We study price dynamics in an electronic service market environment consisting of buyers and competing service providers. In this markeT, each service provider has limited capacity to serve the buyers. We present price dynamics in a two-seller market when buyers use comparison shopping agents to know about price and expected delay at each service provider. Each seller uses an automated pricing agent to reset the price at random intervals in order to maximize his expected profits. A Q-learning algorithm for pricing agent is developed and comparative experimental study on various other adaptive strategies is presented.
Further, we present a new multi-time scale actor-critic-type algorithm for multi-agent learning in the underlying stochastic games. Preliminary experimental results on convergence of the proposed algorithm in a degenerate version of the dynamic pricing game and also on convergence of the algorithm in iterated general-sum bi-matrix games are presented.
By: K Ravikumar, Gaurav Batra, Rohin Saluja
Published in: RI02006 in 2002
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