Kevin Leyton-brown

We introduce a class of mechanisms, called bidding clubs, that allow agents to coordinate their bidding in auctions. Bidding clubs invite a set of agents to join, and each invited agent freely chooses whether to accept the invitation or to participate independently in the auction. Agents who join a bidding club flrst conduct a \knockout auction" within the club; depending

Action-graph games (AGGs) are a fully expres- sive game representation which can compactly express both strict and context-specific indepen- dence between players' utility functions. Ac- tions are represented as nodes in a graph G, and the payoff to an agent who chose the action s depends only on the numbers of other agents who chose actions connected to s. We

In combinatorial auctions, multiple goods are sold simultaneously and bidders may bid for arbitrary combinations of goods. Determining the outcome of such an auction is an optimization problem that is NP-complete in the general case. We propose two methods of overcoming this ...

ABSTRACT Behavioral game theory seeks to describe the way actual people (as compared to idealized, ``rational'' agents) act in strategic situations. Our own recent work has identified iterative models (such as quantal cognitive hierarchy) as the state of the art for predicting human play in unrepeated, simultaneous-move games [Wright and Leyton-Brown 2012]. Iterative models predict that agents reason iteratively about their opponents, building up from a specification of nonstrategic behavior called level-0. The modeler is in principle free to choose any description of level-0 behavior that makes sense for the given setting; however, in practice almost all existing work specifies this behavior as a uniform distribution over actions. In most games it is not plausible that even nonstrategic agents would choose an action uniformly at random, nor that other agents would expect them to do so. A more accurate model for level-0 behavior has the potential to dramatically improve predictions of human behavior, since a substantial fraction of agents may play level-0 strategies directly, and furthermore since iterative models ground all higher-level strategies in responses to the level-0 strategy. Our work considers ``meta-models'' of level-0 behavior: models of the way in which level-0 agents construct a probability distribution over actions, given an arbitrary game. We evaluated many such meta-models, each of which makes its prediction based only on general features that can be computed from any normal form game. We evaluated the effects of combining each new level-0 meta-model with various iterative models, and in many cases observed large improvements in the models' predictive accuracies. In the end, we recommend a meta-model that achieved excellent performance across the board: a linear weighting of features that requires the estimation of five weights.

The papers in these proceedings were presented at the 13th ACM Conference on Electronic Commerce (EC'12), held June 4-8 in Valencia, Spain. Since 1999 the ACM Special Interest Group on Electronic Commerce (SIGecom) has sponsored the leading scientific conference on advances in theory, systems, and applications for electronic commerce. The natural focus of the conference is on computer science issues, but the conference is interdisciplinary in nature, including research in economics and research related to (but not limited to) the following three non-exclusive focus areas: TF: Theory and Foundations (Computer Science Theory; Economic Theory) AI: Artificial Intelligence (AI, Agents, Machine Learning, Data Mining) EA: Experimental and Applications (Empirical Research, Experience with E-Commerce Applications) In addition to the main technical program, EC'12 featured four workshops and five tutorials. EC'12 was also co-located with the Autonomous Agents and Multiagent Systems...

Although some algorithms are better than others on average, there is rarely a best algo-rithm for a given problem. Instead, different algorithms often perform well on different problem instances. Not surprisingly, this phenomenon is most pronounced among algo-rithms for ...