{"id":168412,"date":"2015-06-01T00:00:00","date_gmt":"2015-06-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/integrating-market-makers-limit-orders-and-continuous-trade-in-prediction-markets\/"},"modified":"2018-10-16T20:18:52","modified_gmt":"2018-10-17T03:18:52","slug":"integrating-market-makers-limit-orders-and-continuous-trade-in-prediction-markets","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/integrating-market-makers-limit-orders-and-continuous-trade-in-prediction-markets\/","title":{"rendered":"Integrating Market Makers, Limit Orders, and Continuous Trade in Prediction Markets"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We provide the first concrete algorithm for combining market makers and limit orders in a prediction market with continuous trade. Our mechanism is general enough to handle both bundle orders and arbitrary securities defined over combinatorial outcome spaces. We define the notion of an approximately fair trading path, a path in security space along which no order executes at a price more than a fixed tolerance above its limit, and every order executes when its market price falls more than a fixed tolerance below its limit. We show that under a certain supermodularity condition, a fair trading path exists for which the endpoint is efficient, but that under very general conditions, reaching an efficient endpoint via a fair trading path is not possible. We develop an algorithm for operating a continuous market maker with limit orders that respects the fairness conditions in the general case in which the supermodularity condition may not hold. We conduct simulations of our algorithm using real combinatorial predictions made during the 2008 U.S. Presidential election and evaluate it against a natural baseline according to trading volume, social welfare, and violations of the two fairness conditions.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We provide the first concrete algorithm for combining market makers and limit orders in a prediction market with continuous trade. Our mechanism is general enough to handle both bundle orders and arbitrary securities defined over combinatorial outcome spaces. We define the notion of an approximately fair trading path, a path in security space along which [&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":"ACM - Association for Computing Machinery","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":"\u00a9 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version can be found at http:\/\/dl.acm.org.","msr_conference_name":"","msr_doi":"10.1145\/2764468.2764532","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Hoda Heidari, 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":"2015-06-01","msr_highlight_text":"","msr_notes":"","msr_longbiography":"","msr_publicationurl":"","msr_external_url":"","msr_secondary_video_url":"","msr_conference_url":"","msr_journal_url":"","msr_s2_pdf_url":"","msr_year":2015,"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":[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-168412","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-economics","msr-locale-en_us"],"msr_publishername":"ACM - Association for Computing Machinery","msr_edition":"","msr_affiliation":"","msr_published_date":"2015-06-01","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":"204291","msr_publicationurl":"","msr_doi":"10.1145\/2764468.2764532","msr_publication_uploader":[{"type":"file","title":"HLPV-wapp.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/HLPV-wapp.pdf","id":204291,"label_id":0},{"type":"doi","title":"10.1145\/2764468.2764532","viewUrl":false,"id":false,"label_id":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":204291,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/HLPV-wapp.pdf"}],"msr-author-ordering":[{"type":"text","value":"Hoda Heidari","user_id":0,"rest_url":false},{"type":"user_nicename","value":"slahaie","user_id":33682,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=slahaie"},{"type":"user_nicename","value":"dpennock","user_id":31679,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=dpennock"},{"type":"user_nicename","value":"jenn","user_id":32235,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=jenn"}],"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. 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