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

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

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Slides of the talk at the 10th International Workshop on Multi-Agent Systems and Simulation, MAS&S 2016, Gdansk, Poland, 11-14 Sept. 2016

Slides of the talk at the 10th International Workshop on Multi-Agent Systems and Simulation, MAS&S 2016, Gdansk, Poland, 11-14 Sept. 2016

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

  1. 1. Simulating the Fractional Reserve Banking using Agent- based Modelling with NetLogo Talk at the 10th International Workshop on Multi-Agent Systems and Simulation, MAS&S 2016 Prof. Dr. Dagmar Monett Dr. Jesús Emeterio Navarro-Barrientos
  2. 2. D. Monett 2Gdańsk, Poland, September 11 – 14, 2016 Finance is a black box “If the physical world is so uncertain, so difficult to know precisely, then how much more uncertain and unknowable must be the world of money? Finance is a black box covered by a veil.” Mandelbrot, Benoit, and Hudson, Richard L. (2004) The (mis)Behavior of Markets: A Fractal View of Risk, Ruin, and Reward. New York, NY: Basic Books. Mandelbrot‘s work via C. Lewis
  3. 3. D. Monett 3Gdańsk, Poland, September 11 – 14, 2016 Background
  4. 4. D. Monett 4Gdańsk, Poland, September 11 – 14, 2016 FRB … “in which banks hold only a fraction of their deposits in reserves, so that the reserve- deposit ratio is less than 1” (Abel & Bernanke, 2005) The fractional reserve banking is a banking system... The bank invests or loans the rest of the deposits.
  5. 5. D. Monett 5Gdańsk, Poland, September 11 – 14, 2016 FRB cycle
  6. 6. D. Monett 6Gdańsk, Poland, September 11 – 14, 2016 Our approach
  7. 7. D. Monett 7Gdańsk, Poland, September 11 – 14, 2016 Start simple “In building a model, start simple.” (Mandelbrot & Hudson, 2004)
  8. 8. D. Monett 8Gdańsk, Poland, September 11 – 14, 2016 Paraphrasing... In understanding a model, simulate it.
  9. 9. D. Monett 9Gdańsk, Poland, September 11 – 14, 2016 Our work ■ No “big” economics insights there... ■ ...but a simple agent-based computational model ■ ...that uses NetLogo ■ ...to focus on the dynamics of the fractional reserve banking system ■ ...to ease its understanding through simulations and graphical tools!
  10. 10. D. Monett 10Gdańsk, Poland, September 11 – 14, 2016 Why? ■ To anticipate economic scenarios that could eventually be avoided ■ To provide artificial ways to represent and to simulate the impact of the FRB system ■ To provide a basic playground setting for doing it ■ To drive the policies and behaviours of banks before testing their validity in the real world ■ To find out the sufficient conditions for a banking system to become fragile and unstable
  11. 11. D. Monett 11Gdańsk, Poland, September 11 – 14, 2016 An agent-based computational model for the FRB
  12. 12. D. Monett FRB model overview 12Gdańsk, Poland, September 11 – 14, 2016
  13. 13. D. Monett Deliberation: depositor 13Gdańsk, Poland, September 11 – 14, 2016
  14. 14. D. Monett Deliberation: depositor 14Gdańsk, Poland, September 11 – 14, 2016 t : trust in the bank C : current capital D : amount to deposit S : current deposit  : deposit interest rate W : amount to withdraw p : time preference
  15. 15. D. Monett 15Gdańsk, Poland, September 11 – 14, 2016 Experimental settings and results (more: on the paper!)
  16. 16. D. Monett Experiments 16Gdańsk, Poland, September 11 – 14, 2016 We are interested in finding which parameter values lead to either bank insolvency or to a stationary scenario with no insolvency of the bank, over iterations ■ 1 bank and fixed no. of depositors/debtors ■ E.g. of parameters: ■ loan (credit) interest, start capital, confidence win/loss rates, minimum reserve rate, deposit interest, among others
  17. 17. D. Monett FRB in NetLogo 17Gdańsk, Poland, September 11 – 14, 2016
  18. 18. D. Monett Some results 18Gdańsk, Poland, September 11 – 14, 2016 ■ avg loss of confidence > avg win of confidence ■ depositors & debtors lose their trust in the bank ■ Insolvency of the bank more probable: ■ when loss of confidence increases ■ when reserves are low ■ when depositors & debtors lose their trust ■ when depositors start to withdraw deposits (just one single depositor could be enough for starting a cascade)
  19. 19. D. Monett 19Gdańsk, Poland, September 11 – 14, 2016 Further work
  20. 20. D. Monett Further work 20Gdańsk, Poland, September 11 – 14, 2016 To consider/study… ■ different trust reputation mechanisms ■ whether the dynamics of the model scale in size or not ■ impact of excluding external sources that would help a bank to pay to the depositors and keep it solvent ■ money needed on average to help a bank avoid insolvency? average lifetime of banks?
  21. 21. D. Monett Sources 21Gdańsk, Poland, September 11 – 14, 2016 Related work: - See references list on our paper! ■ https://www.researchgate.net/publication/304244558_Simu lating_the_Fractional_Reserve_Banking_using_Agent- based_Modelling_with_NetLogo
  22. 22. dagmar@monettdiaz.com monettdiaz Contact:

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