The 2008 financial crisis demonstrated that complex and opaque networks of credit relationships among firms can set the stage for sudden and unexpected propagation of financial uncertainty throughout the economy. In this project, new models of dynamic credit networks will provide a basis for evaluating systemic risks of this kind, and for designing institutions and policies that improve robustness to asset price fluctuations and economic shocks of various kinds. Investigators will construct large-scale simulations, where key actors (banks, non-bank financial firms, and non-financial enterprises) make dynamic credit decisions based on uncertain information that changes over time. Analysis of the data promises to yield new insights about credit networks, and techniques for reasoning about complex credit relationships. Such a capacity can lead to new tools for risk management, supporting applications within individual financial firms as well as for central banks and other economic regulators.
The project will employ models based on graph-based trust accounting mechanisms, developed in recent years by computer scientists and economists. The investigation takes an agent-based approach, where decision makers are instantiated by computational objects executing strategies aimed to optimize objectives (profit) given available information. Evidence about systemic properties is derived by simulation, which affords heterogeneity and accommodates complex information and fine-grained dynamics. Salient agent strategies will be selected according to game-theoretic solution concepts, helping to bridge between agent-based and mainstream economic analysis methodology. By providing new ways of analyzing complex financial networks, this project promises to produce new insights about the formation and evolution of complex credit relationships.
For further information, see the project web site: http://web.eecs.umich.edu/srg/?page_id=1593
