Probabilistic Pricebots
Amy R. Greenwald and Jeffrey O. Kephart
Abstract
Past research has been concerned with the potential of embedding
deterministic pricing algorithms into pricebots: software agents
used by on-line sellers to automatically price Internet 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;
however, instantaneous price distributions need not converge.