Probabilistic pricebots

Past research has been concerned with the potential of embedding deterministic pricing algorithms into {\em pricebots}: software agents used by on-line sellers to automatically price Internet goods. In this work, probabilistic pricing algorithms based on {\em 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.

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