{"179011":{"#nid":"179011","#data":{"type":"event","title":"Mike Harrison, Stanford University","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ESpeaker\u003C\/strong\u003E\u003Cbr \/\u003EMichael Harrison\u003Cbr \/\u003EAdams Distinguished Professor of Management\u0026nbsp;\u003Cbr \/\u003EStanford University\u003Cbr \/\u003E\u003Cbr \/\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003Cbr \/\u003EMotivated by applications in financial services, we consider the following customized pricing problem. A seller of some good or service (like auto loans or small business loans) confronts a sequence of potential customers numbered 1, 2, \u00e2\u20ac\u00a6 , T. These customers are drawn at random from a population characterized by a price-response function \u00cf\u0081(p). That is, if the seller offers price p, then the probability of a successful sale is \u00cf\u0081(p). The profit realized from a successful sale is \u00cf\u20ac(p) = p \u00e2\u02c6\u0027 c, where c \u0026gt; 0 is known.\u0026nbsp;\u003Cbr \/\u003E\u003Cbr \/\u003EIf the price-response function \u00cf\u0081(-) were also known, then the problem of finding a price p* to maximize \u00cf\u0081(p)\u00cf\u20ac(p) would be simple, and the seller would offer price p* to each of the T customers. We consider the more complicated case where \u00cf\u0081(-) is fixed but initially unknown: roughly speaking, the seller wants to choose prices sequentially so as to maximize the total profit earned from the T potential customers; each successive choice involves a trade-off between refined estimation of the unknown price-response function (learning) and immediate profit (earning).\u003Cbr \/\u003E\u003Cbr \/\u003E* Joint work with Bora Keskin and Assaf Zeevi\u003C\/p\u003E","summary":null,"format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EMotivated by applications in financial services, we consider the following customized pricing problem. A seller of some good or service (like auto loans or small business loans) confronts a sequence of potential customers numbered 1, 2, \u201d\u00a6 , T. These customers are drawn at random from a population characterized by a price-response function \u00cf\u0081(p). That is, if the seller offers price p, then the probability of a successful sale is \u00cf\u0081(p). The profit realized from a successful sale is \u00cf\u20ac(p) = p \u00e2\u02c6\u0027 c, where c \u0026gt; 0 is known.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Joint Statistics\/OR Colloquium Dynamic policies to learn and earn in a customized pricing context"}],"uid":"27215","created_gmt":"2012-12-20 16:04:47","changed_gmt":"2016-10-08 02:01:40","author":"Mike Alberghini","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2010-02-18T10:00:00-05:00","event_time_end":"2010-02-18T11:00:00-05:00","event_time_end_last":"2010-02-18T11:00:00-05:00","gmt_time_start":"2010-02-18 15:00:00","gmt_time_end":"2010-02-18 16:00:00","gmt_time_end_last":"2010-02-18 16:00:00","rrule":null,"timezone":"America\/New_York"},"extras":[],"groups":[{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1795","name":"Seminar\/Lecture\/Colloquium"}],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Cp\u003E\u003Cspan\u003ETon Dieker, ISyE\u003C\/span\u003E\u003Cbr \/\u003E\u003Ca href=\u0022http:\/\/www.gatech.edu\/contact\/?id=e5068\u0022\u003EContact Ton Dieker\u003C\/a\u003E\u003Cbr \/\u003E\u003Cspan\u003E404-385-3140\u003C\/span\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}