I discuss the concept of complexity and the burgeoning field of complex systems and their relevance to sociology. I begin by comparing and contrasting various definitions of complexity and then describe the attributes of systems capable of producing complexity: diversity, networked interactions, interdependent behavior, and adaptation. Next, I survey the contributions of complexity sciences with the most resonance with sociology. I organize those contributions into four categories: dynamics, aggregation, distributions, and functional properties of structure and diversity. On the basis of that survey, I conclude that incorporating complexity science into sociology requires the introduction of new models and methodologies as well as a more expansive approach to empirical research, and that the benefits of a deeper engagement with complexity will be substantial.

[Erratum, Closure]

An erratum has been published for this article:
What Sociologists Should Know About Complexity

Article metrics loading...

Loading full text...

Full text loading...


Literature Cited

  1. Adami C. 2002. What is complexity?. BioEssays 24:1085–94 [Google Scholar]
  2. Akers TA, Lanier MM. 2009. Epidemiological criminology: coming full circle. Am. J. Public Health 99:3397–402 [Google Scholar]
  3. Anderson P. 1972. More is different. Science 177:393–96 [Google Scholar]
  4. Arthur WB. 1994. Inductive reasoning and bounded rationality. Am. Econ. Rev. Pap. Proc. 84:406–11 [Google Scholar]
  5. Axelrod R. 1997. The dissemination of culture: a model with local convergence and global polarization. J. Confl. Resolut. 41:203–26 [Google Scholar]
  6. Bak P. 1996. How Nature Works: The Science of Self Organized Criticality New York: Springer-Verlag [Google Scholar]
  7. Bednar J. 2008. The Robust Federation: Principles of Design Cambridge, UK: Cambridge Univ. Press [Google Scholar]
  8. Bettencourt L, West G. 2010. A unified theory of urban living. Nature 467:912–13 [Google Scholar]
  9. Bourdieu P. 1984. Distinction: A Social Critique of the Judgement of Taste London: Routledge [Google Scholar]
  10. Bourdieu P. 1993. The Field of Cultural Production New York: Columbia Univ. Press [Google Scholar]
  11. Bruch E. 2014. How population structure shapes neighborhood segregation. Am. J. Sociol. 119:1221–78 [Google Scholar]
  12. Bruch E, Mare R. 2006. Neighborhood choice and neighborhood change. Am. J. Sociol. 112:667–709 [Google Scholar]
  13. Bruch E, Mare R. 2012. Methodological issues in the analysis of residential preferences and residential mobility. Sociol. Methodol. 42:103–54 [Google Scholar]
  14. Burt RS. 1992. Structural Holes: The Social Structure of Competition Cambridge, MA: Harvard Univ. Press [Google Scholar]
  15. Burt RS. 2005. Brokerage and Closure: An Introduction to Social Capital Oxford, UK: Oxford Univ. Press [Google Scholar]
  16. Byrne D, Callaghan G. 2014. Complexity Theory and the Social Sciences London: Routledge [Google Scholar]
  17. Carvalho V, Gabaix X. 2013. The great diversification and its undoing. Am. Econ. Rev. 103:51697–727 [Google Scholar]
  18. Castellani B, Hafferty F. 2009. Sociology and Complexity Science: A New Area of Inquiry Heidelberg, Ger.: Springer-Verlag [Google Scholar]
  19. Centeno MA, Nag M, Patterson TS, Shaver A, Windawi AJ. 2015. The emergence of global systemic risk. Annu. Rev. Sociol. 41:65–85 [Google Scholar]
  20. Centola D, Macy M. 2007. Complex contagions and the weakness of long ties. Am. J. Sociol. 113:702–34 [Google Scholar]
  21. Colander D, Kupers. 2014. Complexity and the Art of Public Policy: Solving Society's Problems from the Bottom Up Princeton, NJ: Princeton Univ. Press [Google Scholar]
  22. DeLanda M. 2006. A New Philosophy of Society: Assemblage Theory and Social Complexity London: Continuum Books [Google Scholar]
  23. Denrell J, Liu C. 2012. Top performers are not the most impressive when extreme performance indicates unreliability. PNAS 109:9331–36 [Google Scholar]
  24. Durlauf S, Ioannides Y. 2010. Social interactions. Annu. Rev. Econ. 2:451–78 [Google Scholar]
  25. Elliott M, Golub B, Jackson M. 2014. Financial networks and contagion. Am. Econ. Rev. 104:103115–53 [Google Scholar]
  26. Epstein J. 2007. Generative Social Science: Studies in Agent-Based Computational Modeling Princeton, NJ: Princeton Univ. Press [Google Scholar]
  27. Epstein J. 2014. Agent Zero: Toward Neurocognitive Foundations for Generative Social Science Princeton, NJ: Princeton Univ. Press [Google Scholar]
  28. Fisher A. 1987. Solving unsolvable problems with supercomputers. Popular Science May 1 48 [Google Scholar]
  29. Goldin I, Mariathasan M. 2014. The Butterfly Defect: How Globalization Creates Systemic Risks and What to Do About It Princeton, NJ: Princeton Univ. Press [Google Scholar]
  30. Golman R, Page SE. 2009. Basins of attraction and equilibrium selection under different learning rules. Evol. Econ. 20:49–72 [Google Scholar]
  31. Golub B, Jackson M. 2012. How homophily affects the speed of learning and best response dynamics. Q. J. Econ. 127:31287–338 [Google Scholar]
  32. Granovetter M. 1978. Threshold models of collective behavior. Am. J. Sociol. 83:61420–43 [Google Scholar]
  33. Helbing D. 2010. Quantitative Sociodynamics: Stochastic Methods and Models of Social Interaction Processes Heidelberg, Ger.: Springer-Verlag [Google Scholar]
  34. Hidalgo J, Grilli J, Suweis S, Banvar J, Munoz M, Amos A. 2014. Information based fitness and the emergence of criticality in living systems. PNAS 111:2810095–100 [Google Scholar]
  35. Holland JH. 2014. Signals and Boundaries: Building Blocks for Complex Adaptive Systems Cambridge, MA: MIT Press [Google Scholar]
  36. Hubbell SP. 2001. The Unified Neutral Theory of Biodiversity and Biogeography Princeton, NJ: Princeton Univ. Press [Google Scholar]
  37. Hutchins E. 1996. Cognition in the Wild Cambridge, MA: MIT Press [Google Scholar]
  38. Iwasa Y, Andreasen V, Levin S. 1987. Aggregation in model ecosystems. I. Perfect aggregation. Ecol. Model. 37:287–302 [Google Scholar]
  39. Jackson MO. 2010. Social and Economic Networks Princeton, NJ: Princeton Univ. Press [Google Scholar]
  40. Jen E. 2005. Robust Design: A Repertoire of Biological, Ecological, and Engineering Case Studies Oxford, UK: Oxford Univ. Press [Google Scholar]
  41. Jervis R. 1998. System Effects: Complexity in Political and Social Life Princeton, NJ: Princeton Univ. Press [Google Scholar]
  42. Kerr B, Riley M, Feldman M, Bohannon B. 2002. Local dispersal and interaction promote coexistence in a real life game of rock-paper-scissors. Nature 418:171–74 [Google Scholar]
  43. Lamberson PJ, Page SE. 2012. Tipping points. Q. J. Polit. Sci. 7:2175–208 [Google Scholar]
  44. LeBaron B. 2001. Stochastic volatility as a simple generator of apparent financial power laws and long memory. Quant. Financ. 1:621–31 [Google Scholar]
  45. Little D. 2012. Explanatory autonomy and Coleman's boat. Theoria 27:2137–51 [Google Scholar]
  46. Lloyd-Smith JO, Schreiber J, Kopp PE, Getz WM. 2005. Superspreading and the effect of individual variation on disease emergence. Nature 438:355–59 [Google Scholar]
  47. Macy M, Willer R. 2002. From factors to actors: computational sociology and agent-based modeling. Annu. Rev. Sociol. 28:143–66 [Google Scholar]
  48. May R, Levin SA, Sugihara G. 2008. Ecology for bankers. Nature 45:893–95 [Google Scholar]
  49. Merton RK. 1968. The Matthew effect in science. Science 159:381056–63 [Google Scholar]
  50. Miller JH, Page SE. 2007. Complex Adaptive Systems: An Introduction to Computational Models of Social Life Princeton, NJ: Princeton Univ. Press [Google Scholar]
  51. Mitchell M. 2009. Complexity: A Guided Tour Oxford, UK: Oxford Univ. Press [Google Scholar]
  52. Nagel K, Paczuski M. 1995. Self-organized criticality and 1/f noise in traffic. Phys. Rev. E 51:42909–18 [Google Scholar]
  53. Newman MJ. 2003. On the logic and structure of complex networks. SIAM Rev. 45:167–256 [Google Scholar]
  54. Newman MJ. 2005. Power laws, Pareto distributions and Zipf's law. Contemp. Phys. 46:323–51 [Google Scholar]
  55. Newman MJ. 2010. Networks: An Introduction Oxford, UK: Oxford Univ. Press [Google Scholar]
  56. Omerod P. 2012. Positive Linking: How Networks Can Revolutionise the World London: Faber & Faber [Google Scholar]
  57. Padgett JF, Powell WW. 2012. The Emergence of Organizations and Markets Princeton, NJ: Princeton Univ. Press [Google Scholar]
  58. Page SE. 1997. On incentives and updating in agent based models. Comput. Econ. 10:67–87 [Google Scholar]
  59. Page SE. 2001. Self organization and coordination. Comput. Econ. 18:25–48 [Google Scholar]
  60. Page SE. 2006. Essay: path dependence. Q. J. Polit. Sci. 1:187–115 [Google Scholar]
  61. Page SE. 2007. The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies Princeton, NJ: Princeton Univ. Press [Google Scholar]
  62. Page SE. 2008. Uncertainty, difficulty, and complexity. J. Theor. Polit. 20:115–49 [Google Scholar]
  63. Page SE. 2010. Diversity and Complexity Princeton, NJ: Princeton Univ. Press [Google Scholar]
  64. Page SE. 2012. A complexity perspective on institutional design. Polit. Philos. Econ. 11:5–25 [Google Scholar]
  65. Prokopenko M, Boshectti F, Ryan AJ. 2009. An information-theoretic primer on complexity, self-organization, and emergence. Complexity 15:111–28 [Google Scholar]
  66. Putnam RD. 2000. Bowling Alone: The Collapse and Revival of American Community New York: Simon & Schuster [Google Scholar]
  67. Ramo J. 2009. The Age of the Unthinkable: Why the New World Disorder Constantly Surprises Us And What We Can Do About It New York: Little, Brown [Google Scholar]
  68. Reiter S. 1977. Information, incentives, and performance in the (new)2 economy. Am. Econ. Rev. 67:1226–34 [Google Scholar]
  69. Salganik MJ, Dodds PS, Watts DJ. 2006. Experimental study of inequality and unpredictability in an artificial cultural market. Science 311:854–56 [Google Scholar]
  70. Sawyer DK. 2005. Social Emergence: Societies as a Complex System Cambridge, UK: Cambridge Univ. Press [Google Scholar]
  71. Scheffer M, Bascompte J, Brock WA, Brovkin V, Carpenter SR. et al. 2009. Early-warning signals for critical transitions. Nature 461:53–59 [Google Scholar]
  72. Schelling TC. 1971. Dynamic models of segregation. J. Math. Sociol. 1:143–86 [Google Scholar]
  73. Shalizi C. 2013. Scaling and hierarchy in urban economies. arXiv:1102.4101
  74. Shalizi C, Shalizi KL, Haslinger R. 2004. Quantifying self-organization with optimal predictors. Phys. Rev. Lett. 93:118701 [Google Scholar]
  75. Sterman J. 2000. Business Dynamics: Systems Thinking and Modeling for a Complex World New York: McGraw Hill [Google Scholar]
  76. Tao T. 2012. E pluribus unum: from complexity, universality. Deadalus 141:323–34 [Google Scholar]
  77. Tassier T. 2013. The Economics of Epidemiology Amsterdam: Springer-Verlag [Google Scholar]
  78. Tweedle V, Smith RJ. 2011. A mathematical model of Bieber fever: the most infectious disease of our time?. Understanding the Dynamics of Emerging and Re-Emerging Infectious Diseases Using Mathematical Models S Mushayabasa, CP Bhunu 157–77 Kerala, India: Transw. Res. Netw. [Google Scholar]
  79. de Rijt A, Siegel D, Macy M. van 2009. Neighborhood chance and neighborhood change. Am. J. Sociol. 114:1166–80 [Google Scholar]
  80. Vriend N. 2000. An illustration of the essential difference between individual and social learning, and its consequences for computational analyses. J. Econ. Dyn. Control 24:1–19 [Google Scholar]
  81. Watts DJ. 2004. Six Degrees: The Science of a Connected Age New York: WW Norton [Google Scholar]
  82. Watts DJ. 2011. Everything Is Obvious: *Once You Know the Answer New York: Crown [Google Scholar]
  83. Weaver W. 1948. Science and complexity. Am. Sci. 36:536–54 [Google Scholar]
  84. Wellman MP. 2014. Putting the agent in agent-based modeling Presented at International Conference on Autonomous Agents and Multiagent Systems, 13th, Paris [Google Scholar]
  85. Wolfram S. 2002. A New Kind of Science Champaign, IL: Wolfram Media [Google Scholar]
  86. Xie Y. 2007. Otis Dudley Duncan's legacy: the demographic approach to quantitative reasoning in social science. Res. Soc. Stratif. Mobil. 25:141–56 [Google Scholar]
  87. Xie Y, Cheng S, Zhou X. 2015. Assortative mating without assortative preferences. PNAS. 112195974–78 [Google Scholar]
  88. Xie Y, Zhou X. 2012. Modeling individual-level heterogeneity in racial residential segregation. PNAS 109:2911646–51 [Google Scholar]

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error