Visualization for Combinatorial Auctions

Abstract

Visualization converts raw data into meaningful graphical images that allow users to rapidly identify and explore values, trends, and patterns in their datasets. Visualization techniques often apply spatial relationships to abstract data to represent the relationships between data elements in a meaningful way. Our specific interest in this thesis is visualizing combinatorial datasets that contain all subsets of a collection of base elements. The combinatorial datasets we study encapsulate results from combinatorial auctions where multiple items are sold simultaneously to multiple bidders. Different bidding strategies and the relationships between them are revealed in our visualizations.

We propose a new 2D scheme for concisely visualizing combinatorial datasets.

The visualization displays concentric rings composed of arcs, with each base element subset mapped to a single arc. Equal sized subsets are placed on a common ring. The outermost ring contains subsets of size one. Interior rings contain larger subsets. The rings are positioned to try to overlap common base elements as much as possible. This allows viewers to search a local region of the visualization to study the behavior of a given base element (i.e., a given item offered within the combinatorial auction).

Additional visual features, including motion, color, and texture are applied to represent auction attributes like the identity of a bidder, which bids win in a particular stage of the auction, and so on. Our visualizations provide viewers with an efficient and effective way to observe how an auction progresses.