Multi-attribute Regret-based Dynamic Pricing

In this paper, we consider the problem of dynamic pricing by a set of competing sellers in an information economy where buyers differentiate products along multiple attributes, and buyer preferences can change temporally. Previous research in this area has either focused on dynamic pricing along a limited number of (e.g. binary) attributes, or, assumes that each seller has access to private information such as preference distribution of buyers, and profit/price information of other sellers. However, in real information markets, private information about buyers and sellers cannot be assumed to be available a priori. Moreover, due to the competition between sellers, each seller faces a tradeoff between accuracy and rapidity of the pricing mechanism. In this paper, we describe a multi-attribute dynamic pricing algorithm based on minimax regret that can be used by a seller’s agent called a pricebot, to maximize the seller’s utility. Our simulation results show that the minimax regret based dynamic pricing algorithm performs significantly better than other algorithms for rapidly and dynamically tracking consumer attributes without using any private information from either buyers or sellers.