AF: Large: Networks, Learning and Markets with Strategic Agents

在过去的十年里，一种活跃的算法研究已经开发出了分析自利益主体系统的技术。分析这类系统的一个关键挑战是在大尺度上预测聚集体的性质；这需要对系统中的全局现象得出结论，这些现象的行为目前只能在个体或个体对的水平上得到很好的理解。在计算和社会科学中，从微观层面描述系统的宏观特性得出结论是很重要的。例如，市场价格产生于个体交易者的微观互动。要理解市场的正常运作及其失灵，需要能够弥合这些不同规模的解决方案之间差距的方法。这个项目利用网络的思想和从算法博弈论中学习来弥合微观和宏观的差距。网络研究考虑了参与者受网络结构约束的议价和交易理论。这包括网络中代理人之间权力分配的模型，以及通过网络中做市中介的相互作用战略性地产生市场价格的模型。该研究开发了市场失灵的模型，尤其是在2008年全球金融危机中发挥了关键作用的信任连锁崩溃。该项目采用学习模型来捕捉感知到的交易对手风险（交易伙伴完成交易的能力）如何在市场中扩散。对金融市场信任的研究可能有助于就恢复市场信任的方法进行更广泛的政策辩论。目前还缺乏分析技术，可以跟踪地操纵非平凡的学习动态，以揭示所产生的网络级结果，例如级联。该研究将为分析金融市场中信任的决定因素和演变提供工具。该项目还将为跨越许多学科的入门课程的开发提供信息，为具有广泛背景的本科生提供基于计算的视角，以推理相互作用的代理网络的行为和后果。

An active line of algorithmic research over the past decade has developed techniques for analyzing systems of self-interested agents. A crucial challenge in analyzing such systems is to predict aggregate properties at large scales; this requires drawing conclusions about global phenomena in systems whose behavior is currently only well-understood at the level of individual agents or pairs of agents. Deriving conclusions about macroscopic properties of systems described at a microscopic level is important in both computing and the social sciences. Market prices, for instance, arise from the microscopic interactions of individual traders. Understanding both the normal functioning of markets and their failure requires methods that can bridge the gap between these different scales of resolution.This project uses ideas about networks and learning from algorithmic game theory to bridge the micro-macro gap. The research on networks considers theories of bargaining and trade in which participants are constrained by a network structure. This includes models for the distribution of power among agents in a network, as well as models in which prices in a market arise strategically through the interaction of market-making intermediaries in a network. The research develops models of market failures, particularly the kinds of cascading breakdowns of trust that played a crucial role in the global financial crisis in 2008. The project employs learning models to capture how perceived counterparty risk -- the ability of one's trading partner to complete a transaction --- spreads through a market.The research on trust in financial markets can potentially contribute to broader policy debates about methods for restoring trust in markets. Currently there is a lack of analytical techniques that can tractably manipulate non-trivial learning dynamics to uncover the resulting network-level consequences, such as cascades. The research will provide tools for analyzing the determinants and evolution of trust in financial markets.The project will also inform the development of introductory courses that cut across many disciplines, providing undergraduates from a wide range of backgrounds with a computationally grounded perspective for reasoning about the behavior and consequences of networks of interacting agents.