[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124091-en":3,"doc-seo-124091-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124091,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Essays in Econometrics and Machine Learning","This dissertation enhances transparency in AI systems by integrating machine learning and econometrics, with a focus on Dynamic Discrete Choice (DDC) models. It contrasts the Econometrics Nested Fixed Point (NFXP) algorithm with Max-Margin Inverse Reinforcement Learning (IRL) using Monte Carlo experiments, showing how preference shocks address identification issues in IRL while reducing computational burden. It further applies the approaches to 2013 New York City taxi data and evaluates IRL versus NFXP for payoff estimation. Finally, it studies limited attention in marijuana dispensary adoption.","Essays in Econometrics and Machine Learning  \nby  \nColleen O’Briant  \nA dissertation accepted and approved in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Economics  \nDissertation Committee:  \nKeaton Miller, Chair  \nMark Colas, Core Member David Evans, Core Member Brittany Erickson, Institutional Representative  \nUniversity of Oregon Spring 2024  \n© 2024 Colleen O’Briant All rights reserved.  \nThis work is openly licensed via CC BY 4.0.  \n2  \nDISSERTATION ABSTRACT  \nColleen O’Briant  \nDoctor of Philosophy in Economics  \nTitle: Essays in Econometrics and Machine Learning  \nThis dissertation aims to enhance transparency in AI systems by integrating methods from Machine Learning and Econometrics, specifically focusing on Dynamic Discrete Choice (DDC) models. In Chapter 2, I compare the Nested Fixed Point (NFXP) algorithm from Econometrics with Max-Margin Inverse Reinforcement Learning (IRL) methods from AI/ML, using Monte-Carlo experiments to demonstrate that preference shocks from Econometrics can resolve fundamental identification issues in IRL. The simulation results show that while Projection IRL is slightly less accurate than NFXP, IRL significantly reduces computational demands, requiring 20 times fewer dynamic programming problems to be solved. Chapter 3 investigates the practical applications of these methods by analyzing publicly available 2013 taxi data to compare IRL and NFXP in estimating payoffs for New York City taxi drivers during the morning commute. The analysis highlights that IRL’s flatter objective function has the problem of allowing a broader range of acceptable payoff functions, however its feature expectation matching technique provides valuable feedback on the smoothing parameter for kernel density estimation of the transition probability function. This chapter offers recommendations and identifies potential drawbacks of using IRL, thereby deepening our understanding of the real-world performance of the algorithm.  \nIn Chapter 4, the dissertation explores how small business owners may misattribute noise for profit signals using an instrumental variables approach and a rich dataset of product ordering decisions by Washington State marijuana dispensaries over the first three years of recreational marijuana legalization. The study examines whether entrepreneurs’ predictions about product profitability are influenced by exogenous weather shocks, assessing if owners with previous retail experience make more informed decisions, if attentiveness improves over time, and if living further from the dispensary increases the likelihood of conflating weather shocks with profitability signals.  \nContents  \n1 INTRODUCTION 8  \n2 THE ECONOMETRICS OF INVERSE REINFORCEMENT LEARNING 10  \n2.1 Introduction .................................. 10  \n2.2 Background on Methods ........................... 11  \n2.3 Model ..................................... 14  \n2.3.1 Nested Fixed Point Implementation Details ............ 14  \n2.3.2 Linear Programming IRL Implementation Details ......... 16  \n2.3.3 Projection IRL Implementation Details ............... 18  \n2.4 Methods .................................... 19  \n2.5 Results ..................................... 20  \n2.6 Discussion ................................... 21  \n2.6.1 Estimates Under Preference Shocks ................. 21  \n2.6.2 Identification Problem from Wind Shocks .............. 21  \n2.7 Conclusion ................................... 23  \n2.8 Tables and Figures .............................. 24  \n3 COMPARING INVERSE REINFORCEMENT LEARNING TO RUST’S ESTIMATOR 37  \n3.1 Introduction .................................. 37  \n3.1.1 Related Work ............................. 38  \n3.2 Model ..................................... 39  \n3.3 Data ...................................... 40  \n3.4 Methods .................................... 41  \n3.4.1 Estimating the Transition Probability Function .......... 41  \n3.4.2 Nested Fixed Point Algorithm .....","cbCaij61m2QS3vTM","https://ap.wps.com/l/cbCaij61m2QS3vTM","pdf",12271972,1,86,"English","en",105,"# Dissertation Abstract\n# Introduction\n## The Econometrics of Inverse Reinforcement Learning\n## Comparing Inverse Reinforcement Learning to Rust’s Estimator\n## Limited Attention and New Product Adoption for Marijuana Dispensaries\n# References Cited","[{\"question\":\"What is the dissertation’s main goal for improving AI transparency?\",\"answer\":\"It aims to enhance transparency in AI systems by integrating methods from machine learning and econometrics, centered on Dynamic Discrete Choice (DDC) models.\"},{\"question\":\"How does the work compare NFXP and inverse reinforcement learning methods?\",\"answer\":\"It compares the Econometrics NFXP algorithm with max-margin IRL methods via Monte Carlo experiments, showing that preference shocks can resolve key identification issues and that IRL can reduce computational demands.\"},{\"question\":\"What real-world datasets and applications are analyzed in the dissertation?\",\"answer\":\"The dissertation analyzes public 2013 NYC taxi data to compare IRL and NFXP for payoff estimation, and it uses product ordering and weather-related data from Washington State marijuana dispensaries to study how noise may be misattributed as profit signals.\"}]","Essays in Econometrics and Machine Learning | PDF",1785820278,217,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"essays-in-econometrics-and-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/essays-in-econometrics-and-machine-learning/124091/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the dissertation’s main goal for improving AI transparency?","Question",{"text":75,"@type":76},"It aims to enhance transparency in AI systems by integrating methods from machine learning and econometrics, centered on Dynamic Discrete Choice (DDC) models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the work compare NFXP and inverse reinforcement learning methods?",{"text":80,"@type":76},"It compares the Econometrics NFXP algorithm with max-margin IRL methods via Monte Carlo experiments, showing that preference shocks can resolve key identification issues and that IRL can reduce computational demands.",{"name":82,"@type":73,"acceptedAnswer":83},"What real-world datasets and applications are analyzed in the dissertation?",{"text":84,"@type":76},"The dissertation analyzes public 2013 NYC taxi data to compare IRL and NFXP for payoff estimation, and it uses product ordering and weather-related data from Washington State marijuana dispensaries to study how noise may be misattributed as profit signals.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]