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.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015
Place of PublicationLos Alamitos, CA
Number of pages7
ISBN (Print)978-1-4799-7560-0
Publication statusPublished - Dec 2015
Event2015 IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2015 - Cape Town, South Africa
Duration: 7 Dec 201510 Dec 2015


Conference2015 IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2015
Abbreviated titleIEEE SSCI
CountrySouth Africa
CityCape Town

    Research areas

  • Games, Electronic mail, Stochastic processes, Mathematical model, Analytical models, Computational intelligence, Smart grids

ID: 11310897