Standard

Winning in Retail Market Games : Relative Profit and Logit Demand. / Hoogland, Jasper; De Weerdt, Mathijs M.; Poutre, Han La.

Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015. Los Alamitos, CA : IEEE, 2015. p. 1794-1800 7376827.

Research output: Scientific - peer-reviewConference contribution

Harvard

Hoogland, J, De Weerdt, MM & Poutre, HL 2015, Winning in Retail Market Games: Relative Profit and Logit Demand. in Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015., 7376827, IEEE, Los Alamitos, CA, pp. 1794-1800, 2015 IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2015, Cape Town, South Africa, 7/12/15. DOI: 10.1109/SSCI.2015.250

APA

Hoogland, J., De Weerdt, M. M., & Poutre, H. L. (2015). Winning in Retail Market Games: Relative Profit and Logit Demand. In Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015 (pp. 1794-1800). [7376827] Los Alamitos, CA: IEEE. DOI: 10.1109/SSCI.2015.250

Vancouver

Hoogland J, De Weerdt MM, Poutre HL. Winning in Retail Market Games: Relative Profit and Logit Demand. In Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015. Los Alamitos, CA: IEEE. 2015. p. 1794-1800. 7376827. Available from, DOI: 10.1109/SSCI.2015.250

Author

Hoogland, Jasper ; De Weerdt, Mathijs M. ; Poutre, Han La. / Winning in Retail Market Games : Relative Profit and Logit Demand. Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015. Los Alamitos, CA : IEEE, 2015. pp. 1794-1800

BibTeX

@inbook{a1be5cdbbb494af9912d917093285374,
title = "Winning in Retail Market Games: Relative Profit and Logit Demand",
abstract = "We examine retailers that maximize their relative profit, which is the (absolute) profit relative to the average profit of the other retailers. Customer behavior is modelled by a multinomial logit (MNL) demand model. Although retailers with low retail prices attract more customers than retailers high retail prices, the retailer with the lowest retail price, according to this model, does not attract all the customers. We provide first and second order derivatives, and show that the relative profit, as a function of the own price, has a unique local maximum. Our experiments show that relative profit maximizers {"}beat{"} absolute profit maximizers, i.e. They outperform absolute profit maximizers if the goal is to make a higher profit. These results provide insight into market simulation competitions, such as the Power TAC.",
keywords = "Games, Electronic mail, Stochastic processes, Mathematical model, Analytical models, Computational intelligence, Smart grids",
author = "Jasper Hoogland and {De Weerdt}, {Mathijs M.} and Poutre, {Han La}",
year = "2015",
month = "12",
doi = "10.1109/SSCI.2015.250",
isbn = "978-1-4799-7560-0",
pages = "1794--1800",
booktitle = "Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015",
publisher = "IEEE",
address = "United States",

}

RIS

TY - CHAP

T1 - Winning in Retail Market Games

T2 - Relative Profit and Logit Demand

AU - Hoogland,Jasper

AU - De Weerdt,Mathijs M.

AU - Poutre,Han La

PY - 2015/12

Y1 - 2015/12

N2 - We examine retailers that maximize their relative profit, which is the (absolute) profit relative to the average profit of the other retailers. Customer behavior is modelled by a multinomial logit (MNL) demand model. Although retailers with low retail prices attract more customers than retailers high retail prices, the retailer with the lowest retail price, according to this model, does not attract all the customers. We provide first and second order derivatives, and show that the relative profit, as a function of the own price, has a unique local maximum. Our experiments show that relative profit maximizers "beat" absolute profit maximizers, i.e. They outperform absolute profit maximizers if the goal is to make a higher profit. These results provide insight into market simulation competitions, such as the Power TAC.

AB - We examine retailers that maximize their relative profit, which is the (absolute) profit relative to the average profit of the other retailers. Customer behavior is modelled by a multinomial logit (MNL) demand model. Although retailers with low retail prices attract more customers than retailers high retail prices, the retailer with the lowest retail price, according to this model, does not attract all the customers. We provide first and second order derivatives, and show that the relative profit, as a function of the own price, has a unique local maximum. Our experiments show that relative profit maximizers "beat" absolute profit maximizers, i.e. They outperform absolute profit maximizers if the goal is to make a higher profit. These results provide insight into market simulation competitions, such as the Power TAC.

KW - Games

KW - Electronic mail

KW - Stochastic processes

KW - Mathematical model

KW - Analytical models

KW - Computational intelligence

KW - Smart grids

UR - http://www.scopus.com/inward/record.url?scp=84964944695&partnerID=8YFLogxK

UR - http://resolver.tudelft.nl/uuid://a1be5cdb-bb49-4af9-912d-917093285374

U2 - 10.1109/SSCI.2015.250

DO - 10.1109/SSCI.2015.250

M3 - Conference contribution

SN - 978-1-4799-7560-0

SP - 1794

EP - 1800

BT - Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015

PB - IEEE

ER -

ID: 11310897