BSF:2014389: Networked Markets

近年来，在线社交网络已成为重要的沟通媒介，因此可以为市场提供有效的平台。 由于社交网络应用的规模和复杂性，这些环境中的市场需要一个不同于传统互联网市场设计的理论。 该项目旨在研究在网络环境中运作的市场。 本研究的目的是发展适合网络化市场设计的理论和实证方法。这项研究有三个主要目标。 第一个目标是研究网络级联中的激励机制。 信息级联在网络中传播信息，激励个人传播大级联是网络市场设计的核心问题。 第二个目标是研究网络化市场中的竞争。 在某些情况下，连锁反应是由竞争机构发起的，旨在影响个人采用他们推广的技术或想法。更一般地说，网络中的竞争激发了复杂的动态，这些动态很难分析。 本项目分析这种相互作用的均衡，以及在这些复杂环境中的市场设计原则。 第三个重点是开发数据挖掘技术来研究社会营销平台的经济学。这些市场的主要挑战是，社交网络服务无法控制发布的内容，用户经常在没有任何指示的情况下发布广告。这使得社交营销平台的分析成为检测隐蔽广告的具有挑战性的数据挖掘问题。这些分析和设计网络市场的方法可能会对互联网经济产生重大影响。

In recent years, online social networks have emerged as an important medium for communication, and as a result can provide an effective platform for markets. Due to the scale and complexity of social networking applications, markets in these environments require a different theory than the theory which guides the design of traditional internet markets. This project aims to study markets that operate in networked environments. The goal of this research is to develop both theory and empirical methods that are suitable for networked market design. This research has three major thrusts. The first goal is to study incentives in network cascades. Information cascades spread information in networks and incentivizing individuals to spread large cascades is a central problem for network market design. The second objective is to study competition in networked markets. In some cases, cascades are initiated by competing agencies that seek to influence individuals to adopt the technology or idea they promote. More generally, competition in networks spurs complex dynamics that are challenging to analyze. This project analyzes equilibrium of such interactions, as well as principles of market design in these complex environments. The third thrust develops data mining techniques to study the economics of social marketing platforms. The main challenge in such markets is that the social networking services do not control the content being published, and users often post advertisements without any indication. This makes analysis of social marketing platforms a challenging data mining problem of detecting covert advertisements. These methods for analysis and design of networked markets could have a significant impact on internet economics.