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Emotional Agents Make a (Bank) Run

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Multi-Agent Systems and Agreement Technologies (EUMAS 2020, AT 2020)

Abstract

Agent-based Computational Economics (ACE) is an area that has gained significant attention, since it offers the possibility to model economic phenomena in a more fine-grained manner than other approaches. One such phenomenon is “bank panic” in which the term “panic” implies the existence of emotional bias towards to the sudden withdrawal of deposits from financial institutions (simultaneous bank runs). However, research towards complex emotional agents in ACE has not been extensively conducted. The paper employs a formal state-based model enhanced with explicit emotional states, mood and personality characteristics in order to describe the agents behavior. A NetLogo simulation of a multi-agent system in a limited economic environment is attempted in order to study the effects of emotions, emotion contagion and the role of various players in the genesis of a bank panic crisis. The aim is to investigate further whether such agent models that are already used in other areas, such as evacuation simulation, could also provide a better insight on the evolution of such economic phenomena.

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Notes

  1. 1.

    The code can by found at https://github.com/isakellariou/NetLogoBankRun.

References

  1. Aymanns, C., Farmer, J.D.: The dynamics of the leverage cycle. J. Econ. Dyn. Control 50, 155–179 (2015). https://doi.org/10.1016/j.jedc.2014.09.015

    Article  MathSciNet  MATH  Google Scholar 

  2. Brown, M., Trautmann, S.T., Vlahu, R.: Understanding bank-run contagion. Technical report, European Central Bank (2014). https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1711.pdf

  3. Chan-Lau, J.A.: ABBA: an agent-based model of the banking system. IMF Working Papers 17/136, International Monetary Fund (2017). https://ideas.repec.org/p/imf/imfwpa/17-136.html

  4. Damasio, A.R.: Descartes Error: Emotion, Reason, and the Human Brain. G.P. Putnam, New York (1994)

    Google Scholar 

  5. Davis, D.D., Reilly, R.J.: On freezing depositor funds at financially distressed banks: an experimental analysis. J. Money Credit Bank. 48(5), 989–1017 (2016). https://doi.org/10.1111/jmcb.12324

    Article  Google Scholar 

  6. Deng, J., Yu, T., Li, H.: Bank runs in a local interaction model. Phys. Procedia 3(5), 1687–1697 (2010). https://doi.org/10.1016/j.phpro.2010.07.007

    Article  Google Scholar 

  7. Fridja, N.: The psychologists’ point of view. In: Lewis, M., Haviland-Jones, J., Feldman-Barrett, L. (eds.) Handbook of Emotions, 3rd edn., pp. 68–87. The Guildford Press, New York (2008). https://hdl.handle.net/11245/1.295660

  8. He, Z., Manela, A.: Information acquisition in rumor’ based bank runs. J. Financ. 71(3), 1113–1158 (2016). https://doi.org/10.1111/jofi.12202

    Article  Google Scholar 

  9. Holcombe, M., Ipate, F.: The theory of x-machines. In: Correct Systems: Building a Business Process Solution, pp. 135–168. Springer, London (1998). https://doi.org/10.1007/978-1-4471-3435-0_6

  10. Hoogendoorn, M., Treur, J., Wal, C., Wissen, A.: Modelling the interplay of emotions, beliefs and intentions within collective decision making based on insights from social neuroscience. In: Neural Information Processing: Theory and Algorithms, LNCS, vol. 6443, pp. 196–206. Springer, Berlin Heidelberg (2010). https://doi.org/10.1007/978-3-642-17537-4_25

  11. Huang, W., Huang, Q.: Connectionist agent-based learning in bank-run decision making. Chaos Interdisc. J. Nonlinear Sci. 28(5), 055910 (2018). https://doi.org/10.1063/1.5022222

  12. Iyer, R., Puri, M.: Understanding bank runs: the importance of depositor-bank relationships and networks. Am. Econ. Rev. 102(4), 1414–1445 (2012). https://doi.org/10.1257/aer.102.4.1414

    Article  Google Scholar 

  13. Judd, K.L.: Chapter 17 Computationally intensive analyses in economics. In: Handbook of Computational Economics, pp. 881–893. Elsevier (2006). https://doi.org/10.1016/s1574-0021(05)02017-4

  14. Kefalas, P., Sakellariou, I., Basakos, D., Stamatopoulou, I.: A formal approach to model emotional agents behaviour in disaster management situations. In: Likas, A., Blekas, K., Kalles, D. (eds.) SETN 2014. LNCS (LNAI), vol. 8445, pp. 237–250. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07064-3_19

    Chapter  Google Scholar 

  15. Kefalas, P., Sakellariou, I., Savvidou, S., Stamatopoulou, I., Ntika, M.: The role of mood on emotional agents behaviour. In: Nguyen, N.-T., Manolopoulos, Y., Iliadis, L., Trawiński, B. (eds.) ICCCI 2016. LNCS (LNAI), vol. 9875, pp. 53–63. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45243-2_5

    Chapter  Google Scholar 

  16. Kiss, H.J., Rodriguez-Lara, I., Rosa-García, A.: Do social networks prevent or promote bank runs? J. Econ. Behav. Organ. 101, 87–99 (2014). https://doi.org/10.1016/j.jebo.2014.01.019

    Article  Google Scholar 

  17. Padgham, L., Taylor, G.: A system for modelling agents having emotion and personality. In: Cavedon, L., Rao, A., Wobcke, W. (eds.) IAS 1996. LNCS, vol. 1209, pp. 59–71. Springer, Heidelberg (1997). https://doi.org/10.1007/3-540-62686-7_28

    Chapter  Google Scholar 

  18. Pereira, D., Oliveira, E., Moreira, N.: Formal modelling of emotions in BDI agents. In: Sadri, F., Satoh, K. (eds.) CLIMA 2007. LNCS (LNAI), vol. 5056, pp. 62–81. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88833-8_4

    Chapter  Google Scholar 

  19. Provenzano, D.: Contagion and bank runs in a multi-agent financial system. In: Teglio, A., Alfarano, S., Camacho-Cuena, E., Ginés-Vilar, M. (eds.) Managing Market Complexity: The Approach of Artificial Economics, pp. 27–38. LNE, Springer, Berlin, Heidelberg (2013). https://doi.org/10.1007/978-3-642-31301-1_3

  20. Rao, A.S., Georgeff, M.P.: Modeling rational agents within a BDI-architecture. In: Principles of Knowledge Representation and Reasoning. Proceedings of the second International Conference. pp. 473–484. Morgan Kaufmann, San Mateo (1991). https://doi.org/10.5555/3087158.3087205

  21. Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980). https://doi.org/10.1037/h0077714

    Article  Google Scholar 

  22. Russell, J.A.: Core affect and the psychological construction of emotion. Psychol. Rev. 110(1), 145–172 (2003). https://doi.org/10.1037/0033-295X.110.1.145

    Article  Google Scholar 

  23. Sakellariou, I., Kefalas, P., Savvidou, S., Stamatopoulou, I., Ntika, M.: The role of emotions, mood, personality and contagion in multi-agent system decision making. In: Iliadis, L., Maglogiannis, I. (eds.) AIAI 2016. IAICT, vol. 475, pp. 359–370. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44944-9_31

    Chapter  Google Scholar 

  24. Sakellariou, I., Kefalas, P., Stamatopoulou, I.: Evacuation simulation through formal emotional agent based modelling. In: Duval, B., van den Herik, H.J., Loiseau, S., Filipe, J. (eds.) ICAART 2014 - Proceedings of the 6th International Conference on Agents and Artificial Intelligence, vol. 2, ESEO, Angers, Loire Valley, France, 6–8 March, 2014, pp. 193–200. SciTePress (2014). https://doi.org/10.5220/0004824601930200

  25. Santos, R., Marreiros, G., Ramos, C., Neves, J., Bulas-Cruz, J.: Personality, emotion, and mood in agent-based group decision making. IEEE Intell. Syst. 26(6), 58–66 (2011). https://doi.org/10.1109/mis.2011.92

    Article  Google Scholar 

  26. dos Santos, T., Nakane, M.: Dynamic bank runs: an agent-based approach. Working Papers Series 465, Central Bank of Brazil, Research Department (2017). https://EconPapers.repec.org/RePEc:bcb:wpaper:465

  27. Shi, S., Temzelides, T.: A model of bureaucracy and corruption. Int. Econ. Rev. 45(3), 873–908 (2004). https://doi.org/10.1111/j.0020-6598.2004.00290.x

    Article  Google Scholar 

  28. Tesfatsion, L.: Chapter 16 agent-based computational economics: a constructive approach to economic theory. In: Tesfatsion, L., Judd, K. (eds.) Handbook of Computational Economics, vol. 2, pp. 831–880. Elsevier (2006). https://doi.org/10.1016/s1574-0021(05)02016-2

  29. Tsai, J., et al.: Escapes - evacuation simulation with children, authorities, parents, emotions, and social comparison. In: AAMAS 2011: The Tenth International Conference on Autonomous Agents and Multiagent System, vol. 2, pp. 457–464. ACM Digital Library, New York, New York, United States (2011). https://dl.acm.org/doi/abs/10.5555/2031678.2031682

  30. Wilensky, U.: NetLogo (1999). http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL

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Grevenitis, K., Sakellariou, I., Kefalas, P. (2020). Emotional Agents Make a (Bank) Run. In: Bassiliades, N., Chalkiadakis, G., de Jonge, D. (eds) Multi-Agent Systems and Agreement Technologies. EUMAS AT 2020 2020. Lecture Notes in Computer Science(), vol 12520. Springer, Cham. https://doi.org/10.1007/978-3-030-66412-1_12

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  • DOI: https://doi.org/10.1007/978-3-030-66412-1_12

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