Hostname: page-component-848d4c4894-pftt2 Total loading time: 0 Render date: 2024-05-17T03:24:17.327Z Has data issue: false hasContentIssue false

Optimal stopping methodology for the secretary problem with random queries

Published online by Cambridge University Press:  02 October 2023

George V. Moustakides*
Affiliation:
University of Patras
Xujun Liu*
Affiliation:
Xi’an Jiaotong-Liverpool University
Olgica Milenkovic*
Affiliation:
University of Illinois, Urbana-Champaign
*
*Postal address: Department of Electrical and Computer Engineering, University of Patras, Rion, Greece. Email: moustaki@upatras.gr
**Postal address: Department of Foundational Mathematics, Xi’an Jiaotong-Liverpool University, Suzhou, China. Email: Xujun.Liu@xjtlu.edu.cn
***Postal address: Department of Electrical and Computer Engineering, University of Illinois, Urbana-Champaign, IL, USA. Email: milenkov@illinois.edu

Abstract

Candidates arrive sequentially for an interview process which results in them being ranked relative to their predecessors. Based on the ranks available at each time, a decision mechanism must be developed that selects or dismisses the current candidate in an effort to maximize the chance of selecting the best. This classical version of the ‘secretary problem’ has been studied in depth, mostly using combinatorial approaches, along with numerous other variants. We consider a particular new version where, during reviewing, it is possible to query an external expert to improve the probability of making the correct decision. Unlike existing formulations, we consider experts that are not necessarily infallible and may provide suggestions that can be faulty. For the solution of our problem we adopt a probabilistic methodology and view the querying times as consecutive stopping times which we optimize with the help of optimal stopping theory. For each querying time we must also design a mechanism to decide whether or not we should terminate the search at the querying time. This decision is straightforward under the usual assumption of infallible experts, but when experts are faulty it has a far more intricate structure.

Type
Original Article
Copyright
© The Author(s), 2023. Published by Cambridge University Press on behalf of Applied Probability Trust

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

This article was originally published with some funding information missing. The missing information has been added and the online PDF and HTML versions updated.

References

Antoniadis, A., Gouleakis, T., Kleer, P. and Kolev, P. (2020). Secretary and online matching problems with machine learned advice. In Proc. 34th Conf. Neural Inf. Proc. Syst., pp. 79337944.Google Scholar
Chien, I., Pan, C. and Milenkovic, O. (2018). Query K-means clustering and the double dixie cup problem. In Proc. 32nd Conf. Neural Inf. Proc. Syst., pp. 6649–6658.Google Scholar
Crews, M., Jones, B., Myers, K., Taalman, L., Urbanski, M. and Wilson, B. (2019). Opportunity costs in the game of best choice. Electron. J. Combinatorics 26, P1.45.Google Scholar
Dutting, P., Lattanzi, S., Leme, R. P. and Vassilvitskii, S. (2021). Secretaries with advice. In Proc. 22nd ACM Conf. Econom. Comp., pp. 409429.10.1145/3465456.3467623CrossRefGoogle Scholar
Dynkin, E. B. (1963). The optimal choice of the stopping moment for a Markov process. Dokl. Akad. Nauk. SSSR 150, 238240.Google Scholar
Ferguson, T. S. (1989). Who solved the secretary problem? Statist. Sci. 4, 282289.Google Scholar
Freeman, P. R. (1983). The secretary problem and its extensions – a review. Int. Statist. Rev. 51, 189206.CrossRefGoogle Scholar
Gardner, M. (1960). Mathematical games. Scientific American 202, 178179.CrossRefGoogle Scholar
Gilbert, J. and Mosteller, F. (1966). Recognizing the maximum of a sequence. J. Amer. Statist. Assoc. 61, 3573.CrossRefGoogle Scholar
Gusein-Zade, S. (1966). The problem of choice and the optimal stopping rule for a sequence of independent trials. Theory Prob. Appl. 11, 472476.CrossRefGoogle Scholar
Jones, B. (2020). Weighted games of best choice. SIAM J. Discrete Math. 34, 399414.CrossRefGoogle Scholar
Lindley, D. (1961). Dynamic programming and decision theory. Applied Statist. 10, 3952.CrossRefGoogle Scholar
Liu, X. and Milenkovic, O. (2022). Finding the second-best candidate under the Mallows model. Theoret. Comput. Sci. 929, 3968.CrossRefGoogle Scholar
Liu, X., Milenkovic, O. and Moustakides, G. V. (2021). Query-based selection of optimal candidates under the Mallows model. Preprint, arXiv:2101.07250.Google Scholar
Mazumdar, A. and Saha, B. (2017). Clustering with noisy queries. In Proc. 31st Conf. Neural Inf. Proc. Syst., pp. 57895800.Google Scholar
Nikolaev, M. (1977). On a generalization of the best choice problem. Theory Prob. Appl. 22, 187190.CrossRefGoogle Scholar
Peskir, P. and Shiryaev, A. (2006). Optimal Stopping and Free-Boundary Problems. Springer, New York.Google Scholar
Rose, J. S. (1982). A problem of optimal choice and assignment. Operat. Res. 30, 172181.CrossRefGoogle Scholar
Sakaguchi, M. (1978). Dowry problems and OLA policies. Rep. Stat. Appl. Res. JUSE 25, 124128.Google Scholar
Shiryaev, A. (1978). Optimal Stopping Rules. Springer, New York.Google Scholar
Tamaki, M. (1979). A secretary problem with double choices. J. Operat. Res. Soc. Japan 22, 257264.Google Scholar
Tamaki, M. (1979). Recognizing both the maximum and the second maximum of a sequence. J. Appl. Prob. 16, 803812.10.2307/3213146CrossRefGoogle Scholar