Dynamic pricing by software agents
Computer Networks: The International Journal of Computer and Telecommunications Networking - electronic commerce
Dynamic pricing strategies under a finite time horizon
Proceedings of the 3rd ACM conference on Electronic Commerce
Dynamic Pricing with Limited Competitor Information in a Multi-Agent Economy
CooplS '02 Proceedings of the 7th International Conference on Cooperative Information Systems
A Partially Observed Markov Decision Process for Dynamic Pricing
Management Science
An application of EDA and GA to dynamic pricing
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Dynamic Pricing with a Prior on Market Response
Operations Research
Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Design of an incentive mechanism to promote honesty in e-marketplaces with limited inventory
Proceedings of the 14th Annual International Conference on Electronic Commerce
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Dynamic Pricing (DyP) is a form of Revenue Management in which the price of a (usually) perishable good is changed over time to increase revenue. It is an effective method that has become even more relevant and useful with the emergence of Internet firms and the possibility of readily and frequently updating prices. In this paper a new approach to DyP is presented. We design an adaptive dynamic pricing strategy and optimize its parameters with an Evolutionary Algorithm (EA) offline, while the strategy can deal with stochastic market dynamics quickly online. We design the adaptive heuristic dynamic pricing strategy in a duopoly where each firm has a finite inventory of a single type of good. We consider two cases, one in which the average of a customer population's stochastic valuation for each of the goods is constant throughout the selling horizon and one in which the average customer valuation for each good is changed according to a random Brownian motion. We also design an agent-based software framework for simulating various dynamic pricing strategies in agent-based marketplaces with multiple firms in a bounded time horizon. We use an EA to optimize the parameters of the pricing strategy in each of the settings and compare our strategy with other strategies from the literature. We also perform sensitivity analysis and show that the optimized strategy works well even when used in settings with varied demand functions.