MoMTSim: A Multi-Agent-Based Simulation Platform Calibrated for Mobile Money Transactions

Abstract

Research on multi-agent systems has extensively modeled real-world phenomena across various domains including epidemiology, urban planning, and financial transactions. These systems often struggle to produce agent behaviors that comprehensively capture the dynamics of the real-world ecosystem and the unique behaviors of each agent type. Furthermore, the limited explainability of these models due to non-iterative calibration poses significant challenges. This paper introduces an iterative model calibration algorithm that dynamically adjusts the multitude of parameters in a multi-agent simulation platform. Initially treating the simulation model as a black box, our method refines simulation parameters through cycles of adjustments based on clusters of observed behaviors comprising the behavior of both agents and actors. This approach allows for the identification and correction of inaccuracies, introduces new parameters, and discards erroneous ones within the agent-based model as demonstrated in a Mobile Money Transaction Simulator (MoMTSim). The calibration algorithm enhances the realism and applicability of the simulation model by ensuring that the generated synthetic datasets closely mirror real transaction data. The effectiveness of this calibration method was determined by validating the generated data.We computed the delta between the real and synthetic data using the sum of squared errors approach. This shows that MoMTSim can produce synthetic data that resemble real mobile money transaction data, thereby confirming the efficacy of the calibration algorithm for use in simulating complex financial ecosystems.