinz, F., Standburg, K.J., Swendsen, R.H.: Monte Carlo simulation of particulate matter segregation. Powder Technol. 49(1), 59–69 (1986)

    Article  Google Scholar 

  129. Rosato, A., Strandburg, K.J., Prinz, F., Swendsen, R.H.: Why the Brazil nuts are on top: size segregation of particulate matter by shaking. Phys. Rev. Lett. 58(10), 1038–1040 (1987)

    Article  MathSciNet  Google Scholar 

  130. Ruelle, D.: Chance and Chaos. Princeton University Press, Princeton, NJ (1993)

    Google Scholar 

  131. Schelling, T.C.: Models of segregation. Am. Econ. Rev. 59(2), 488–493 (1969)

    Google Scholar 

  132. Schelling, T.C.: Dynamic models of segregation. J. Math. Sociol. 1(2), 143–186 (1971)

    Article  MATH  Google Scholar 

  133. Schelling, T.C.: Micromotives and Macrobehavior. Fels Lectures on Public Policy Analysis. Norton, New York, NY (1978)

    Google Scholar 

  134. Secchi, D.: A case for agent-based models in organizational behavior and team research. Team Perform. Manag. Int. J. 21(1/2), 37–50 (2015)

    Article  Google Scholar 

  135. Secchi, D.: How do I Develop an Agent-Based Model? Edward Elgar Publishing, Cheltenham (2022)

    Google Scholar 

  136. Secchi, D., Neumann, M. (eds.): Agent-Based Simulation of Organizational Behavior. New Frontiers of Social Science Research. Springer International Publishing, Cham (2016)

    Google Scholar 

  137. Secchi, D., Seri, R.: Controlling for false negatives in agent-based models: a review of power analysis in organizational research. Comput. Math. Organ. Theory 23(1), 94–121 (2017)

    Article  Google Scholar 

  138. Segrè, E.: From X-Rays to Quarks: Modern Physicists and Their Discoveries. W. H. Freeman, San Francisco, CA (1980)

    Google Scholar 

  139. Sen, A.: Maximization and the act of choice. Econometrica 65(4), 745 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  140. Seri, R., Secchi, D.: How many times should one run a computational simulation? In: Edmonds, B., Meyer, R. (eds.) Simulating Social Complexity: A Handbook. Understanding Complex Systems, pp. 229–251. Springer International Publishing, Cham (2017)

    Chapter  Google Scholar 

  141. Smith, A.: An Inquiry into the Nature and Causes of the Wealth of Nations, vol. 2. Printed for W. Strahan and T. Cadell, in the Strand, London (1776)

    Google Scholar 

  142. Spanier, J.: Monte Carlo Methods. In: Nuclear Computational Science, pp. 117–165. Springer, Dordrecht (2010)

    Google Scholar 

  143. Squazzoni, F.: Agent-Based Computational Sociology. John Wiley & Sons Ltd, Chichester (2012)

    Book  Google Scholar 

  144. Sterman, J.D.: System dynamics modeling: tools for learning in a complex world. Calif. Manag. Rev. 43(4), 8–25 (2001)

    Article  Google Scholar 

  145. Tesfatsion, L.: Chapter 16: Agent-based computational economics: a constructive approach to economic theory. In: Handbook of Computational Economics, vol. 2, pp. 831–880. Elsevier (2006)

    Google Scholar 

  146. Troitzsch, K.G.: Perspectives and challenges of agent-based simulation as a tool for economics and other social sciences. In: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems, AAMAS ’09, vol. 1, pp. 35–42, Richland, SC, 2009. International Foundation for Autonomous Agents and Multiagent Systems

    Google Scholar 

  147. Tubaro, P., Casilli, A.A.: An ethnographic seduction: how qualitative research and agent-based models can benefit each other. Bull. Sociol. Methodol./Bull. Méthodol. Sociol. 106(1), 59–74 (2010)

    Google Scholar 

  148. Ulam, S.: On some mathematical problems connected with patterns of growth in figures. In: Bellman, R.E. (ed.) Mathematical Problems in the Biological Sciences. Number 14 in Proceedings of Symposia in Applied Mathematics, pp. 215–224. American Mathematical Society, Providence, RI (1962)

    Chapter  Google Scholar 

  149. van Bertalanffy, L.: General System Theory: Foundations, Development, Applications. Braziller, New York, NY (1968)

    Google Scholar 

  150. Vinković, D., Kirman, A.P.: A physical analogue of the Schelling model. Proc. Natl. Acad. Sci. 103(51), 19261–19265 (2006)

    Article  Google Scholar 

  151. Volterra, V.: Fluctuations in the abundance of a species considered mathematically. Nature 118(2972), 558–560 (1926)

    Article  MATH  Google Scholar 

  152. Volterra, V.: Variazioni e fluttuazioni del numero d’individui in specie animali conviventi. Atti della R. Accademia nazionale dei Lincei. Memorie della Classe di scienze fisiche, matematiche e naturali 2(III), 31–113 (1926)

    Google Scholar 

  153. von Neumann, J.: The general and logical theory of automata (with discussion). In: Jeffress, L.A. (ed.) Cerebral Mechanisms in Behaviour, pp. 1–41. Wiley, Chapman & Hall, New York, NY; London (1951)

    Google Scholar 

  154. von Neumann, J.: Various techniques used in connection with random digits. In: Householder, A.S., Forsythe, G.E., Germond, H.H. (eds.) Monte Carlo Method. National Bureau of Standards Applied Mathematics Series, vol. 12, Chap. 13, pp. 36–38. US Government Printing Office, Washington, DC (1951)

    Google Scholar 

  155. von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press, Princeton, NJ (1944)

    MATH  Google Scholar 

  156. Wall, F.: Agent-based modeling in managerial science: an illustrative survey and study. Rev. Manag. Sci. 10(1), 135–193 (2016)

    Article  Google Scholar 

  157. Wang, L., Ahn, K., Kim, C., Ha, C.: Agent-based models in financial market studies. J. Phys. Conf. Ser. 1039, 012022 (2018)

    Google Scholar 

  158. Weiss, G. (ed.): Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. MIT Press, Cambridge, MA (1999)

    Google Scholar 

  159. Wolf, R.: Versuche zur Vergleichung der Erfahrungswahrscheinlichkeit mit der mathematischen Wahrscheinlichkeit: Vierte Versuchsreihe. Mittheilungen der naturforschenden Gesellschaft in Bern 176, 85–88 (1850)

    Google Scholar 

  160. Yoon, M., Lee, K.: Agent-based and “History-Friendly” models for explaining industrial evolution. Evol. Inst. Econ. Rev. 6(1), 45–70 (2009)

    Google Scholar 

Download references

Acknowledgements

The second author gratefully acknowledges financial and logistic support from the DiECO, Università degli Studi dell’Insubria, during a visiting period. The third author acknowledges the PRIN Grant 2017 “How Good Is Your Model? Empirical Evaluation and Validation of Quantitative Models in Economics.” We thank Eugenio Caverzasi, Ernesto Carrella, Alan Kirman, Alessio Moneta, and Massimo Rusconi for useful feedback on the paper.

Author information

Authors and Affiliations

  1. DiECO, Università degli Studi dell’Insubria, Varese, 21100, Italy

    Raffaello Seri

  2. Department of Language and Communication, Centre for Computational & Organisational Cognition (CORG), University of Southern Denmark, Slagelse, 4200, Denmark

    Raffaello Seri & Davide Secchi

  3. Istituto di Economia and EMbeDS, Scuola Universitaria Superiore Sant’Anna, Pisa, 56127, Italy

    Mario Martinoli

Authors
  1. Raffaello Seri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Davide Secchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mario Martinoli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Raffaello Seri .

Editor information

Editors and Affiliations

  1. University of Bonn and Hausdorff Center for Mathematics, Bonn, Germany

    Sergio Albeverio

  2. Departments of Economics, Insubria University, Varese, Italy

    Elisa Mastrogiacomo

  3. Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milano, Italy

    Emanuela Rosazza Gianin

  4. Department of Mathematics, University of Milan, Milano, Italy

    Stefania Ugolini

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Seri, R., Secchi, D., Martinoli, M. (2022). Randomness, Emergence and Causation: A Historical Perspective of Simulation in the Social Sciences. In: Albeverio, S., Mastrogiacomo, E., Rosazza Gianin, E., Ugolini, S. (eds) Complexity and Emergence. CEIM 2018. Springer Proceedings in Mathematics & Statistics, vol 383. Springer, Cham. https://doi.org/10.1007/978-3-030-95703-2_7

Download citation

Publish with us

Policies and ethics

Search

Navigation