Posted: Oct 04, 2014
The study of financial intermediaries has traditionally been confined to the domains of economics and finance, with limited integration of computational methodologies that capture the dynamic and adaptive nature of these institutions. Financial intermediaries—including banks, credit unions, investment funds, and other institutions that channel funds from savers to borrowers—play a crucial role in modern economies. However, conventional economic models often treat these entities as static components within equilibrium frameworks, failing to capture their evolving behaviors and the complex network effects they generate. This research introduces a novel computational framework that reconceptualizes financial intermediaries as learning agents within a complex adaptive system, providing unprecedented insights into how these institutions collectively influence economic growth and resource allocation efficiency. Our approach bridges computational science with economic theory by developing a multi-agent simulation platform that models financial intermediaries as adaptive entities capable of learning from their environment and adjusting their strategies accordingly. This perspective represents a significant departure from traditional economic modeling, which typically assumes perfect information and rational behavior. Instead, we incorporate bounded rationality, learning mechanisms, and network dynamics to create a more realistic representation of how financial intermediaries operate and evolve over time.
Downloads: 72
Abstract Views: 2121
Rank: 360944