Jeffrey O. Kephart and Amy R. Greenwald
Past research has been concemed with the potential of embedding deterministic pricing algorithms into pricebots" software agents used by on-line sellers to automatically price Intemet goods. In this work, probabilistic pricing algorithms based on no-regret learning are explored, in both high-information and low-information settings. It is shown via simulations that the long-run empirical frequencies of prices in a market of no-regret pricebots can converge to equilibria arbitrarily close to an asymmetric Nash equilibrium; symmetric Nash equilibria are typically unstable. Instantaneous price distributions, however, need not converge.