An application of EDA and GA to dynamic pricing.

E-commerce has transformed the way firms develop their pricing strategies, producing shift away from fixed pricing to dynamic pricing. In this paper, we use two different Estimation of distribution algorithms (EDAs), a Genetic Algorithm (GA) and a Simulated Annealing (SA) algorithm for solving two different dynamic pricing models. Promising results were obtained for an EDA confirming its suitability for resource management in the proposed model. Our analysis gives interesting insights into the application of population based optimization techniques for dynamic pricing

Adaptive Strategies for Dynamic Pricing Agents

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 adaptive dynamic pricing strategies and optimize their parameters with an Evolutionary Algorithm (EA) offline while the strategies can deal with stochastic market dynamics quickly online. We design two adaptive heuristic dynamic pricing strategies 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 for each of the pricing strategies in each of the settings and compare the strategies with other strategies from the literature. We also perform sensitivity a analysis and show that the optimized strategies work well even when used in settings with varied demand functions

Adaptive Strategies for Dynamic Pricing Agents

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 adaptive dynamic pricing strategies and optimize their parameters with an Evolutionary Algorithm (EA) offline while the strategies can deal with stochastic market dynamics quickly online. We design two adaptive heuristic dynamic pricing strategies 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 for each of the pricing strategies in each of the settings and compare the strategies with other strategies from the literature. We also perform sensitivity analysis and show that the optimized strategies work well even when used in settings with varied demand functions

Técnicas de aprendizaje estadístico en modelos de valoración dinámica de precios

En el presente trabajo se estudian diversas técnicas de aprendizaje por refuerzo con la idea de solucionar varios problemas de fijación y planificación de precios. Para ello, se lleva a cabo una introducción al aprendizaje por refuerzo, estudiando los problemas de decisión de Markov en el caso de un único agente. En estos juegan un papel fundamental las ecuaciones de optimalidad de Bellman, que permiten el desarrollo de métodos para la resolución del problema. En el trabajo se verán los algoritmos Q-Learning, SARSA y dos de sus variantes. En el caso de varios agentes se estudian los juegos matriciales y su generalización a varios estados, los juegos estocásticos. En estos problemas, y bajo el soporte de la teoría de juegos, aparecen los equilibrios de Nash. Veremos dos de las principales técnicas para resolver estos problemas, los métodos de mejor respuesta y los métodos de los equilibrios. Finalmente se analizarán los resultados obtenidos tras aplicar los algoritmos estudiados a problemas de fijación de precios.<br /

ABSTRACT An Application of EDA and GA to Dynamic Pricing

E-commerce has transformed the way firms develop their pricing strategies, producing shift away from fixed pricing to dynamic pricing. In this paper, we use two different Estimation of distribution algorithms (EDAs), a Genetic Algorithm (GA) and a Simulated Annealing (SA) algorithm for solving two different dynamic pricing models. Promising results were obtained for an EDA confirming its suitability for resource management in the proposed model. Our analysis gives interesting insights into the application of population based optimization techniques for dynamic pricing. Categories and Subject Descriptor