When sufficiently widespread adoption of shopbots by buyers forces sellers to become more competitive, it seems likely that sellers will respond by creating pricebots that automatically set prices so as to maximize profitability. It is unrealistic, however, to expect that pricebots will simply compute the mixed strategy Nash equilibrium and distribute their prices accordingly. The real business world is fraught with uncertainties that undermine the validity of traditional game-theoretic analyses: sellers lack perfect knowledge of buyer demands, and have an incomplete understanding of competitors' strategies. In order to be profitable, pricebots will need to continually adapt to changing market conditions.
In this section, we discuss simulations of two adaptive pricing strategies, and we compare the resulting price and profit dynamics with the game-theoretic equilibrium. Recently, empirical studies of sophisticated learning algorithms have revealed that learning tends to converge to pure strategy Nash equilibria in games for which such equilibria exist [Greenwald et al. 1998]. As there does not exist a pure strategy Nash equilibrium in the shopbot model, it is of particular interest to study the outcome of adaptive pricing schemes.