Evolutionary Dynamics of Four Pricebot Algorithms

Victoria Manfredi

July 31, 2001

Abstract:

This paper looks at different pricebot algorithms, compares how well they do against each other and determines which algorithm does the best. Pricebots are agents that determine the best price at which to sell an item [1]. In particular this paper compares the game-theoretic, myopically-optimal, derivative-following, and Q-learning algorithms [1,2,3]. We look at the effects of evolution on a population of pricebots using these various dynamic pricing algorithms in an evolutionary simulation; a sort of ``survival of the fittest.''