This article describes the agent-based approach to modeling financial crises. It focuses on the interactions of agents and on how these interactions feed back to change the financial environment. It explains how these models embody the contagion and cascades that occur owing to the financial leverage and market concentration of the agents and the liquidity of the markets. This article also compares agent-based models to the standard economic approach to crises and shows the ways in which agent-based models overcome limitations of economic models when dealing with financial crises. In particular, this article demonstrates how agent-based models replace homogeneous, representative agents with heterogeneous agents and optimization with heuristics, and how such models move away from a focus on equilibrium, allowing non-ergodic dynamics that are manifest during financial crises to emerge.


Article metrics loading...

Loading full text...

Full text loading...


Literature Cited

  1. Aguiar A, Bookstaber R, Wipf T. 2014. A map of funding durability and risk Work. Pap. 14-03 Off. Financ. Res.
  2. Anderson PW, Arrow KJ, Pines D. 1988. The Economy as an Evolving Complex System Redwood City, CA: Addison-Wesley
  3. Arthur WB. 1999. Complexity and the economy. Science 284:107–9 [Google Scholar]
  4. Ashraf Q, Gershman B, Howitt P. 2016. How inflation affects macroeconomic performance: an agent-based computational investigation. Macroecon. Dyn. 20:558–81 [Google Scholar]
  5. Bookstaber R. 2017. The End of Theory: Financial Crisis, the Failure of Economics, and the Sweep of Human Interaction Princeton, NJ: Princeton Univ. Press [Google Scholar]
  6. Bookstaber R, Foley M, Tivnan B. 2016. Toward an understanding of market resilience: market liquidity and heterogeneity in the investor decision cycle. J. Econ. Interact. Coord. 11:205–27 [Google Scholar]
  7. Bookstaber R, Kenett D. 2016. Looking deeper, seeing more: a multi-layer map of the financial system Brief Ser. 16-06 Off. Financ. Res.
  8. Bookstaber R, Langsam J. 1985. On the optimality of coarse behavior rules. J. Theor. Biol. 116:161–93 [Google Scholar]
  9. Bookstaber R, Paddrik M, Tivnan B. 2017. An agent-based model for financial vulnerability. J. Econ. Interact. Coord. In press
  10. Brunnermeier M, Pedersen LH. 2009. Market liquidity and funding liquidity. Rev. Financ. Stud. 22:2201–38 [Google Scholar]
  11. Buchanan M. 2009. Meltdown modeling: Could agent-based computer models prevent another financial crisis. Nature 460:680–82 [Google Scholar]
  12. Cifuentes R, Ferrucci G, Shin HS. 2005. Liquidity risk and contagion. J. Eur. Econ. Assoc. 3:556–66 [Google Scholar]
  13. Colander D, Goldberg M, Haas A, Juselius K, Kirman A. et al. 2009. The financial crisis and the systemic failure of the economics profession. Crit. Rev. 21:249–67 [Google Scholar]
  14. Davis M. 1958. Computability and Unsolvability New York: McGraw-Hill
  15. Duffie D. 2010. Presidential address: asset price dynamics with slow-moving capital. J. Finance 65:1237–67 [Google Scholar]
  16. Ehrentreich N. 2008. Agent-Based Modeling: The Santa Fe Institute Artificial Stock Market Model Revisited Berlin: Springer-Verlag
  17. Evans GW, Honkapohja S. 2005. An interview with Thomas J. Sargent. Macroecon. Dyn. 9:561–83 [Google Scholar]
  18. Farmer JD, Foley D. 2009. The economy needs agent-based modelling. Nature 460:685–86 [Google Scholar]
  19. Farmer JD, Geanakoplos J. 2009. The virtues and vices of equilibrium and the future of financial economics. Complexity 14:11–38 [Google Scholar]
  20. Gardner M. 1970. Mathematical games: the fantastic combinations of John Conway's new solitaire game ‘Life.’. Sci. Am 223:120–23 [Google Scholar]
  21. Geanakoplos J, Axtell R, Farmer DJ, Howitt P, Conlee B. et al. 2012. Getting at systemic risk via an agent-based model of the housing market. Am. Econ. Rev. 102:53–58 [Google Scholar]
  22. Gigerenzer G, Brighton H. 2009. Homo heuristics: why biased minds make better inferences. Top. Cogn. Sci. 1:107–43 [Google Scholar]
  23. Gigerenzer G, Gaissmaier W. 2011. Heuristic decision making. Annu. Rev. Psychol. 62:451–82 [Google Scholar]
  24. Haldane A. 2016. The dappled world Speech at GLS Shackle Bien. Mem. Lect., Bank Engl., Nov. 10 London: http://www.bankofengland.co.uk/publications/Pages/speeches/2016/937.aspx
  25. Helbing D, Farkas I, Vicsek T. 2000. Simulating dynamical features of escape panic. Nature 407:487–90 [Google Scholar]
  26. Helbing D, Kirman A. 2013. Rethinking economics using complexity theory. Real-World Econ. Rev. 64:23–51 [Google Scholar]
  27. Int. Monet. Fund. 2007. World Economic Outlook: Globalization and Inequality Washington, DC: Int. Monet. Fund
  28. Keynes JM. 1978. Letter to Harrod, 4 July 1938. The Collected Writings of John Maynard Keynes 14 The General Theory and After: Part II. Defence and Development E Johnson, D Moggridge 295–97 Cambridge, UK: Cambridge Univ. Press http://economia.unipv.it/harrod/edition/editionstuff/rfh.346.htm [Google Scholar]
  29. Kirman A. 1989. The intrinsic limits of modern economic theory: The emperor has no clothes. Econ. J. 99:126–39 [Google Scholar]
  30. Kirman A. 1992. Whom or what does the representative individual represent. J. Econ. Perspect. 6:117–36 [Google Scholar]
  31. Kirman A. 2010. The economic crisis is a crisis for economic theory. CESifo Econ. Stud. 56:498–535 [Google Scholar]
  32. Leal SJ, Napoletano M, Roventini A, Fagiolo G. 2016. Rock around the clock: an agent-based model of low- and high-frequency trading. J. Evol. Econ. 26:49–76 [Google Scholar]
  33. LeBaron B. 2006a. Agent-based computational finance. Handbook of Computational Economics 2 Agent-Based Computational Economics L Tesfatsion, KL Judd 1187–227 Amsterdam: North-Holland/Elsevier [Google Scholar]
  34. LeBaron B. 2006b. Agent-based financial markets: matching stylized facts with style. Post Walrasian Macroeconomics D Colander 221–35 New York: Cambridge Univ. Press [Google Scholar]
  35. LeBaron B, Arthur WB, Palmer R. 1999. Time series properties of an artificial stock market model. J. Econ. Dyn. Control 23:1487–516 [Google Scholar]
  36. Lucas R Jr.. 1981. Studies in Business-Cycle Theory Cambridge, MA: MIT Press
  37. Lucas R Jr.. 2009. In defence of the dismal science. The Economist Aug. 6. http://www.economist.com/node/14165405
  38. Monin P, Bookstaber R. 2017. Information flows, the accuracy of opinions, and crashes in a dynamic network Staff Discuss. Pap. 17-01 Off. Financ. Res.
  39. Olberg R, Worthington A, Venator K. 2000. Prey pursuit and interception in dragonflies. J. Comp. Physiol. A 186:155–62 [Google Scholar]
  40. Page SE. 2011. Diversity and Complexity Princeton, NJ: Princeton Univ. Press
  41. Palmer RG, Arthur WB, Holland JH, LeBaron B, Tayler P. 1994. Artificial economic life: a simple model of a stock market. Physica D 75:264–74 [Google Scholar]
  42. Preis T, Golke S, Paul W, Schneider JJ. 2006. Multi-agent based order book model of financial markets. Europhys. Lett. 75:510–16 [Google Scholar]
  43. Rieck C. 1994. Evolutionary simulation of asset trading strategies. Many-Agent Simulation and Artificial Life E Hillebrand, J Stender 112–36 Amsterdam: IOS Press [Google Scholar]
  44. Romer P. 2016. The trouble with macroeconomics Commons Meml. Lect., Omicron Delta Epsilon Soc. Jan. 5
  45. Saari DG. 1995. Mathematical complexity of simple economics. Not. Am. Math. Soc. 42:222–30 [Google Scholar]
  46. Saari DG. 1996. The ease of generating chaotic behavior in economics. Chaos Solut. Fractals 7:2267–78 [Google Scholar]
  47. Scarf H. 1960. Some examples of the global instability of the competitive equilibrium. Int. Econ. Rev. 1:157–72 [Google Scholar]
  48. Shi J, Ren A, Chen C. 2009. Agent-based evacuation model of large public buildings under fire conditions. Autom. Constr. 18:338–47 [Google Scholar]
  49. Shleifer A, Vishny R. 2011. Fire sales in finance and macroeconomics. J. Econ. Perspect. 25:29–48 [Google Scholar]
  50. US House, Subcomm. Investig. Overs., Comm. Sci. Technol. 2010. Building a Science of Economics for the Real World Hearing (Serial 111-106), 111 Congr., 2nd sess., July 10, statement of Robert M. Solow. https://www.gpo.gov/fdsys/pkg/CHRG-111hhrg57604/pdf/CHRG-111hhrg57604.pdf
  51. Syll LP. 2016. On the Use and Misuse of Theories and Models in Mainstream Economics London: Coll. Publ.
  52. Thurner S, Farmer JD, Geanakoplos J. 2012. Leverage causes fat tails and clustered volatility. Quant. Finance 12:695–707 [Google Scholar]
  53. Tirole J. 2011. Illiquidity and all its friends. J. Econ. Lit. 49:2287–325 [Google Scholar]
  54. Trichet J-C. 2010. Reflections on the nature of monetary policy non-standard measures and finance theory Opening address, ECB Cent. Bank. Conf., Nov. 18, Frankfurt, Ger.
  55. Zeeman E. 1974. On the unstable behavior of stock exchanges. J. Math. Econ. 1:39–49 [Google Scholar]
  56. Zheng H, Son Y-J, Chiu Y-C, Head L, Feng Y. et al. 2013. A primer for agent-based simulation and modeling in transportation applications Rep. FHWA-13-054 Fed. Highway Adm.

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