The Global Financial Crisis revealed that systemic fragility arises not only from identifiable risks but also from systemic uncertainty – the endogenous and often unpredictable dynamics that build up within financial systems and amplify across time and space. More than a decade later, this dual challenge remains pressing as the rising inequality and geopolitical insecurity have underscored the vulnerability of interconnected economies and societies.
This book illustrates how agent-based modelling (ABM) can enrich the study of crises, systemic risk and uncertainty, and the distributional effects of policies that are designed to contain them. Rather than treating crises as external shocks, the book highlights how fragility can emerge endogenously within economies that are understood as complex, evolving systems. Through clear explanations and targeted applications, it not only demonstrates how ABM can capture the effects of macroprudential regulation on the real economy and inequality and shed light on moments of financial instability but also provides a practical guide to building such data-driven models in the first place. At the same time, it situates these insights within a broader discussion of contagion, procyclicality and global financial regulation, thus connecting the modelling results with ongoing debates on how best to safeguard financial stability. Further, it offers a valuable synthesis of lessons learned from advanced economies, illustrating how institutional design and macroprudential policy can strengthen financial resilience while also generating significant redistributive effects across sectors and households.
The book is primarily aimed at an academic audience of scholars and advanced students in economics, finance and computational social science. It will also appeal to professionals in central banks, regulators and policymakers engaged in designing and evaluating macroprudential frameworks.
The Open Access version of this book, available at http://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC BY-NC-ND) 4.0 license.