{"id":165873,"date":"2013-01-01T00:00:00","date_gmt":"2013-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/cost-function-market-makers-for-measurable-spaces\/"},"modified":"2019-06-21T05:56:08","modified_gmt":"2019-06-21T12:56:08","slug":"cost-function-market-makers-for-measurable-spaces","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/cost-function-market-makers-for-measurable-spaces\/","title":{"rendered":"Cost Function Market Makers for Measurable Spaces"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We characterize cost function market makers designed to elicit traders&#8217; beliefs about the expectations of an infinite set of random variables or the full distribution of a continuous random variable. This characterization is derived from a duality perspective that associates the market maker&#8217;s liabilities with market beliefs, generalizing the framework of Abernethy et al. [2011, 2013], but relies on a new subdifferential analysis. It differs from prior approaches in that it allows arbitrary market beliefs, not just those that admit density functions. This allows us to overcome the impossibility results of Gao and Chen [2010] and design the first automated market maker for betting on the realization of a continuous random variable taking values in [0,1] that has bounded loss without resorting to discretization. Additionally, we show that scoring rules are derived from the same duality and share a close connection with cost functions for eliciting beliefs.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We characterize cost function market makers designed to elicit traders&#8217; beliefs about the expectations of an infinite set of random variables or the full distribution of a continuous random variable. This characterization is derived from a duality perspective that associates the market maker&#8217;s liabilities with market beliefs, generalizing the framework of Abernethy et al. [2011, [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Fourteenth ACM Conference on Electronic Commerce (EC)","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Yiling Chen, Michael Ruberry, Jennifer Wortman Vaughan","msr_other_contributors":"","msr_speaker":"","msr_award":"","msr_affiliation":"","msr_institution":"","msr_host":"","msr_version":"","msr_duration":"","msr_original_fields_of_study":"","msr_release_tracker_id":"","msr_s2_match_type":"","msr_citation_count_updated":"","msr_published_date":"2013-1-1","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"http:\/\/www.jennwv.com\/papers\/measures.pdf","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":0,"msr_citation_count":0,"msr_influential_citations":0,"msr_reference_count":0,"msr_s2_match_confidence":0,"msr_microsoftintellectualproperty":true,"msr_s2_open_access":false,"msr_s2_author_ids":[],"msr_pub_ids":[],"msr_hide_image_in_river":0,"footnotes":""},"msr-research-highlight":[],"research-area":[13561,13556,13548],"msr-publication-type":[193716],"msr-publisher":[],"msr-focus-area":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-165873","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-algorithms","msr-research-area-artificial-intelligence","msr-research-area-economics","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2013-1-1","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"http:\/\/www.jennwv.com\/papers\/measures.pdf","msr_doi":"","msr_publication_uploader":[{"type":"url","viewUrl":"false","id":"false","title":"http:\/\/www.jennwv.com\/papers\/measures.pdf","label_id":"243109","label":0}],"msr_related_uploader":"","msr_citation_count":0,"msr_citation_count_updated":"","msr_s2_paper_id":"","msr_influential_citations":0,"msr_reference_count":0,"msr_arxiv_id":"","msr_s2_author_ids":[],"msr_s2_open_access":false,"msr_s2_pdf_url":null,"msr_attachments":[{"id":0,"url":"http:\/\/www.jennwv.com\/papers\/measures.pdf"}],"msr-author-ordering":[{"type":"text","value":"Yiling Chen","user_id":0,"rest_url":false},{"type":"text","value":"Michael Ruberry","user_id":0,"rest_url":false},{"type":"user_nicename","value":"Jenn Wortman Vaughan","user_id":32235,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Jenn Wortman Vaughan"}],"msr_impact_theme":[],"msr_research_lab":[199571],"msr_event":[],"msr_group":[144904],"msr_project":[171055],"publication":[],"video":[],"msr-tool":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":171055,"post_title":"Prediction Engines","post_name":"prediction-engines","post_type":"msr-project","post_date":"2012-11-12 11:49:03","post_modified":"2021-11-11 17:27:16","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/prediction-engines\/","post_excerpt":"Research around information aggregation and prediction, including polls, probability elicitation, and prediction markets.These methods, broadly defined as wisdom of the crowds, are utilized for a range of outcomes: elections, marketing, internal corporate, military intelligence, etc. We demonstrate some serious advances. (1) Combinatorial Prediction Markets: frontend, backened, and unique questions. (2) Experimental Prediction Markets and Polling. 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