On the minimax complexity of pricing in a changing environment

Operations Research

Volumn 59, Issue 1, 2011, Pages 66-79

  Besbes, Omar ^a   Zeevi, Assaf ^a  

^a Howard Hughes Medical Institute   (United States)

Author keywords

Change point; Detection; Estimation; Nonstationary demand; Price experimentation

Indexed keywords

CHANGE-POINTS; CHANGING ENVIRONMENT; DETECTION; LOWER BOUNDS; MINIMAX; NON-STATIONARY DEMAND; OPTIMAL DETECTION; PRICE EXPERIMENTATION; PRICING POLICY; PRICING PROBLEMS; PRICING STRATEGY; PURCHASE DECISION; RESERVATION PRICE; WILLINGNESS TO PAY;

CUSTOMER SATISFACTION; ECONOMICS; EXPERIMENTS; SALES;

COSTS;

EID: 79952957936     PISSN: 0030364X     EISSN: 15265463     Source Type: Journal    
DOI: 10.1287/opre.1100.0867     Document Type: Article

References (21)

1. Bayraktar, E., S. Dayanik, I. Karatzas. 2005. The standard Poisson disorder problem revisited. Stochastic Processes Appl. 115(9) 1437-1450. (Pubitemid 41064476)
2. Besbes, O., A. Zeevi. 2009. Dynamic pricing without knowing the demand function: Risk bounds and near-optimal algorithms. Oper. Res. 57(6) 1407-1420.
3. Besbes, O., R. Phillips, A. Zeevi. 2010. Testing the validity of a demand model: An operations perspective. Manufacturing Service Oper. Management 12(1) 162-183.
4. Borovkov, A. 1998. Mathematical Statistics. Gordon and Breach Science Publishers, Amsterdam.
5. Cesa-Bianchi, N., G. Lugosi. 2006. Prediction, Learning, and Games. Cambridge University Press, Cambridge, UK.
6. Gallego, G., G. van Ryzin. 1997. A multiproduct dynamic pricing problem and its applications to network yield management. Oper. Res. 45(1) 24-41. (Pubitemid 127684684)
7. Hannan, J. 1957. Approximation to Bayes risk in repeated play. Contributions to the Theory of Games, Vol. III. Princeton University Press, Princeton, NJ, 97-139.
8. Keller, G., S. Rady. 1999. Optimal experimentation in a changing environment. Rev. Econom. Stud. 66(3) 475-507.
9. Lai, T. L. 2001. Sequential analysis: Some classical problems and new challenges. Statistica Sinica 11(2) 303-408. (Pubitemid 33669754)
10. Levina, T., Y. Levin, J. McGill, M. Nediak. 2009. Dynamic pricing with online learning and strategic consumers. An application of the aggregating algorithm. Oper. Res. 57(2) 327-341.
11. Lobo, M. S. 2007. The value of dynamic pricing. Working paper, Duke University, Durham, NC.
12. Page, E. S. 1954. Continuous inspection schemes. Biometrika 41(1-2) 100-115.
13. Phillips, R. 2005. Pricing and Revenue Optimization. Stanford University Press, Palo Alto, CA.
14. Polyak, B. T., A. Tsybakov. 1990. Optimal order of accuracy of search algorithms in stochastic optimization. Problems Inform. Transmission 26(2) 126-133. (Pubitemid 21670810)
15. Roberts, S. W. 1966. A comparison of some control chart procedures. Technometrics 8(3) 411-430.
16. Shewhart, W. A. 1931. The Economic Control of the Quality of Manufactured Product. Van Nostrand, New York.
17. Shiryayev, A. N. 1963. On optimum methods in quickest detection problems. Theory Probab. Its Appl. 8(1) 22-46.
18. Shiryayev, A. N. 1978. Optimal Stopping Rules. Springer-Verlag, Berlin.
19. Siegmund, D. 1985. Sequential Analysis. Springer-Verlag, New York.
20. Talluri, K. 2009. A finite-population revenue management model and a risk-ratio procedure for the joint estimation of population size and parameters. Working paper, Universitat Pompeu Fabra, Barcelona, Spain.
21. Talluri, K. T., G. J. van Ryzin. 2005. Theoryand Practice of Revenue Management. Springer-Verlag, New York.