Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/106557
Title: Connectionist agent-based learning in bank-run decision making
Authors: Huang, Weihong
Huang, Qiao
Keywords: Artificial Intelligence
DRNTU::Social sciences::Economic development
Stochastic Processes
Issue Date: 2018
Source: Huang, W., & Huang, Q. (2018). Connectionist agent-based learning in bank-run decision making. Chaos : An Interdisciplinary Journal of Nonlinear Science, 28(5), 055910-. doi:10.1063/1.5022222
Series/Report no.: Chaos: An Interdisciplinary Journal of Nonlinear Science
Abstract: It is of utter importance for the policy makers, bankers, and investors to thoroughly understand the probability of bank-run (PBR) which was often neglected in the classical models. Bank-run is not merely due to miscoordination (Diamond and Dybvig, 1983) or deterioration of bank assets (Allen and Gale, 1998) but various factors. This paper presents the simulation results of the nonlinear dynamic probabilities of bank runs based on the global games approach, with the distinct assumption that heterogenous agents hold highly correlated but unidentical beliefs about the true payoffs. The specific technique used in the simulation is to let agents have an integrated cognitive-affective network. It is observed that, even when the economy is good, agents are significantly affected by the cognitive-affective network to react to bad news which might lead to bank-run. Both the rise of the late payoffs, R, and the early payoffs, r, will decrease the effect of the affective process. The increased risk sharing might or might not increase PBR, and the increase in late payoff is beneficial for preventing the bank run. This paper is one of the pioneers that links agent-based computational economics and behavioral economics.
URI: https://hdl.handle.net/10356/106557
http://hdl.handle.net/10220/48942
ISSN: 1054-1500
DOI: 10.1063/1.5022222
Rights: © 2019 The Author(s). All rights reserved. This paper was published by AIP Publishing in Chaos : An Interdisciplinary Journal of Nonlinear Science and is made available with permission of The Author(s).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SSS Journal Articles

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