Game-theoretic analysis of a model of a simple commodity market established a quantitative relationship between the degree of shopbot usage among buyers and the degree of price competition among sellers. This motivated a comparative study of various pricebot algorithms that sellers might employ in an effort to gain an edge in a market in which the presence of shopbots has increased the degree of competition.
MY pricebots were shown to be capable of inducing price wars, yet even so they earn profits that are well above those of GT strategists. DF pricebots were observed to exhibit tacit collusion, leading to cartel-level profits. Finally, game-theoretic equilibria arose dynamically as the outcome of repeated play among certain NR pricebots. In related work (see [20]), we explore the dynamics of prices and profits among pricebots that use non-myopic learning algorithms, such as Q-learning, and we directly compare the profitability of various pricing strategies by simulating heterogeneous collections of pricebots. In future work, we intend to study the dynamics of markets in which more sophisticated shopbots base their search on product attributes as well as price, and in which pricebot strategies are extended accordingly.