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

影响和改善由战略代理人形成的网络越来越多的科学研究前沿网络，对我们的世界非常重要，是由大量独立代理人开发，建立和维护的，他们都以自己的利益行事。这种具有战略代理人的网络包括互联网、对等文件共享方案、公司之间的商业合同以及代表人群之间关系的社交网络。虽然这些网络无法完全控制，但它们通常可以受到有限的影响，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一些代理以不同的行为激励，改变网络的一小部分，甚至提供一些额外的信息，从而产生巨大的差异。本研究课题将开发具有可证明的保证的方法和算法，以改善网络的整体行为。本研究课题将研究由战略主体构成的各种网络，特别关注互联网上的自治系统（AS）之间的客户-供应商和对等合同系统。在这项研究的过程中，新的近似算法的概念将被引入，并将产生技术，用于提高网络的全局质量，并防止不良的实体获得不当的影响超过it. While相互作用的自利代理人已经在许多领域进行了研究，如何影响这样的代理人只有有限的权力从来没有做过系统的研究。除了对算法博弈论和网络研究做出贡献外，这项研究还将开辟经济学、人工智能和社会科学的新研究方向。

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.