Mixed mechanisms for auctioning ranked items

dc.contributor.authorAlonso, Estrella
dc.contributor.authorSánchez-Soriano, Joaquín
dc.contributor.authorTejada Cazorla, Juan Antonio
dc.date.accessioned2023-06-17T08:57:48Z
dc.date.available2023-06-17T08:57:48Z
dc.date.issued2020-12-15
dc.description.abstractThis paper deals with the problem of designing and choosing auctioning mechanisms for multiple commonly ranked objects as, for instance, keyword auctions in search engines on Internet. We shall adopt the point of view of the auctioneer who has to select the auction mechanism to be implemented not only considering its expected revenue, but also its associated risk. In order to do this, we consider a wide parametric family of auction mechanisms which contains the generalizations of discriminatory-price auction, uniform-price auction and Vickrey auction. For completeness, we also analyze the Generalized Second Price (GSP) auction which is not in the family. The main results are: (1) all members of the family satisfy the four basic properties of fairness, no over-payment, optimality and efficiency, (2) the Bayesian Nash equilibrium and the corresponding value at risk for the auctioneer are obtained for the considered auctions, (3) the GSP and all auctions in the family provide the same expected revenue, (4) there are new interesting auction mechanisms in the family which have a lower value at risk than the GSP and the classical auctions. Therefore, a window opens to apply new auction mechanisms that can reduce the risk to be assumed by auctioneers.
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/63715
dc.identifier.doi10.3390/math8122227
dc.identifier.issn2227-7390
dc.identifier.officialurlhttps://doi.org/10.3390/math8122227
dc.identifier.relatedurlhttps://www.mdpi.com/2227-7390/8/12/2227
dc.identifier.urihttps://hdl.handle.net/20.500.14352/7732
dc.issue.number12
dc.journal.titleMathematics
dc.language.isoeng
dc.page.initial2227
dc.publisherMDPI
dc.rightsAtribución 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by/3.0/es/
dc.subject.cdu519.8
dc.subject.keywordRanked items auctions
dc.subject.keywordBayesian Nash equilibrium
dc.subject.keywordExpected revenue
dc.subject.keywordAuctioneer’s risk
dc.subject.keywordValue at risk
dc.subject.keywordEquilibrio bayesiano de Nash
dc.subject.ucmMatemáticas (Matemáticas)
dc.subject.ucmEstadística aplicada
dc.subject.ucmInvestigación operativa (Estadística)
dc.subject.unesco12 Matemáticas
dc.subject.unesco1207 Investigación Operativa
dc.titleMixed mechanisms for auctioning ranked items
dc.typejournal article
dc.volume.number8
dcterms.references1. Krishna, V. Auction Theory, 2nd ed.; Elsevier Monographs, Elsevier: Amsterdam, The Netherlands, 2009. 2. Klemperer, P. Auction: Theory and Practice; Princeton University Press: Princeton, NJ, USA; Oxford, UK, 2004. 3. Milgrom, P. Putting Auction Theory to Work; Cambridge Books; Cambridge University Press: Cambridge, UK, 2004. 4. Hortaçsu, A.; McAdams, D. Empirical Work on Auctions of Multiple Objects. J. Econ. Lit. 2018, 56, 84–157. [CrossRef] 5. Lahaie, S.; Pennock, D.M.; Saberi, A.; Vohra, R.V. Sponsored search auctions. In Algorithm Game Theory; Nisan, N., Roughgarden, T., Tardos, E., Vazirani, V.V., Eds.; Cambrige University Press: Cambrige, UK, 2007; Chapter 28; pp. 699–716. 6. Gomes, D.R.; Sweeney, S.K. Bayes-Nash equilibria of the generalized second-price auction. Games Econ. Behav. 2014, 86, 421–437. 7. Su, X.; Chan, S.; Peng, G. Generalized Second-Price Auction in Multi-Path Routing with Selfish Nodes. 2010. Available online: http://cs.yale.edu/homes/xs45/pdf/scp-gc2009.pdf (accessed on 1 November 2019). 8. Gergen, M.; Cannon, G.; Torgerson, S. A modest proposal: A market-based approach to generation interconnection process reform. Electr. J. 2008, 21, 8–18. 9. Schöne, S. Auctions in the Electricity Market: Bidding when Production Capacity is Constrained; Lecture Notes in Economics and Mathematical Systems; Springer: Berlin/Heidelberg, Germany, 2009; Volume 617. 10. Alonso, E.; Tejada, J. Risk optimal single-object auctions. Span. J. Econ. Financ. 2012, 35, 131–138. [CrossRef] 11. Alonso, E.; Sanchez-Soriano, J.; Tejada, J. A parametric family of two ranked objects auctions: Equilibria and associated risk. Ann. Oper. Res. 2015, 225, 141–160. 12. Aven, T.; Renn, O. Risk Management and Governance, Concepts, Guidelines and Applications, Risk, Governance and Society; Springer: Berlin/Heidelberg: Germany, 2010; Volume 16. 13. Sadgrove, K. The Complete Guide to Business Risk Management, 3rd ed.; Routledge: London, UK, 2015. 14. Koller, G. Risk Assessment and Decision Making in Business and Industry; Chapman and Hall/CRC: New York, NY, USA, 2005. 15. Crowley, L.G.; Hancher, D.E. Risk Assessment of Competitive Procurement. J. Constr. Eng. Manag. 1995, 121, 230–237. 16. Vickrey, W. Counterspeculation, Auctions and Competitive Sealed Tenders. J. Financ. 1961, 16, 8–37. [CrossRef] 17. Harsanyi, J.C. Games with Incomplete Information Played by Bayesian Players. Part I. Manag. Sci. 1967, 14, 159–189. 18. Harsanyi, J.C. Games with Incomplete Information Played by Bayesian Players. Part II. Manag. Sci. 1968, 14, 320–334. 19. Harsanyi, J.C. Games with Incomplete Information Played by Bayesian Players. Part III. Manag. Sci. 1968, 14, 486–502. 20. Holton, G. Value at Risk: Theory and Practice; Elsevier Academic Press: Cambridge, MA, USA, 2004. 21. Milgrom, P.; Weber, R. A theory of auctions and competitive bidding, II. In The Economic Theory of Auctions; Klemperer, P., Ed.; Edgar Publishing: Cheltenham, UK, 2000; Volume II, Chapter 8. 22. Weber, G.; An, R.J. Multiple-Object Auctions. In Auctions, Bidding, and Contracting: Uses and Theory; Engelbrecht-Wiggans, R., Shubik, M., Stark, R.M., EdS.; NYU Press: New York, NY, USA, 1983. 23. Sofronov, G. An Optimal Decision Rule for a Multiple Selling Problem with a Variable Rate of Offers. Mathematics 2020, 8, 690. [CrossRef] 24. Hortaçsu, A. Recent progress in the empirical analysis of multi-unit auction. Int. J. Ind. Organ. 2011, 29, 345–349. 25. Bajo-Buenestado, R. A Survey on Auctions in Electricity Markets: The Challenges of Recent Advances in Auction Theory; USAEE Working Paper No. 14-185; SSRN: Cleveland, OH, USA, 2014. 26. Von der Fehr, N.H.M.; Harbord, D. Spot Market Competition in the UK Electricity Industry. Econ. J. 1993, 103, 531–546. 27. Alonso, E.; Tejada, J. Equivalencia de ingresos en un duopolio eléctrico con costes estocásticos. Lat. Am. J. Econ. 2010, 47, 191–215. 28. Sancho, J.; Sanchez-Soriano, J.; Chazarra, J.A.; Aparicio, J. Design and implementation of a decision support system for competitive electricity markets. Decis. Support Syst. 2008, 44, 765–784. 29. Das, D.; Wollenberg, B.F. Risk Assessment of Generators Bidding in Day-Ahead Market. IEEE Trans. Power Syst. 2005, 20, 416–424. 30. Ausubel, L.; Cramton, P.; Pycia, M.; Rostek, M.; Weretka, M. Demand Reduction and Inefficiency in Multi-Unit Auctions. Rev. Econ. Stud. 2014, 81, 1366–1400. 31. Fang, D.; Ren, Q.; Yu, Q. How Elastic Demand Affects Bidding Strategy in Electricity Market: An Auction Approach. Energies 2019, 12, 9. 32. Huibers, F.E. Towards an Optimal IPO Mechanism. J. Risk Financ. Manag. 2020, 13, 115. 33. Janssen, M.; Karamychev, V.; Maasl, E. Pooled Auctions with Multiple Bids and Preference Lists. J. Inst. Theor. Econ. JITE 2010, 166, 286–298. 34. Mishra, D.; Garg, R. Descending price multi-item auctions. J. Math. Econ. 2006, 42, 161–179. 35. Cramton, P. The FCC Spectrum Auctions: An Early Assessment. J. Econ. Manag. Strategy 1997, 6, 431–495. 36. Cramton, P. Spectrum Auction Design. Rev. Ind. Organ. 2013, 42, 161–190. 37. Cramton, P.; Ockenfels, A. The German 4G Spectrum Auction: Design and Behaviour. Econ. J. 2017, 127, F305–F324. [CrossRef] 38. Milgrom, P. Game theory and the spectrum auctions. Eur. Econ. Rev. 1998, 42, 771–778. 39. Gershkov, A.; Moldovanu, B. Dynamic Revenue Maximization with Heterogeneous Objects: A Mechanism Design Approach. Am. Econ. J. Microecon. 2009, 1, 98–168. 40. Binmore, K.; Klemperer, P. The Biggest Auction Ever: The Sale of the British 3G Telecom Licenses. Econ. J. 2002, 112, C74–C96. 41. Klemperer, P. How (Not) to Run Auctions: The European 3G Telecom Auctions. Eur. Econ. Rev. 2002, 46, 829–845. 42. Klemperer, P. The Product-Mix Auction: A New Auction Design for Differentiated Goods. J. Eur. Econ. Assoc. 2010, 8, 526–536. (Reprinted in The Handbook of Spectrum Auction Design, Bichler, M. and Goeree, J.; (eds.) 2017). 43. Myerson, R.B. Optimal Auction Design. Math. Oper. 1981, 6, 58–73. 44. Riley, J.G.; Samuelson, W.F. Optimal Auctions. Am. Rev. 1981, 71, 381–392. 45. Shapley, L.; Shubik, M. The assignment game I: The core. Int. J. Game Theory 1972, 1, 111–130. 46. Aggarwal, G.; Goel, A.; Motwani, R. Bayes-Nash equilibria of the generalized second-price auction. In Proceedings of the EC ’06: Proceedings of the 7th ACM conference on Electronic commerce, Ann Arbor, MI, USA, 11–15 June 2006; Volume 2006, pp. 1–7. 47. Varian, H.R. Position auctions. Int. J. Ind. Organ. 2007, 25, 1163–1178. 48. Edelman, B.; Ostrovsky, M.; Schwarz, M. Internet advertising and the generalized second-price auction: Selling billions of dollars worth of keywords. Am. Econ. Rev. 2007, 97, 242–259. 49. Yenmez, M.B. Pricing in position auctions and online advertising. Econ. Theory 2014, 55, 243–256. 50. Lu, Z.; Riis, C. Bayes-Nash Equilibria in Generalized Second Price Auctions with Allocative Externalities; Publication No. 1-2016; CREAM (Center for Research in Economics and Management): Zürich, Switzerland, 2016; Volume 2016. 51. Watts, A.Generalized Second Price Auctions over a Network. Games 2018, 9, 67. 52. Menezes, F.M.; Monteiro, P.K. Simultaneous Pooled Auctions.J. Real Estate Financ. Econ. 1998, 17, 219–232. 53. Han, X.; Liu, S. Bayes-Nash Equilibrium of the Generalized First-Price Auction. Math. Probl. Eng. 2015, 2015, 301734. 54. Dutting, P.; Fischer, F.; Parkes, D.C. Expressiveness and robustness of first-price position auctions. Math. Oper. Res. 2019, 44, 196–211. 55. Garg, D.; Narahari, Y. An Optimal Mechanism for Sponsored Search Auctions on the Web and Comparison With Other Mechanisms. IEEE Trans. Autom. Sci. Eng. 2009, 6, 641–657. 56. Edelman, B.; Schwarz, M. Optimal Auction Design and Equilibrium Selection in Sponsored Search Auctions. Am. Econ. Rev. 2010, 100, 597–602. 57. Feng, J. Optimal Mechanism for Selling a Set of Commonly-Ranked Objects. Mark. Sci. 2008, 27, 501–512. 58. Liu, D.; Chen, J.; Whinston, A.B. Ex Ante Information and the Design of Keyword Auctions. Inf. Syst. Res. 2010, 21, 133–153. 59. Li, J.; Liu, D.; Liu, S. Optimal keyword auctions for optimal user experiences. Decis. Support Syst. 2013, 56, 450–461. 60. Moreno, D.; Wooders, J. Reserve prices in auctions with entry when the seller is risk-averse. Econ. Lett. 2017, 154, 6–9. 61. Yang, Q.; Hong, X.; Wang, Z.; Zhang, H. Reserve Prices of Risk-Averse Search Engine in Keyword Auctions with Advertisers’ Endogenous Investment; RAIRO-Operations Research: Les Ulis Cedex, France, 2019. 62. Waehrer, K.; Harstad, R.M.; Rothkopf, M.H. Auction form preferences of risk-averse bid takers. RAND J. Econ. 1998, 29, 179–192. 63. Beltran, F.; Santamaria, N. A measure of the variability of revenue in auctions: A look at the Revenue Equivalence Theorem. J. Appl. Math. Decis. Sci. 2006, 2006, 27417. 64. Krishna, V. Auction Theory; Elsevier Academic Press: Cambridge, MA, USA, 2002. 65. Aqlan, F. A software application for rapid risk assessment in integrated supply chains. Expert Syst. Appl. 2016, 43, 109–116. 66. Samantra, C.; Datta, S.; Mahapatra, S.S. Risk assessment in IT outsourcing using fuzzy decision-making approach: An Indian perspective. Expert Syst. Appl. 2014, 41, 4010–4022. 67. Yang, M.; Wong, S.C.P.; Coid, J. The Efficacy of Violence Prediction: A Meta-Analytic Comparison of Nine Risk Assessment Tools. Psychol. Bull. 2010, 136, 740–767. 68. Blume, M.E. On the assessment of risk. J. Financ. 1971, 26, 1–10. 69. Aparicio, J.; LLorca, N.; Pulido, M.; Sanchez-Soriano, J.; Sancho, J. Ranking auctions: A cooperative approach. Int. Game Theory Rev. 2012, 14, 1250003. 70. Aparicio, J.; Sanchez, E.; Sanchez-Soriano, J.; Sancho, J. Design and Implementation of a Decision Support System for Analysing Ranking Auction Markets for Internet Search Services. In Decision Support Systems; Jao, C.S., Ed.; INTECH: Rijeka, Croatia, 2010; pp. 261–280. 71. Kim, C.; Park, S.; Kwon, K.; Chang, W. How to select search keywords for online advertising depending on consumer involvement: An empirical investigation. Expert Syst. Appl. 2012, 39, 594–610. 72. Kamijo, Y. Bidding behaviors for a keyword auction in a sealed-bid environment. Decis. Support Syst. 2013, 56, 371–378. 73. Lim, W.S.; Tang, C.S. An auction model arising from an Internet search service provider. Eur. J. Oper. 2006, 172, 956–970. 74. Feng, J.; Shen, Z.-J.M.; Zhan, R.L. Ranked Items Auctions and Online Advertisement. Prod. Oper. Manag. 2007, 16, 510–522.
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