We model the strategic decisions of web sites in content markets, where sites may reduce user search cost by aggregating content. Example aggregations include political news, technology, and other niche-topic websites. We model this market scenario as an extensive form game of complete information, where sites choose a set of content to aggregate and users associate with sites that are nearest to their interests. Thus, our scenario is a location game in which sites choose to aggregate content at a certain point in user-preference space, and our choice of distance metric, Jacquard distance, induces a lattice structure on the game. We provide two variants of this scenario: one where users associate with the first site to enter amongst sites of equal distances, and a second where users choose uniformly between sites at equal distances. We show that subgame perfect Nash equilibria exist for both games. While it appears to be computationally hard to compute equilibria in both games, we show a polynomial-time satisficing strategy called Frontier Descent for the first game. A satisficing strategy is not a best response, but ensures that earlier sites will have positive profits, assuming all subsequent sites also have positive profits. By contrast, we show that the second game has no satisficing solution.

The inaugural tournament for the Trading Agent Competition Ad Auctions game was held in July 2009. We describe the results, identifying key strategic behavior of the top agents in the competition. Through post-tournament simulation, we construct an empirical game using the agents and mechanism from the competition, and derive equilibria of this game. We then vary the auction mechanism used in simulation, and construct and solve an empirical game for each respective mechanism. Using the derived equilibria as predictions of play, we analyze revenue implications of these mechanism variations.

We present and evaluate the design of Deep Maize, our entry in the 2005 Trading Agent Competition Supply Chain Management scenario. The central idea is to decompose the problem by estimating the value of key resources in the game. We first create a high-level production schedule that considers cross-cutting constraints and future decisions, but abstracts aways from the details of sales and purchasing. We then make specific sales and purchasing decisions separately, coordinating these decisions with the high-level schedule using resource values derived from the schedule. All of these decisions are made using approximate optimization techniques and make use of explicit predictions about market conditions. Deep Maize was one of the most successful agents in the 2005 tournament, both in overall performance and on specific measures that emphasize coordination.