SSRN Author: Sheng QiangSheng Qiang SSRN Content
http://www.ssrn.com/author=2535221
http://www.ssrn.com/rss/en-usThu, 11 Aug 2016 01:39:25 GMTeditor@ssrn.com (Editor)Thu, 11 Aug 2016 01:39:25 GMTwebmaster@ssrn.com (WebMaster)SSRN RSS Generator 1.0REVISION: Dynamic Pricing with Demand CovariatesWe consider a firm that sells a product over T periods without knowing the demand function. The firm sequentially sets prices to earn revenue and to learn the underlying demand function simultaneously. In practice, this problem is commonly solved via greedy iterative least squares (GILS). At each time period, GILS estimates the demand as a linear function of the price by applying least squares to the set of prior prices and realized demands. Then a price that maximizes the revenue is used for the next period. The performance is measured by the regret, which is the expected revenue compared to an oracle that knows the true demand function. Recently, den Boer and Zwart (2014) and Keskin and Zeevi (2014) demonstrated that GILS is sub-optimal and introduced optimal algorithms which integrate forced price-dispersion with GILS. Here, we consider this dynamic pricing problem in a data-rich environment. We assume that the firm has access to demand covariates which may be predictive of the ...
http://www.ssrn.com/abstract=2765257
http://www.ssrn.com/1501141.htmlWed, 01 Jun 2016 19:17:45 GMTREVISION: Dynamic Pricing with Demand CovariatesWe consider a generic problem in which a firm sells products over T periods without knowing the demand function. The firm sequentially sets prices to earn revenue and to learn the underlying demand function simultaneously. A natural heuristic for this problem, commonly used in practice, is greedy iterative least squares (GILS). At each time period, GILS estimates the demand as a linear function of the price by applying least squares to the set of prior prices and realized demands. Then a price that maximizes the revenue, given the estimated demand function, is used for the next time period. The performance is measured by the regret, which is the expected revenue loss from the optimal (oracle) pricing policy when the demand function is known. Recently, den Boer and Zwart (2014) and Keskin and Zeevi (2014) demonstrated that GILS is sub-optimal. They introduced algorithms which integrate forced price dispersion with GILS and achieve asymptotically optimal performance.
In this paper, we ...
http://www.ssrn.com/abstract=2765257
http://www.ssrn.com/1491239.htmlWed, 27 Apr 2016 09:07:20 GMTREVISION: Dynamic Pricing with Demand CovariatesWe consider a generic problem in which a firm sells products over T periods without knowing the demand function. The firm sequentially sets prices to earn revenue and to learn the underlying demand function simultaneously. A natural heuristic for this problem, commonly used in practice, is greedy iterative least squares (GILS). At each time period, GILS estimates the demand as a linear function of the price by applying least squares to the set of prior prices and realized demands. Then a price that maximizes the revenue, given the estimated demand function, is used for the next time period. The performance is measured by the regret, which is the expected revenue loss from the optimal (oracle) pricing policy when the demand function is known. Recently, den Boer and Zwart (2014) and Keskin and Zeevi (2014) demonstrated that GILS is sub-optimal. They introduced algorithms which integrate forced price dispersion with GILS and achieve asymptotically near-optimal performance.
In this ...
http://www.ssrn.com/abstract=2765257
http://www.ssrn.com/1488695.htmlMon, 18 Apr 2016 15:59:46 GMT