Kevin M. Lochner

Walverine: a Walrasian trading agent

TAC-02 was the third in a series of Trading Agent Competition events fostering research in automating trading strategies by showcasing alternate approaches in an open-invitation market game. TAC presents a challenging travel-shopping scenario where agents must satisfy client preferences for complementary and substitutable goods by interacting through a variety of market types. Michigans entry, Walverine, bases its decisions on a competitive (Walrasian) analysis of the TAC travel economy. Using this Walrasian model, we construct a decision-theoretic formulation of the optimal bidding problem, which Walverine solves in each round of bidding for each good. Walverines optimal bidding approach, as well as several other features of its overall strategy, are potentially applicable in a broad class of trading environments.

Bid expressiveness and clearing algorithms in multiattribute double auctions

We investigate the space of two-sided multiattribute auctions, focusing on the relationship between constraints on the offers traders can express through bids, and the resulting computational problem of determining an optimal set of trades. We develop a formal semantic framework for characterizing expressible offers, and show conditions under which the allocation problem can be separated into first identifying optimal pairwise trades and subsequently optimizing combinations of those trades. We analyze the bilateral matching problem while taking into consideration relevant results from multiattribute utility theory. Network flow models we develop for computing global allocations facilitate classification of the problem space by computational complexity, and provide guidance for developing solution algorithms. Experimental trials help distinguish tractable problem classes for proposed solution techniques.

Rule-Based Specification of Auction Mechanisms

Machine-readable specifications of auction mechanisms facilitate configurable implementation of computational markets, as well as standardization and formalization of the auction design space. We present an implemented rule-based scripting language for auctions, which provides constructs for specifying temporal control structure, while supporting orthogonal definition of mechanism policy parameters. Through a series of examples, we show how the language can capture much of the space of single-dimensional auctions, and can be extended to cover other novel designs.

Searching for walverine 2005

We systematically explore a range of variations of our TAC travel-shopping agent, Walverine. The space of strategies is defined by settings to behavioral parameter values. Our empirical game-theoretic analysis is facilitated by approximating games through hierarchical reduction methods. This approach generated a small set of candidates for the version to run in the TAC-05 tournament. We selected among these based on performance in preliminary rounds, ultimately identifying a successful strategy for Walverine 2005.

Information Feedback and Efficiency in Multiattribute Double Auctions

We investigate tradeoffs among expressiveness, operational cost, and economic efficiency for a class of multiattribute double-auction markets. To enable polynomial-time clearing and information feedback operations, we restrict the bidding language to a form of multiattribute OR-of-XOR expressions. We then consider implications of this restriction in environments where bidders’ preferences lie within a strictly larger class, that of complement-free valuations. Using valuations derived from a supply chain scenario, we show that an iterative bidding protocol can overcome the limitations of this language restriction. We further introduce a metric characterizing the degree to which valuations violate the substitutes condition, theoretically known to guarantee efficiency, and present experimental evidence that the actual efficiency loss is proportional to this metric.