In this paper we introduce a new option pricing mechanism for reducing the exposure problem encountered by bidding agents with complementary valuations when participating in sequential, second-price auction markets. Existing option pricing models have two main drawbacks: they either apply fixed exercise prices, which may deter bidders with low valuations, thereby decreasing allocative efficiency, or options are offered for free, in which case bidders are less likely to exercise them, thereby reducing seller revenues. The proposed mechanism involving flexibly priced options addresses these problems by calculating the exercise price as well as the option price based on the bids received during an auction. For this new model, which extends and encompasses all the previous models examined, we derive the optimal strategies for a bidding agent with complementary preferences. Finally, we use these strategies to evaluate the proposed option mechanism through Monte-Carlo simutions, and compare it to existing mechanisms, both in terms of the seller revenue and the social welfare. We show that our new mechanism achieves higher market efficiency compared to having no options and free options, while achieving higher revenues for the seller than any existing option mechanism.

In this paper we describe the IAMwildCAT agent, designed for the TAC Market Design game which is part of the International Trading Agent Competition. The objective of an agent in this competition is to effectively manage and operate a market that attracts traders to compete for resources in it. This market, in turn, competes against markets operated by other competition entrants and the aim is to maximise the market and profit share of the agent, as well as its transaction success rate. To do this, the agent needs to continually monitor and adapt, in response to the competing marketplaces, the rules it uses to accept offers, clear the market, price the transactions and charge the traders. Given this context, this paper details IAMwildCAT's strategic behaviour and describes the wide techniques we developed to operationalise this. Finally, we empirically analyse our agent in different environments, including the 2007 competition where it ranked first.

In previous work we have introduced a principled methodology for systematically exploring the space of bidding strategies when agents participate in a significant number of simultaneous auctions, and thus finding an analytical solution is not possible. We decompose the problem into sub-problems and then use rigorous experimentation to determine the best partial strategies. In this paper we clarify and extend our methodology. We discuss our agent design for TAC 2003 and furthermore the changes to our agent as a result of the rule changes in TAC 2004. We also present a "full" set of experiments for determining an overall "optimal" strategy in the 2003 and 2004 Trading Agent Competition (TAC). Our agent was created by using the results of this methodology and has consistently been the top-scoring agent in several rounds of the TAC competition.