Pick-a-bundle: a novel bundling strategy for selling multiple items within online auctions

ABSTRACT

In this paper, we consider the design of an agent that is able to autonomously make optimal bundling decisions when selling multiple heterogeneous items within existing online auctions. We show that while bundling the items together into a single lot is effective at reducing listing costs, it also results in a loss in auction revenue. To address this loss we introduce a novel bundling strategy, that we call pick-a-bundle, that can be implemented within any existing auction format. We show, mainly using simulations, that this new bundling strategy generates greater expected revenue than the complete bundle of all items, and, by inducing additional competition between bidders, it usually generates greater expected revenue than using separate auctions for each item. In order for our agent to accurately and efficiently calculate its expected revenue when using our new strategy, we derive a novel polynomial time algorithm for calculating the probability distributions of the sum of the top order statistics of i.i.d. variables drawn from any arbitrary distribution. Furthermore, we include in our analysis the strategic behaviour, in terms of bid shading, that the buyers may consider in our new auction format.

REFERENCES

• 1: W. Adams and J. Yellen. Commodity Bundling and the Burden of Monopoly. The Quarterly Journal of Economics, 90(3):475--498., 1976.
• 2: Yannis Bakos , Erik Brynjolfsson, Bundling Information Goods: Pricing, Profits, and Efficiency, Management Science, v.45 n.12, p.1613-1630, December 1999 [doi>10.1287/mnsc.45.12.1613]
• 3: Yannis Bakos , Erik Brynjolfsson, Bundling and Competition on the Internet, Marketing Science, v.19 n.1, p.63-82, January 2000 [doi>10.1287/mksc.19.1.63.15182]
• 4: Wolfram Conen , Tuomas Sandholm, Preference elicitation in combinatorial auctions, Proceedings of the 3rd ACM conference on Electronic Commerce, p.256-259, October 14-17, 2001, Tampa, Florida, USA [doi>10.1145/501158.501191]
• 5: Peter Cramton , Yoav Shoham , Richard Steinberg, Combinatorial Auctions, The MIT Press, 2006
• 6: A. Giovannucci, J. Rodriguez-Aguilar, and J. Cerquides. Multi-unit combinatorial reverse auctions with transformability relationships among goods. In Proceedings of First Workshop on Internet and Network Economics (WINE 2005), pages 858--867., Hong Kong, China, 2005.
• 7: P. Jehiel, M. Meyer-ter Vehn, and B. Moldovanu. Mixed bundling auctions. Journal of Economic Theory, 134(1):494--512, 2007.
• 8: Jeffrey O. Kephart , Christopher H. Brooks , Rajarshi Das , Jeffrey K. MacKie-Mason , Robert Gazzale , Edmund H. Durfee, Pricing information bundles in a dynamic environment, Proceedings of the 3rd ACM conference on Electronic Commerce, p.180-190, October 14-17, 2001, Tampa, Florida, USA [doi>10.1145/501158.501178]
• 9: A. Likhodedov and T. Sandholm. Approximating revenue-maximizing combinatorial auctions. In Proceedings of the Twelfth National Conference on Artificial Intelligence (AAAI-05), pages 267--274, Austin, Texas, USA, 2005.
• 10: R. P. McAfee, J. McMillan, and M. D. Whinston. Multiproduct monopoly, commodity bundling, and correlation of values. The Quarterly Journal of Economics, 104(2):371--383., 1989.
• 11: David C. Parkes, iBundle: an efficient ascending price bundle auction, Proceedings of the 1st ACM conference on Electronic commerce, p.148-157, November 03-05, 1999, Denver, Colorado, United States [doi>10.1145/336992.337032]
• 12: Shin-yi Wu , Lorin M. Hitt , Pei-yu Chen , G. Anandalingam, Customized Bundle Pricing for Information Goods: A Nonlinear Mixed-Integer Programming Approach, Management Science, v.54 n.3, p.608-622, March 2008 [doi>10.1287/mnsc.1070.0812]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence

General Terms:
Algorithms, Economics, Experimentation, Theory

Keywords:
auction, bundling, multiple items, revenue