Interbank payment system (RTGS) simulation using multi-agent approach
This work consists in simulating a real time interbank gross payment system (RTGS) through a multi-agent model, to analyze the evolution of the liquidity brought by the banks to the system. In this model, each bank chooses the amount of a daily liquidity on the basis of costs minimization (costs of liquidity and delaying) by taking into account the liquidity brought by the other banks. Banks agents’ strategies are based on a liquidity game during several payment days where each bank plays against the others. For their adaptability, we integrate into bank agents learning classifier systems. We carry out several simulations to follow the system total liquidity evolution as that of each bank agent with varying costs coefficients. The question to answer is: what are the cash amounts that banks must provide and under what costs of liquidity and delaying, the system avoids the lack of liquidity? We find that liquidity depends on costs coefficients.
Real Time Gross Settlement, Multi-agent system, Classifier system, Evolutionary game theory