TY - GEN
T1 - Multi-attribute regret-based dynamic pricing
AU - Jumadinova, Janyl
AU - Dasgupta, Prithviraj
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Dynamic pricing
KW - Minimax regret
KW - Pricebots
UR - http://www.scopus.com/inward/record.url?scp=77956108588&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77956108588&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15237-5_6
DO - 10.1007/978-3-642-15237-5_6
M3 - Conference contribution
AN - SCOPUS:77956108588
SN - 3642152368
SN - 9783642152368
T3 - Lecture Notes in Business Information Processing
SP - 73
EP - 87
BT - Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis - AAMAS Workshop, AMEC 2008, and AAAI Workshop, TADA 2008, Revised Selected Papers
PB - Springer Verlag
T2 - 10th Workshop on Agent-Mediated Electronic Commerce, AMEC-X, Co-located with AAMAS 2008 and the 6th Workshop on Trading Agent Design and Analysis, TADA, Co-located with AAAI 2008
Y2 - 14 July 2008 through 14 July 2008
ER -