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Conference Paper: Algorithmic Price Discrimination
Title | Algorithmic Price Discrimination |
---|---|
Authors | |
Issue Date | 2020 |
Publisher | Society for Industrial and Applied Mathematics (SIAM) |
Citation | 31st Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2020), Salt Lake City, UT, USA, 5-8 January 2020. In Chawla, S (eds.), Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, p. 2432-2451 How to Cite? |
Abstract | We consider a generalization of the third degree price discrimination problem studied in [4](Bergemann et al., 2015), where an intermediary between the buyer and the seller can design market segments to maximize any linear combination of consumer surplus and seller revenue. Unlike in [4], we assume that the intermediary only has partial information about the buyer's value. We consider three different models of information, with increasing order of difficulty. In the first model, we assume that the intermediary's information allows him to construct a probability distribution of the buyer's value. Next we consider the sample complexity model, where we assume that the intermediary only sees samples from this distribution. Finally, we consider a bandit online learning model, where the intermediary can only observe past purchasing decisions of the buyer, rather than her exact value. For each of these models, we present algorithms to compute optimal or near optimal market segmentation. |
Persistent Identifier | http://hdl.handle.net/10722/284139 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Cummings, R | - |
dc.contributor.author | Devanur, N | - |
dc.contributor.author | Huang, Z | - |
dc.contributor.author | WANG, X | - |
dc.date.accessioned | 2020-07-20T05:56:24Z | - |
dc.date.available | 2020-07-20T05:56:24Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | 31st Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2020), Salt Lake City, UT, USA, 5-8 January 2020. In Chawla, S (eds.), Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, p. 2432-2451 | - |
dc.identifier.isbn | 9781611975994 | - |
dc.identifier.uri | http://hdl.handle.net/10722/284139 | - |
dc.description.abstract | We consider a generalization of the third degree price discrimination problem studied in [4](Bergemann et al., 2015), where an intermediary between the buyer and the seller can design market segments to maximize any linear combination of consumer surplus and seller revenue. Unlike in [4], we assume that the intermediary only has partial information about the buyer's value. We consider three different models of information, with increasing order of difficulty. In the first model, we assume that the intermediary's information allows him to construct a probability distribution of the buyer's value. Next we consider the sample complexity model, where we assume that the intermediary only sees samples from this distribution. Finally, we consider a bandit online learning model, where the intermediary can only observe past purchasing decisions of the buyer, rather than her exact value. For each of these models, we present algorithms to compute optimal or near optimal market segmentation. | - |
dc.language | eng | - |
dc.publisher | Society for Industrial and Applied Mathematics (SIAM) | - |
dc.relation.ispartof | 31st Annual ACM-SIAM Symposium on Discrete Algorithms (SODA) | - |
dc.title | Algorithmic Price Discrimination | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Huang, Z: zhiyi@cs.hku.hk | - |
dc.identifier.authority | Huang, Z=rp01804 | - |
dc.identifier.doi | 10.1137/1.9781611975994.149 | - |
dc.identifier.hkuros | 310909 | - |
dc.identifier.spage | 2432 | - |
dc.identifier.epage | 2451 | - |
dc.publisher.place | Philadelphia, Pennsylvania | - |