Skip to main content

Automated Mechanism Design: A New Application Area for Search Algorithms

  • Conference paper
Principles and Practice of Constraint Programming – CP 2003 (CP 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2833))

Abstract

Mechanism design is the art of designing the rules of the game (aka. mechanism) so that a desirable outcome (according to a given objective) is reached despite the fact that each agent acts in his own self-interest. Examples include the design of auctions, voting protocols, and divorce settlement procedures. Mechanisms have traditionally been designed manually for classes of problems. In 2002, Conitzer and Sandholm introduced the automated mechanism design approach, where the mechanism is computationally created for the specific problem instance at hand. This approach has several advantages: 1) it can yield better mechanisms than the ones known to date, 2) it applies beyond the problem classes studied manually to date, 3) it can circumvent seminal economic impossibility results, and 4) it shifts the burden of design from man to machine. In this write-up I overview the approach, focusing on problem representations, computational complexity, and initial applications. I also lay out an agenda for future research in this area.

This material is based upon work supported by the National Science Foundation under CAREER Award IRI-9703122, Grant IIS-9800994, ITR IIS-0081246, and ITR IIS-0121678.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Armstrong, M.: Optimal multi-object auctions. Review of Economic Studies 67, 455–481 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  2. Arrow, K.: The property rights doctrine and demand revelation under incomplete information. In: Boskin, M. (ed.) Economics and human welfare. Academic Press, New York (1979)

    Google Scholar 

  3. Aumann, R.: Acceptable points in general cooperative n-person games. Contributions to the Theory of Games, vol. IV. Princeton University Press, Princeton (1959)

    Google Scholar 

  4. Avery, C., Hendershott, T.: Bundling and optimal auctions of multiple products. Review of Economic Studies 67, 483–497 (2000)

    Article  MATH  Google Scholar 

  5. Bartholdi III, J.J., Orlin, J.B.: Single transferable vote resists strategic voting. Social Choice and Welfare 8(4), 341–354 (1991)

    Article  MATH  MathSciNet  Google Scholar 

  6. Bartholdi III, J.J., Tovey, C.A., Trick, M.A.: The computational difficulty of manipulating an election. Social Choice and Welfare 6(3), 227–241 (1989)

    Article  MATH  MathSciNet  Google Scholar 

  7. Bernheim, B.D., Peleg, B., Whinston, M.D.: Coalition-proof Nash equilibria: I concepts. Journal of Economic Theory 42(1), 1–12 (1987)

    Article  MATH  MathSciNet  Google Scholar 

  8. Blum, A., Jackson, J., Sandholm, T., Zinkevich, M.: Preference elicitation and query learning. In: Conference on Learning Theory (COLT), Washington, D.C (2003)

    Google Scholar 

  9. Clarke, E.H.: Multipart pricing of public goods. Public Choice 11, 17–33 (1971)

    Article  Google Scholar 

  10. Conen, W., Sandholm, T.: Preference elicitation in combinatorial auctions. In: Proceedings of the ACM Conference on Electronic Commerce (ACM-EC), Tampa, FL, October 2001, pp. 256–259 (2001); A more detailed description of the algorithmic aspects appeared in the IJCAI-2001 Workshop on Economic Agents, Models, and Mechanisms, pp. 71–80, Extended abstract

    Google Scholar 

  11. Conen, W., Sandholm, T.: Differential-revelation VCG mechanisms for combinatorial auctions. In: Padget, J., Shehory, O., Parkes, D.C., Sadeh, N.M., Walsh, W.E. (eds.) AMEC 2002. LNCS (LNAI), vol. 2531, pp. 34–51. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  12. Conen, W., Sandholm, T.: Partial-revelation VCG mechanism for combinatorial auctions. In: Proceedings of the National Conference on Artificial Intelligence (AAAI), Edmonton, Canada, pp. 367–372 (2002)

    Google Scholar 

  13. Conitzer, V., Lang, J., Sandholm, T.: How many candidates are needed to make elections hard to manipulate? In: Theoretical Aspects of Rationality and Knowledge (TARK IX), Bloomington, Indiana, USA (2003)

    Google Scholar 

  14. Conitzer, V., Sandholm, T.: Automated mechanism design: Complexity results stemming from the single-agent setting (2002) (Draft)

    Google Scholar 

  15. Conitzer, V., Sandholm, T.: Complexity of manipulating elections with few candidates. In: Proceedings of the National Conference on Artificial Intelligence (AAAI), Edmonton, Canada, pp. 314–319 (2002)

    Google Scholar 

  16. Conitzer, V., Sandholm, T.: Complexity of mechanism design. In: Proceedings of the 18th Annual Conference on Uncertainty in Artificial Intelligence (UAI 2002), Edmonton, Canada, pp. 103–110 (2002)

    Google Scholar 

  17. Conitzer, V., Sandholm, T.: Vote elicitation: Complexity and strategy-proofness. In: Proceedings of the National Conference on Artificial Intelligence (AAAI), pp. 392–397, Edmonton, Canada (2002)

    Google Scholar 

  18. Conitzer, V., Sandholm, T.: An algorithm for single-agent deterministic automated mechanism design without payments. In: IJCAI 2003 workshop on Distributed Constraint Reasoning (DCR), Acapulco, Mexico (2003)

    Google Scholar 

  19. Conitzer, V., Sandholm, T.: Applications of automated mechanism design. In: UAI 2003 workshop on Bayesian Modeling Applications, Acapulco, Mexico (2003)

    Google Scholar 

  20. Conitzer, V., Sandholm, T.: Automated mechanism design for a selfinterested designer. In: Proceedings of the ACM Conference on Electronic Commerce (ACM-EC), San Diego, CA, pp. 232–233 (2003); Poster paper. Full-length, available at http://www.cs.cmu.edu/draft/~sandholm/

  21. Conitzer, V., Sandholm, T.: Automated mechanism design with a structured outcome space (2003)

    Google Scholar 

  22. Conitzer, V., Sandholm, T.: Computational criticisms of the revelation principle. In: AAMAS 2003 workshop on Agent-Mediated Electronic Commerce (AMEC), Melbourne, Australia (2003) (poster paper)

    Google Scholar 

  23. Conitzer, V., Sandholm, T.: Universal voting protocol tweaks to make manipulation hard. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI), Acapulco, Mexico (2003)

    Google Scholar 

  24. d’Aspremont, C., Gérard-Varet, L.A.: Incentives and incomplete information. Journal of Public Economics 11, 25–45 (1979)

    Article  Google Scholar 

  25. Gibbard, A.: Manipulation of voting schemes. Econometrica 41, 587–602 (1973)

    Article  MATH  MathSciNet  Google Scholar 

  26. Green, J., Laffont, J.-J.: Incentives in Public Decision Making. North-Holland, Amsterdam (1979)

    MATH  Google Scholar 

  27. Groves, T.: Incentives in teams. Econometrica 41, 617–631 (1973)

    Article  MATH  MathSciNet  Google Scholar 

  28. Hsu, E.: Automated mechanism design: Type space and exponential auction. In: AAMAS 2003 workshop on Evolutionary Game Theory for Learning in MAS, Melbourne, Australia (2003)

    Google Scholar 

  29. Hudson, B., Sandholm, T.: Effectiveness of preference elicitation in combinatorial auctions. In: AAMAS 2002 workshop on Agent-Mediated Electronic Commerce (AMEC), Bologna, Italy, March; Extended version: Carnegie Mellon University, Computer Science Department, CMU-CS-02-124 (2002); Also: Stanford Institute for Theoretical Economics workshop (SITE-02).

    Google Scholar 

  30. Jameson, A., Hackl, C., Kleinbauer, T.: Evaluation of automatically designed mechanisms. In: UAI 2003 workshop on Bayesian Modeling Applications, Acapulco, Mexico (2003)

    Google Scholar 

  31. Larson, K., Sandholm, T.: Bargaining with limited computation: Deliberation equilibrium. Artificial Intelligence 132(2), 183–217 (2001); Short early version appeared in the Proceedings of the National Conference on Artificial Intelligence (AAAI), Austin, TX, pp. 48–55(2000)

    Article  MATH  MathSciNet  Google Scholar 

  32. Larson, K., Sandholm, T.: Computationally limited agents in auctions. In: AGENTS-01 Workshop of Agents for B2B, Montreal, Canada, May 2001, pp. 27–34 (2001)

    Google Scholar 

  33. Larson, K., Sandholm, T.: Costly valuation computation in auctions. In: Theoretical Aspects of Rationality and Knowledge (TARK VIII), Sienna, Italy, July 2001, pp. 169–182 (2001)

    Google Scholar 

  34. Larson, K., Sandholm, T.: An alternating offers bargaining model for computationally limited agents. In: International Conference on Autonomous Agents and Multi-Agent Systems, Bologna, Italy (July 2002)

    Google Scholar 

  35. Lavi, R., Mu’Alem, A., Nisan, N.: Towards a Characterization of Truthful Combinatorial Auctions, April 8th (2003) (Draft)

    Google Scholar 

  36. Likhodedov, A., Sandholm, T.: Auction mechanism for optimally trading off eficiency and revenue. In: AAMAS workshop on Agent-Mediated Electronic Commerce (AMEC V), Melbourne, Australia (2003)

    Google Scholar 

  37. Mas-Colell, A., Whinston, M., Green, J.R.: Microeconomic Theory. Oxford University Press, Oxford (1995)

    Google Scholar 

  38. Maskin, E.S., Riley, J.: Optimal multi-unit auctions. In: Hahn, F. (ed.) The Economics of Missing Markets, Information, and Games,  ch. 14, pp. 312–335. Clarendon Press, Oxford (1989)

    Google Scholar 

  39. Myerson, R.: Optimal auction design. Mathematics of Operation Research 6, 58–73 (1981)

    Article  MATH  MathSciNet  Google Scholar 

  40. Myerson, R., Satterthwaite, M.: Efficient mechanisms for bilateral exchange. Journal of Economic Theory 28, 265–281 (1983)

    Article  MathSciNet  Google Scholar 

  41. Nisan, N., Ronen, A.: Algorithmic mechanism design. Games and Economic Behavior 35, 166–196 (2001); Early version in STOC-99

    Article  MATH  MathSciNet  Google Scholar 

  42. Parkes, D.C., Ungar, L.: Iterative combinatorial auctions: Theory and practice. In: Proceedings of the National Conference on Artificial Intelligence (AAAI), Austin, TX, August 2000, pp. 74–81 (2000)

    Google Scholar 

  43. Sandholm, T.: An implementation of the contract net protocol based on marginal cost calculations. In: Proceedings of the National Conference on Artificial Intelligence (AAAI), Washington, D.C, July 1993, pp. 256–262 (1993)

    Google Scholar 

  44. Sandholm, T.: Negotiation among Self-Interested Computationally Limited Agents. PhD thesis, University of Massachusetts, Amherst (1996), Available at http://www.cs.cmu.edu/~sandholm/dissertation.ps

  45. Sandholm, T.: Issues in computational Vickrey auctions. International Journal of Electronic Commerce 4(3), 107–129 (2000); special Issue on Applying Intelligent Agents for Electronic Commerce. A short, early version appeared at the 2nd International Conference on Multi–Agent Systems (ICMAS), pages 299– 306 (1996)

    Google Scholar 

  46. Sandholm, T., Gilpin, A.: Sequences of take-it-or-leave-it offers: Nearoptimal auctions without full valuation revelation. In: Faratin, P., Parkes, D.C., Rodríguez-Aguilar, J.-A., Walsh, W.E. (eds.) AMEC 2003. LNCS (LNAI), vol. 3048, pp. 73–91. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  47. Satterthwaite, M.A.: Strategy-proofness and Arrow’s conditions: existence and correspondence theorems for voting procedures and social welfare functions. Journal of Economic Theory 10, 187–217 (1975)

    Article  MATH  MathSciNet  Google Scholar 

  48. Vickrey, W.: Counterspeculation, auctions, and competitive sealed tenders. Journal of Finance 16, 8–37 (1961)

    Article  Google Scholar 

  49. Vohra, R.V.: Research problems in combinatorial auctions. Mimeo, version October 29 (2001)

    Google Scholar 

  50. Wurman, P.R., Wellman, M.P.: AkBA: A progressive, anonymousprice combinatorial auction. In: Proceedings of the ACM Conference on Electronic Commerce (ACM-EC), Minneapolis, MN, October 2000, pp. 21–29 (2000)

    Google Scholar 

  51. Yokoo, M.: The characterization of strategy/false-name proof combinatorial auction protocols: Price-oriented, rationing-free protocol. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI), Acapulco, Mexico (August 2003)

    Google Scholar 

  52. Zinkevich, M., Blum, A., Sandholm, T.: On polynomial-time preference elicitation with value queries. In: Proceedings of the ACM Conference on Electronic Commerce (ACM-EC), San Diego, CA, pp. 176–185 (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sandholm, T. (2003). Automated Mechanism Design: A New Application Area for Search Algorithms. In: Rossi, F. (eds) Principles and Practice of Constraint Programming – CP 2003. CP 2003. Lecture Notes in Computer Science, vol 2833. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45193-8_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-45193-8_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20202-8

  • Online ISBN: 978-3-540-45193-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics