T1: Ad Exchanges: Auctions and Optimizations
Location: Maxwell Dworkin (MD G115), 33 Oxford Street

Tutor: M. Muthukrishnan (Google Research and Rutgers University)

Activities on the Internet can be abstracted as interactions due to the users who navigate to various web pages, the publishers who control the web pages and generate the content in them, and advertisers who wish to get the attention of the users using the publishers as the channel for placing ads on the pages.  Precisely what ads show when a user accesses a page is a detailed process. Traditionally, this is determined by negotiations between publishers and advertisers.  An emerging way of selling and buying ads on the Internet is via an exchange that brings sellers (publishers) and buyers (advertisers) together to a common marketplace.   Ad exchanges are recent.  Publishers expect to get the best price from the exchange, better than from any specific ad network; in addition, publishers get liquidity.  Advertisers get access to a large inventory at the exchange, and in addition, the ability to target more precisely across web pages.  Finally, the exchange is a clearing house ensuring the flow of money.  In many ways, these ad exchanges are modeled after financial stock exchanges. Since 2005 when RightMedia appeared, ad exchanges have become popular. In Sept 2009, RightMedia averaged 9 billion transactions a day with 100's of thousands of buyers and sellers. Recently, DoubleClick announced their new ad exchange. It seems ad exchanges are likely to become a major platform for trading ads. In this tutorial, we will discuss the context of ad exchanges as well as the auction and optimization problems that arise.

T2: Algorithmic Theory and Practice in Online Ad Serving
Location: Maxwell Dworkin (MD G115), 33 Oxford Street

Tutors: Vahab Mirrokni and Aranyak Mehta (Google Research)

As an important part of any ad system, online ad serving is a rich source of challenging algorithmic, learning, and economic problems.  In this tutorial, we survey recent algorithmic results known in the
context of online ad serving in various ad systems, including sponsored search and contextual ads, graphical (or display) ads, and other hybrid and multi-layer markets. In addition, we will discuss further theoretical as well as practical problems which come up in this area.

T3: Empirical analysis of auctions and games
Location: Maxwell Dworkin (MD G125), 33 Oxford Street

Tutors: Denis Nekipelov (UC Berkeley and Microsoft Research/SVC)

Game theory allows us to answer qualitative questions regarding the behavior of strategic agents. For instance, given a particular game, what will the strategies of players be, will the equilibrium be efficient, how many equilibria are there? The answers can depend on multiple features of the game such as the structure of payoffs, information and beliefs of players.  In practice, even if we know that a certain interaction between people or firms can be modeled by a game, we usually know little about the primitive structure of this game, i.e. we don’t know the exact payoffs from different actions, equilibrium selection mechanism or private information of players.  However, we can have the data that shows what were the actions of players in a particular game.
In this tutorial, we will look at some modern empirical techniques that allow us to recover the “primitive” structure of the game from the data on its past realizations.  We will talk about static games of complete and incomplete information and learn fast and efficient methods for modeling these games using simple statistical software. We will study some tools that will allow us to empirically discriminate between different game models using the data.  During the discussion of the games of incomplete information we will also consider auctions as a special case. We will learn how to recover unobserved valuations of bidders from their bids in auctions which are not necessarily truthful. We will also look at some examples where we can use such estimates for predicting welfare and revenue when we make changes to the auction mechanism.

T4: Computational Voting Theory
Location: Maxwell Dworkin (MD G125), 33 Oxford Street

Tutors: Vincent Conitzer (Duke) and Ariel Procaccia (Harvard)

Social choice theory deals with the aggregation of individual preferences. This is a well established and well studied field--the mathematical investigation of social choice dates back to the 18th century. In recent years computer scientists have become involved with social choice theory, giving rise to a new field called computational social choice. One of the main focal points of this field is the computational aspects of elections, or computational voting theory. Computational voting theory provides key techniques for multiagent settings where a decision must be made based on the preferences of multiple agents (e.g., multiple agents that have to decide on a joint plan, in spite of having conflicting preferences over the plans).  In this tutorial we give an overview of computational voting theory. We touch on subjects such as the complexity of manipulation in elections, and algorithms (exact and approximate) for winner determination under intractable voting rules, which lie at the intersection of multiagent systems, artificial intelligence, theoretical computer science, economics, and social choice.