Dynamic Pricing: A learning Approach
We present an optimization approach for jointly learning the demand as a functionof price, and dynamically setting prices of products in an oligopoly environment in order to maximize expected revenue. The models we consider do not assume that the demand as a function of price is known in advance, bu...
| Main Authors: | , |
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| Format: | Working Paper |
| Language: | en_US |
| Published: |
Massachusetts Institute of Technology, Operations Research Center
2004
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| Online Access: | http://hdl.handle.net/1721.1/5314 |
| _version_ | 1826198710336356352 |
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| author | Bertsimas, Dimitris J. Perakis, Georgia |
| author_facet | Bertsimas, Dimitris J. Perakis, Georgia |
| author_sort | Bertsimas, Dimitris J. |
| collection | MIT |
| description | We present an optimization approach for jointly learning the demand as a functionof price, and dynamically setting prices of products in an oligopoly environment in order to maximize expected revenue. The models we consider do not assume that the demand as a function of price is known in advance, but rather assume parametric families of demand functions that are learned over time. We first consider the noncompetitive case and present dynamic programming algorithms of increasing computational intensity with incomplete state information for jointly estimating the demand and setting prices as time evolves. Our computational results suggest that dynamic programming based methods outperform myopic policies often significantly. We then extend our analysis in a competitive environment with two firms. We introduce a more sophisticated model of demand learning, in which the price elasticities are slowly varying functions of time, and allows for increased flexibility in the modeling of the demand. We propose methods based on optimization for jointly estimating the Firm's own demand, its competitor's demand, and setting prices. In preliminary computational work, we found that optimization based pricing methods offer increased expected revenue for a firm independently of the policy the competitor firm is following. |
| first_indexed | 2024-09-23T11:08:56Z |
| format | Working Paper |
| id | mit-1721.1/5314 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:08:56Z |
| publishDate | 2004 |
| publisher | Massachusetts Institute of Technology, Operations Research Center |
| record_format | dspace |
| spelling | mit-1721.1/53142019-04-12T08:07:38Z Dynamic Pricing: A learning Approach Bertsimas, Dimitris J. Perakis, Georgia We present an optimization approach for jointly learning the demand as a functionof price, and dynamically setting prices of products in an oligopoly environment in order to maximize expected revenue. The models we consider do not assume that the demand as a function of price is known in advance, but rather assume parametric families of demand functions that are learned over time. We first consider the noncompetitive case and present dynamic programming algorithms of increasing computational intensity with incomplete state information for jointly estimating the demand and setting prices as time evolves. Our computational results suggest that dynamic programming based methods outperform myopic policies often significantly. We then extend our analysis in a competitive environment with two firms. We introduce a more sophisticated model of demand learning, in which the price elasticities are slowly varying functions of time, and allows for increased flexibility in the modeling of the demand. We propose methods based on optimization for jointly estimating the Firm's own demand, its competitor's demand, and setting prices. In preliminary computational work, we found that optimization based pricing methods offer increased expected revenue for a firm independently of the policy the competitor firm is following. 2004-05-28T19:33:13Z 2004-05-28T19:33:13Z 2001-08 Working Paper http://hdl.handle.net/1721.1/5314 en_US Operations Research Center Working Paper;OR 355-01 1517379 bytes application/pdf application/pdf Massachusetts Institute of Technology, Operations Research Center |
| spellingShingle | Bertsimas, Dimitris J. Perakis, Georgia Dynamic Pricing: A learning Approach |
| title | Dynamic Pricing: A learning Approach |
| title_full | Dynamic Pricing: A learning Approach |
| title_fullStr | Dynamic Pricing: A learning Approach |
| title_full_unstemmed | Dynamic Pricing: A learning Approach |
| title_short | Dynamic Pricing: A learning Approach |
| title_sort | dynamic pricing a learning approach |
| url | http://hdl.handle.net/1721.1/5314 |
| work_keys_str_mv | AT bertsimasdimitrisj dynamicpricingalearningapproach AT perakisgeorgia dynamicpricingalearningapproach |