Intelligent multi-agent modeling of the interbank network and evaluation of the impact of regulatory policies

agent-based modeling is an emerging computational technique that makes it possible to simulate complex economic systems, including the banking network, with a bottom-up approach. In this paper, the country's banking network is simulated with an intelligent multi-agent modeling model and indicates that these agents behave based on the adaptive learning. This modeling has been done with the aim of examining and evaluating the impact of regulatory policies on the interbank market and based on the balance sheet data of  25 member banks of the interbank market in the years 2006-2019. To assess the impact of regulatory policy, the scenario of having Clearing House to reduce non-payments in the interbank market has been examined. Due to the learning of the agents, in this simulation, the results show the direct and indirect impact of regulations on the interbank market by changing the adaptive strategies of the agents. According to the results of this study, monitoring the interbank market through the Clearing House, solves the problem of information asymmetry in the interbank market and thus reduces financial contagion and increases the stability of the system.