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Dynamic pricing and learning with competition : Insights from the Dynamic Pricing Challenge at the 2017 INFORMS RM & Pricing Conference

Dynamic pricing and learning with competition : Insights from the Dynamic Pricing Challenge at the 2017 INFORMS RM & Pricing Conference
Dynamic pricing and learning with competition : Insights from the Dynamic Pricing Challenge at the 2017 INFORMS RM & Pricing Conference
This paper presents the results of the Dynamic Pricing Challenge, held on the occasion of the 17th INFORMS Revenue Management and Pricing Section Conference on June 29-30, 2017, at the Centrum Wiskunde & Informatica, Amsterdam, The Netherlands. The participants of this pricing challenge submitted a wide variety of pricing and learning algorithms of which the numerical performance in a simulated market environment with competition was analyzed. This allows to consider market dynamics that are not analytically tractable or can not be empirically analyzed due to practical complications. Our findings implicate that the relative performance of algorithms varies substantially across oligopoly and duopoly markets and across different market dynamics, which confirms the inherit complexity of pricing and learning in the presence of competition. Most notably, none of the considered algorithms is able to consistently outperform the market. Furthermore, wefind that algorithms vary substantially in terms of robustness, that ignoring competition is increasingly harmful as competition is more fierce, and that too much exploration hurts performance significantly.
Dynamic pricing
1476-6930
1-19
van de Geer, Ruben
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den Boer, Arnoud V.
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Bayliss, Christopher
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Currie, Christine
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Ellina, Andria
7347cbed-1419-4877-8b56-490db931ca0b
Esders, Malte
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Haensel, Alwin
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Lei, Xiao
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Maclean, Kyle D.S.
8cd6f1cc-1a56-4175-9f73-30c5ea4b6a1a
Martinez Sykora, Antonio
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Nilsen Riseth, Asbjorn
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Odegaard, Fredrik
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Zachariades, Simos
1c5ec238-f82a-4819-ba2a-9e4ac2ae5b5b
van de Geer, Ruben
b177e9c0-e110-4bc3-8fb1-ccb28f7742ec
den Boer, Arnoud V.
1b6bd077-f285-42db-afe9-5dcfac5a0a85
Bayliss, Christopher
5fb04968-5cbf-40d8-84b0-02e8c7e94a59
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Ellina, Andria
7347cbed-1419-4877-8b56-490db931ca0b
Esders, Malte
4d348239-d83e-43d1-9c6c-6427f69229f4
Haensel, Alwin
b2549abf-d2fc-4e0f-aefc-ae1d449dd98e
Lei, Xiao
73e9ca95-d42e-4af2-8a88-f8c567034338
Maclean, Kyle D.S.
8cd6f1cc-1a56-4175-9f73-30c5ea4b6a1a
Martinez Sykora, Antonio
2f9989e1-7860-4163-996c-b1e6f21d5bed
Nilsen Riseth, Asbjorn
13fc08a0-7d69-49f8-845b-6a2dd5862e15
Odegaard, Fredrik
e63188b9-b165-46c0-a9fb-ebe5e6b300d8
Zachariades, Simos
1c5ec238-f82a-4819-ba2a-9e4ac2ae5b5b

van de Geer, Ruben, den Boer, Arnoud V., Bayliss, Christopher, Currie, Christine, Ellina, Andria, Esders, Malte, Haensel, Alwin, Lei, Xiao, Maclean, Kyle D.S., Martinez Sykora, Antonio, Nilsen Riseth, Asbjorn, Odegaard, Fredrik and Zachariades, Simos (2018) Dynamic pricing and learning with competition : Insights from the Dynamic Pricing Challenge at the 2017 INFORMS RM & Pricing Conference. Journal of Revenue and Pricing Management, 1-19. (doi:10.1057/s41272-018-00164-4).

Record type: Article

Abstract

This paper presents the results of the Dynamic Pricing Challenge, held on the occasion of the 17th INFORMS Revenue Management and Pricing Section Conference on June 29-30, 2017, at the Centrum Wiskunde & Informatica, Amsterdam, The Netherlands. The participants of this pricing challenge submitted a wide variety of pricing and learning algorithms of which the numerical performance in a simulated market environment with competition was analyzed. This allows to consider market dynamics that are not analytically tractable or can not be empirically analyzed due to practical complications. Our findings implicate that the relative performance of algorithms varies substantially across oligopoly and duopoly markets and across different market dynamics, which confirms the inherit complexity of pricing and learning in the presence of competition. Most notably, none of the considered algorithms is able to consistently outperform the market. Furthermore, wefind that algorithms vary substantially in terms of robustness, that ignoring competition is increasingly harmful as competition is more fierce, and that too much exploration hurts performance significantly.

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Accepted/In Press date: 12 August 2018
e-pub ahead of print date: 16 October 2018
Keywords: Dynamic pricing

Identifiers

Local EPrints ID: 424562
URI: https://eprints.soton.ac.uk/id/eprint/424562
ISSN: 1476-6930
PURE UUID: deb6ee92-ba0c-40f8-86fb-9137b4c86090
ORCID for Christine Currie: ORCID iD orcid.org/0000-0002-7016-3652
ORCID for Antonio Martinez Sykora: ORCID iD orcid.org/0000-0002-2435-3113

Catalogue record

Date deposited: 05 Oct 2018 11:38
Last modified: 20 Jul 2019 01:02

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