2026-09-21T22:51:12Zhttps://dspace.mit.edu/server/oai/request

oai:dspace.mit.edu:1721.1/1393862022-01-15T03:16:57Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022

Aral, Sinan Holtz, David M. Sloan School of Management 2022-01-14T15:08:25Z 2022-01-14T15:08:25Z 2021-06 2021-06-03T18:05:08.408Z https://hdl.handle.net/1721.1/139386 This dissertation consists of three chapters that concern the design of online marketplaces and platforms. In Chapter 1, I estimate the impact of increasing the extent to which content recommendations are personalized by analyzing the results of a randomized experiment on approximately 900,000 Spotify users across seventeen countries. I find that increasing recommendation personalization increased the number of podcasts that Spotify users streamed, but also decreased the individual-level diversity of Spotify users’ podcast consumption and increased the dissimilarity between the podcast consumption patterns of different users across the population. In Chapter 2, I propose methods for obtaining unbiased estimates of the total average treatment effect (TATE) when conducting experiments in online marketplaces, and test the viability of said methods using a simulation built on top of scraped data from Airbnb. I find that blocked graph cluster randomization can reduce the bias of TATE estimates in online marketplaces by as much as 64.5%, however, this reduction in bias comes with a substantial increase in root-mean-square error (RMSE). I also find that fractional neighborhood treatment response (FNTR) exposure models and inverse probability-weighted estimators have the potential to further reduce bias, depending on the choice of FNTR threshold. In Chapter 3, I conduct two large-scale meta-experiments on Airbnb in an attempt to estimate the actual magnitude of bias in TATE estimates from marketplace interference. In both meta-experiments, some Airbnb listings are assigned to experiment conditions at the individual-level, whereas others are assigned to experiment conditions at the level of clusters of listings that are likely to substitute for one another. The two meta-experiments measure the impact of two different pricing-related interventions on Airbnb: a change to Airbnb’s fee policy, and a change to the pricing algorithm that Airbnb uses to recommend prices to sellers. Results from the fee policy meta-experiment reveal that at least 32.60% of the treatment effect estimate in the Bernoulli-randomized meta-experiment arm is due to interference bias. Results from the pricing algorithm meta-experiment highlight the difficulty of detecting interference bias when treatment interventions require intention-to-treat analysis. Ph.D. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ Essays on the Design of Online Marketplaces and Platforms Thesis application/pdf Doctoral Doctor of Philosophy Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="b8ea9bcc-5b84-468d-a00e-f6ba17e0eafa"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Essays on the Design of Online Marketplaces and Platforms</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2021-06</PublicationDate> <Authors> <Author> <DisplayName>Holtz, David M.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>This dissertation consists of three chapters that concern the design of online marketplaces and platforms. In Chapter 1, I estimate the impact of increasing the extent to which content recommendations are personalized by analyzing the results of a randomized experiment on approximately 900,000 Spotify users across seventeen countries. I find that increasing recommendation personalization increased the number of podcasts that Spotify users streamed, but also decreased the individual-level diversity of Spotify users’ podcast consumption and increased the dissimilarity between the podcast consumption patterns of different users across the population. In Chapter 2, I propose methods for obtaining unbiased estimates of the total average treatment effect (TATE) when conducting experiments in online marketplaces, and test the viability of said methods using a simulation built on top of scraped data from Airbnb. I find that blocked graph cluster randomization can reduce the bias of TATE estimates in online marketplaces by as much as 64.5%, however, this reduction in bias comes with a substantial increase in root-mean-square error (RMSE). I also find that fractional neighborhood treatment response (FNTR) exposure models and inverse probability-weighted estimators have the potential to further reduce bias, depending on the choice of FNTR threshold. In Chapter 3, I conduct two large-scale meta-experiments on Airbnb in an attempt to estimate the actual magnitude of bias in TATE estimates from marketplace interference. In both meta-experiments, some Airbnb listings are assigned to experiment conditions at the individual-level, whereas others are assigned to experiment conditions at the level of clusters of listings that are likely to substitute for one another. The two meta-experiments measure the impact of two different pricing-related interventions on Airbnb: a change to Airbnb’s fee policy, and a change to the pricing algorithm that Airbnb uses to recommend prices to sellers. Results from the fee policy meta-experiment reveal that at least 32.60% of the treatment effect estimate in the Bernoulli-randomized meta-experiment arm is due to interference bias. Results from the pricing algorithm meta-experiment highlight the difficulty of detecting interference bias when treatment interventions require intention-to-treat analysis.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>