Degree Candidacy: Master of Science in Mechanical Engineering
Date: Monday, June 23, 2014
Time: 1:00 PM
Location: 108 EIB
Advisor: Dr. Yue Wang
Committee Members: Dr. John R. Wagner and Dr. Haiying Shen
Title: Modeling and Design Strategy of Online Advertising Ecosystems
Internet has undoubtedly become a major and effective medium for advertising, and it will soon replace traditional advertising media. Online advertising ecosystem is consisted of thousands of agents, which can be divided into three types: users, advertisers and publishers. Each type of the agents plays an important role in the ecosystem. However, it is generally very difficult to model such a complex and large-scale system, not to mention the design of suitable strategies for the ecosystem. On the other side, replicator-mutator (RM) dynamics combine tools from evolutionary dynamics of populations, complex networks, and control theory to study evolution of behavior of agents in social networks. RM dynamics have been widely used in multi-agent networks to mimic an agent’s evolutionary states based on its own decision as well as environmental effects.
This thesis develops an analytical framework of a naturally fitted coevolving online advertising ecosystem based on RM dynamics in order to capture the evolution of all participants and design suitable advertisement strategies. Compared with real biological systems, online advertisement ecosystems share a lot of similarities. These similarities not only affect participants’ local characteristics, but also contribute to performance of the entire ecosystems. We first introduce the RM dynamic model for the user agents and advertiser agents, respectively. The effects of major parameters in the RM dynamics are studied. We demonstrate the evolution of the replicator dynamics, mutator dynamics, and RM dynamics using a toy problem. Next, we provide a detailed example using data crawled from a real online advertising ecosystem for tablet sales. We evaluate each parameter in the advertiser and user RM dynamics using the crawled data. We show the changes in ranking places and sales volumes of four chosen advertisers (Apple, Asus, Sumsung and Kindle) at four different stages based on the chosen parameters and corresponding evolutionary dynamics. Finally, we discuss suitable strategies for online advertising based on the proposed framework and conclude the thesis with a summary and discussion of future work.