{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/139386"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/139386","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Essays on the Design of Online Marketplaces and Platforms","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_html":"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_has_math":false,"creators":["Holtz, David M."],"institution":"Massachusetts Institute of Technology","degree_name":"Doctoral","degree_level":null,"degree_discipline":null,"degree_department":"Sloan School of Management","school":null,"contributors":[],"advisors":["Aral, Sinan"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-06","date_published":"2021-06","updated_at":"2026-07-22T22:22:03Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"rights_urls":["http://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/139386","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Aral, Sinan"]},{"key":"dc:contributor.department","label":"Department","values":["Sloan School of Management"]},{"key":"dc:creator","label":"Author","values":["Holtz, David M."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2022-01-14T15:08:25Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2022-01-14T15:08:25Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-06"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctoral","Doctor of Philosophy"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright MIT"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/139386"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Essays on the Design of Online Marketplaces and Platforms"]}]}],"canonical_facts":{"dc:contributor.advisor":["Aral, Sinan"],"dc:contributor.department":["Sloan School of Management"],"dc:creator":["Holtz, David M."],"dc:date.accessioned":["2022-01-14T15:08:25Z"],"dc:date.available":["2022-01-14T15:08:25Z"],"dc:date.issued":["2021-06"],"dc:description.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. 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Results from the pricing algorithm meta-experiment highlight the difficulty of detecting interference bias when treatment interventions require intention-to-treat analysis."],"dc:description.degree":["Ph.D."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/139386"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"dc:rights.uri":["http://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["Essays on the Design of Online Marketplaces and Platforms"],"dc:type":["Thesis"],"thesis:degree_name":["Doctoral","Doctor of Philosophy"]},"updated_at":"2026-07-22T22:22:03Z"}