In the average time it takes this page to load, an auction involving thousands of advertisers could have been executed. Each advertiser would determine a bid from the value they ascribe to displaying an ad on a page like this one for a user like yourself. The auctioneer—typically an ad exchange—ranks the bids based on various criteria and the winning advertiser earns an ad slot (position) on the page.
From an advertiser’s perspective, determining an optimal bid can be complicated. For instance, an advertiser may calculate the likelihood you will take certain actions once you view the ad, such as purchasing a product, as well as model the bidding behavior of its competitors and the rules of the auction. As advertisers have become more nuanced in their valuation of advertising opportunities, different mechanisms have emerged to facilitate sophisticated transactions between advertisers and publishers.
Ad exchanges such as Yahoo’s Right Media Exchange and Google’s DoubleClick Ad Exchange handle billions of advertising opportunities a day. The exchange rules themselves are complex. For instance, one advertiser may specify that it will pay the publisher only when a user purchases an item on its site after viewing an ad on the publisher’s site, whereas another may specify that it will pay only when a user clicks on its ad. The auction mechanism provided by the exchange must compare these offers for the publisher and finally determine which advertiser wins the auction and how much they should pay if contingent events (clicks, purchases, etc) occur.
For each economic opportunity, requesting, determining, and ranking bids spans a duration of milliseconds. Therefore, not only must the advertisers’ bidding algorithms and the auction mechanism be sophisticated, it must be fast. This mix of economics and computer science has produced a growing field called computational or algorithmic economics.
However, research in bidding agent strategies is often limited to stylized models. Few companies would open their proprietary bidding models to external scientific investigation. Since 2000, the Trading Agent Competition (TAC) series of tournaments has spurred researchers to develop improved automated bidding techniques for an array of challenging market domains. TAC provides an important venue for research teams to openly compete in realistic markets. Additionally, the repeatable nature of the simulations allows researchers to draw statistically valid conclusions.
The original TAC game presented a travel-shopping scenario; subsequent games have addressed problems in supply chain management, market design, and ad auctions. By continually introducing new games, the TAC series engages the community in an expanded set of strategic issues bearing on trading agent design and analysis. A key feature of these games is that, like most realistic market environments, they are sufficiently complicated to defy analytic solution. Such games often exhibit severely imperfect and incomplete information revealed over time throughout dynamic activity.
Participants in TAC develop autonomous agents that bid in various markets. In doing so, the designers face many issues found in electronic markets today. For instance, in the Ad Auctions game (TAC/AA), agents representing Internet advertisers bid for search-engine ad placement over a range of interrelated keyword combinations. Advertiser strategies combining online data analysis and bidding tactics compete to maximize profit over the simulated campaign horizon.
Lessons learned in early competitions helped to distill processes by which designers construct bidding agents for a given scenario. In particular, participants in TAC have produced general agent architectures applicable to a variety of market domains. These architectures modularize two core problems agents face: prediction and optimization. For instance, the winning agent in the 2009 and 2010 TAC/AA tournaments developed sophisticated models for predicting the bids of opponents in each keyword auction. These predictions allowed the agent to select the most profitable positions. As it happens, this is one of the key problems faced by search-engine marketing companies as they optimize advertising campaigns for their clients.
Participating research teams come from many of the top international institutions. Participating agent designers, as well as mechanism designers, have produced a considerable amount of academic research as a result of the competitions. In addition, many of the competitions’ participants have applied the lessons learned in TAC to industry: Yahoo, Google, Microsoft, various advertising agencies, etc.
The Twelfth Annual Trading Agent Competition (TAC-11) will be held in July, 2011 in Barcelona, Spain, in conjunction with IJCAI-11 and the workshop on Trading Agent Design and Analysis (TADA-11). TAC 2011 is comprised of five scenarios:
TAC/Travel is an eight-player travel-shopping scenario where each player acts as a travel agent, with the goal of assembling travel packages. Each agent acts on behalf of eight clients, who express their preferences for various aspects of the trip. The objective of the travel agent is to maximize the total satisfaction of its clients (the sum of the client utilities) minus expenditures. An annual competition for the game was run every year from 2000–2006. Over 70 entries have competed over the entire history of the yearly competitions, with many teams submitting entries over the course of multiple competitions. There are over 30 scholarly publications, including one book, that analyze the entries and the game itself.
TAC/SCM is a six-player supply chain management scenario where players act as manufacturers, who must compete with each other for both supplies and customers, and manage inventories and production facilities. The objective of each manufacturer is to maximize its accumulated profits over the course of a simulated year. An annual competition for the game has run every year since 2003. Participants have created over 150 entries and over 50 corresponding scholarly publications.
TAC/Market Design is a variable-player double auction market scenario where agents act as market specialists. Players in the game create respective marketplaces that facilitate transactions between potential buyers and sellers currently associated with the marketplace. Players compete over three objectives: profit share from fees, market share, and transaction efficiency. The corresponding competition has run every year since 2007. Participants have created over 30 entries and at least 10 corresponding publications.
TAC/AA is an eight-player sponsored search advertising scenario where agents act as advertisers. Players manage their respective ad campaign by selecting bids and ads to be displayed for each day. The objective of each advertiser is to maximize its accumulated sales profits net advertising expense over the course of a simulated 2-month campaign. Participants created 15 entries for the July 2009 inaugural competition.
Sustainable energy systems of the future will need more than efficient, clean, low-cost, renewable energy sources; they will also need efficient price signals that motivate sustainable energy consumption as well as a better real-time alignment of energy demand and supply. In Power TAC, agents act as retail brokers in a local power distribution region, purchasing power from a wholesale market as well as from local sources, such as homes and businesses with solar panels, and selling power to local customers and into the wholesale market. Retail brokers must solve a supply-chain problem in which the product is infinitely perishable, and supply and demand must be exactly balanced at all times.
Lemonade Stand Game
It is summer on Lemonade Island, and you need to make some cash. You set up a lemonade stand on the beach (which goes all around the island), as do two other entrepreneurs. There are twelve places to set up, evenly spaced around the island. Your price is fixed, and all customers go to the nearest lemonade stand. Every night, everyone moves under cover of darkness (simultaneously) and in the morning, their locations are fixed. There is no cost to move. After 100 days of summer, the game is over. The utility of the repeated game is the sum of the utilities of single-shot games.