EAGER: III: CIFRAM: Strategic Modeling of Dynamic Credit Networks

2008年的金融危机表明，企业之间复杂而不透明的信贷关系网络可能为金融不确定性在整个经济中的突然和意外传播奠定基础。在本项目中，动态信贷网络的新模型将为评估这类系统性风险提供基础，并为设计提高对资产价格波动和各种经济冲击的稳健性的制度和政策提供基础。研究人员将构建大规模模拟，其中关键参与者（银行、非银行金融公司和非金融企业）根据随时间变化的不确定信息做出动态信贷决策。对数据的分析有望产生关于信用网络的新见解，以及对复杂信用关系进行推理的技术。这种能力可以带来新的风险管理工具，支持单个金融公司以及中央银行和其他经济监管机构的应用。该项目将采用计算机科学家和经济学家近年来开发的基于图表的信任会计机制的模型。该研究采用基于代理的方法，其中决策者由计算对象实例化，执行旨在优化目标（利润）的策略，给定可用信息。关于系统特性的证据是通过模拟得出的，它提供了异质性，并适应复杂的信息和细粒度的动态。根据博弈论解的概念选择显著代理策略，有助于在基于代理的方法和主流经济分析方法之间架起桥梁。通过提供分析复杂金融网络的新方法，该项目有望对复杂信用关系的形成和演变产生新的见解。欲了解更多信息，请参阅项目网站：http://web.eecs.umich.edu/srg/?page_id=1593

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