Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Simulating the Fractional Reserve Banking using Agent-based Modelling with NetLogo

,

DOI: http://dx.doi.org/10.15439/2016F373

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 1467–1470 ()

Full text

Abstract. This work presents a multi-agent-based computational model of an artificial fractional reserve banking (FRB) system. The model is implemented in NetLogo. The computational experiments and simulations we performed to analyse the proposed model show that different scenarios can lead to bank insolvency. We show that both the minimum reserve rate and the loss of confidence have large contributions to the insolvency of a bank, suggesting them as likely destabilizing economic forces driving the dynamics of the model.

References

  1. N. Gilbert and S. Bankes, “Platforms and methods for agent-based modeling,” Proceedings of the National Academy of Sciences, vol. 99, no. 3, pp. 7197–7198, 2002.
  2. A. Abel and B. Bernanke, Macroeconomics, 5th ed. Pearson, 2005.
  3. F. Sigurjónsson, “Monetary Reform - A better monetary system for Iceland,” Reykjavík, Iceland, Tech. Rep. 1.0, March 2015.
  4. K. Soramäki, M. L. Bech, J. Arnold, R. J. Glass, and W. E. Beyeler, “The topology of interbank payment flows,” Federal Reserve Bank of New York, Tech. Rep. Staff Report no. 243, March 2006.
  5. P. Bedford, S. Millard, and J. Yang, “Analysing the impact of operational incidents in large-value payment systems: A simulation approach,” in Liquidity, risks and speed in payment and settlement systems---a simulation approach, H. Leinonen, Ed. Helsinki: Bank of Finland Studies, E:31, 2005, ch. 9, pp. 249–276.
  6. G. Iori, S. Jafarey, and F. Padilla, “Interbank Lending and Systemic Risk,” Journal of Economic Behavior and Organization, vol. 61, pp. 525–542, 2006.
  7. M. Galbiati and K. Soramäki, “An agent-based model of payment systems,” Journal of Economic Dynamics and Control, vol. 35, no. 6, pp. 859–875, 2011.
  8. N. Gilbert and P. Terna, “How to build an use agent-based models in social science,” Mind & Society, vol. 1, pp. 57–72, 2000.
  9. L. Arciero, C. Biancotti, D. L., and C. Impenna, “Exploring agent-based methods for the analysis of payment systems: A crisis model for StarLogo TNG,” Journal of Artificial Societies and Social Simulation, vol. 12, no. 1–2, 2009.
  10. J. Wiens and D. Monett, “Using BDI-extended NetLogo Agents in Undergraduate CS Research and Teaching,” in Proceedings of The 9th International Conference on Frontiers in Education: Computer Science and Computer Engineering, FECS’2013, H. Arabnia, A. Bahrami, V. Clincy, L. Deligiannidis, and G. Jandieri, Eds. CSREA Press U.S.A., July 2013, pp. 396–402.
  11. J. Mallett, “Analysing the behaviour of the textbook fractional reserve banking model as a complex dynamic system,” in Proceedings of the 8th International Conference on Complex Systems, ICCS’2011, H. Sayama, A. Minai, D. Braha, and Y. Bar-Yam, Eds., vol. 8. NECSI Knowledge Press, 2011, pp. 1141–1155.
  12. U. Wilensky, “NetLogo,” Evanston, IL, U.S.A., 1999, available online at http://ccl.northwestern.edu/netlogo/, retrieved January 3, 2013.
  13. A. Rao and M. Georgeff, “BDI Agents: From Theory to Practice,” in Proceedings of the First International Conference on Multi-Agent Systems, ICMAS’95. San Francisco, CA, U.S.A.: AAAI Press / The MIT Press, June 12–14 1995, pp. 312–319.
  14. I. Sakellariou, P. Kefalas, and I. Stamatopoulou, “Enhancing NetLogo to Simulate BDI Communicating Agents,” in Proceedings of the 5th Hellenic Conference on Artificial Intelligence, SETN’08, ser. Lecture Notes in Artificial Intelligence (LNAI), J. Darzentas, G. Vouros, S. Vosinakis, and A. Arnellos, Eds., vol. 5138. Syros, Greece: Springer Berlin Heidelberg, October 2008, pp. 263–275.