AF: Small: Influencing and Improving Networks Formed by Strategic Agents

影响和改进战略主体形成的网络在科学研究的前沿，对我们的世界非常重要，越来越多的网络是由大量独立主体发展、建立和维护的，所有这些主体都按照自己的利益行事。这种具有战略代理的网络包括因特网、点对点文件共享方案、公司之间的商业合同以及代表人群之间关系的社会网络。虽然这些网络不能完全控制，但它们通常可以以有限的方式受到影响，有时会导致全球网络行为的显着改善。例如，这种影响可以包括给予一些代理不同行为的激励，改变网络的一小部分，甚至提供一些产生巨大差异的额外信息。本项目将开发具有可证明保证的方法和算法来影响战略代理网络，以改善整体网络行为。该项目将研究由战略代理组成的各种网络，并将特别关注互联网中客户-提供商系统和自治系统（AS）之间的对等合同。在这项研究的过程中，新的近似算法概念将被引入，并将产生改善网络整体质量的技术，并防止不受欢迎的实体对其产生不当影响。虽然自利主体的相互作用已经在许多领域得到了研究，但如何仅用有限的权力影响这些主体的系统研究从未做过。除了对算法博弈论和网络研究做出贡献外，这项研究还将为经济学、人工智能和社会科学开辟新的研究方向。

Influencing and Improving Networks Formed by Strategic AgentsAn increasing number of networks on the forefront of scientific research, and of great importance to our world, are developed, built, and maintained by a large number of independent agents, all of whom act in their own interests. Such networks with strategic agents include the Internet, peer-to-peer file sharing schemes, business contracts between companies, and social networks representing relationships between groups of people. While these networks cannot be fully controlled, they can often be influenced in a limited way, sometimes resulting in dramatic improvement of the global network behavior. For example, this influence can include giving a few agents incentives to behave differently, altering a small part of the network, or even providing some extra information that makes a huge difference. This project will develop methods and algorithms with provable guarantees for influencing networks of strategic agents in order to improve the overall network behavior.This project will study the formation of various networks by strategic agents, and will especially focus on the system of customer-provider and peering contracts between Autonomous Systems (AS's) in the Internet. In the process of this research, new approximation algorithm concepts will be introduced, and will yield techniques for improving the global qualities of a network, and for preventing undesirable entities from gaining undue influence over it. While interactions of self-interested agents have been studied in numerous fields, a systematic study of how to influence such agents with only a limited amount of power has never been done. Besides contributing to algorithmic game theory and the study of networks, this research will open up new research directions in economics, AI, and the social sciences.