Designing Risk-Averse Bidding Strategies in Sequential Auctions for Transportation Orders

Robu, Valentin and La Poutre, J.A. (2009) Designing Risk-Averse Bidding Strategies in Sequential Auctions for Transportation Orders. In, Studies in Computational Intelligence. , Springer Berlin / Heidelberg, 139-160.


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Designing efficient bidding strategies for agents participating in multiple, sequential auctions remains an important challenge for researchers in agent-mediated electronic markets. The problem is particularly hard if the bidding agents have complementary (i.e. super-additive) utilities for the items being auctioned, such as is often the case in distributed transportation logistics. This paper studies the effect that a bidding agent’s attitude towards taking risks plays in her optimal, decision-theoretic bidding strategy. We model the sequential bidding decision process as an MDP and we analyze, for a category of expectations of future price distributions, the effect that a bidder’s risk aversion profile has on her decision-theoretic optimal bidding policy. Next, we simulate the above strategies, and we study the effect that an agent’s risk aversion has on the chances of winning the desired items, as well as on the market efficiency and expected seller revenue. The paper extends the results presented in our previous work (reported in [1]), not only by providing additional details regarding the analytical part, but also by considering a more complex and realistic market setting for the simulations. This simulation setting is based on a real transportation logistics scenario [2]), in which bidders have to choose between several combinations (bundles) of orders that can be contracted for transportation.

Item Type: Book Section
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Divisions : Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Agents, Interactions & Complexity
ePrint ID: 268197
Accepted Date and Publication Date:
6 October 2009Published
Date Deposited: 11 Nov 2009 18:41
Last Modified: 31 Mar 2016 14:16
Further Information:Google Scholar

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