Systematic analysis of banking sector competition dynamics and market structure evolution

Posted: Oct 09, 2017

• Dr. Nora Ortega
• Dr. Oliver Zhang
• Dr. Olivia Schmidt

Abstract

This research presents a novel computational framework for analyzing banking sector competition dynamics through the lens of complex adaptive systems theory, departing from traditional economic models that often rely on static equilibrium assumptions. We introduce a multi-agent simulation approach that integrates network theory, evolutionary algorithms, and machine learning to model the emergent behavior of banking institutions within dynamic market environments. Our methodology captures the co-evolutionary dynamics between competitive strategies, regulatory frameworks, and market structure transformations over extended temporal horizons. The framework incorporates heterogeneous agent behaviors, adaptive learning mechanisms, and systemic risk propagation pathways that traditional competition analysis methods typically overlook. By simulating various market scenarios, including technological disruptions, regulatory changes, and economic shocks, we demonstrate how banking competition evolves through complex feedback loops and path-dependent processes. Our results reveal previously unidentified tipping points in market concentration, emergent collusion patterns without explicit coordination, and non-linear relationships between competition intensity and financial stability. The findings challenge conventional wisdom regarding optimal market structures and provide new insights for policymakers seeking to balance competition objectives with systemic stability concerns. This research contributes to both computational economics and financial regulation by offering a dynamic, systems-oriented perspective on banking competition that more accurately reflects the complex realities of modern financial markets.