Dynamic service pricing for brokers in a multi-agent economy

Studies the price dynamics in a multi-agent economy consisting of buyers and competing sellers, where each seller has limited information about its competitors´ prices. In this economy, buyers use comparison shopping agents (shopbots) while the sellers employ automated pricing agents (pricebots). Derivative following (DF) provides a simple, albeit naive strategy for dynamic pricing in such a scenario. In this work, we refine the DF algorithm and introduce a model optimizer (MO) algorithm that re-estimates the price-profit relationship for a seller at every interval more efficiently. Simulations using the MO pricebots indicate that it outperforms DF even though it has no additional information about the market