{"id":"https://openalex.org/W2963032716","doi":"https://doi.org/10.1145/3219166.3219193","title":"Strategic Classification from Revealed Preferences","display_name":"Strategic Classification from Revealed Preferences","publication_year":2018,"publication_date":"2018-06-11","ids":{"openalex":"https://openalex.org/W2963032716","doi":"https://doi.org/10.1145/3219166.3219193","mag":"2963032716"},"language":"en","primary_location":{"id":"doi:10.1145/3219166.3219193","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3219166.3219193","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3219166.3219193","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM Conference on Economics and Computation","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3219166.3219193","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076817901","display_name":"Jinshuo Dong","orcid":"https://orcid.org/0000-0002-2741-6722"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jinshuo Dong","raw_affiliation_strings":["University of Pennsylvania, Philadelphia, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pennsylvania, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057693522","display_name":"Aaron Roth","orcid":"https://orcid.org/0000-0002-0586-0515"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aaron Roth","raw_affiliation_strings":["University of Pennsylvania, Philadelphia, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pennsylvania, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085368888","display_name":"Zachary Schutzman","orcid":"https://orcid.org/0000-0002-3448-5654"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zachary Schutzman","raw_affiliation_strings":["University of Pennsylvania, Philadelphia, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pennsylvania, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076830609","display_name":"Bo Waggoner","orcid":"https://orcid.org/0000-0002-1366-1065"},"institutions":[{"id":"https://openalex.org/I79576946","display_name":"University of Pennsylvania","ror":"https://ror.org/00b30xv10","country_code":"US","type":"education","lineage":["https://openalex.org/I79576946"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bo Waggoner","raw_affiliation_strings":["University of Pennsylvania, Philadelphia, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Pennsylvania, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I79576946"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001070941","display_name":"Zhiwei Steven Wu","orcid":"https://orcid.org/0000-0002-8125-8227"},"institutions":[{"id":"https://openalex.org/I1290206253","display_name":"Microsoft (United States)","ror":"https://ror.org/00d0nc645","country_code":"US","type":"company","lineage":["https://openalex.org/I1290206253"]},{"id":"https://openalex.org/I4401726785","display_name":"Microsoft Research New York City (United States)","ror":"https://ror.org/056zprp28","country_code":null,"type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4401726785"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhiwei Steven Wu","raw_affiliation_strings":["Microsoft Research, New York, NY, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Microsoft Research, New York, NY, USA","institution_ids":["https://openalex.org/I1290206253","https://openalex.org/I4401726785"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":11.8904,"has_fulltext":true,"cited_by_count":105,"citation_normalized_percentile":{"value":0.98675937,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"55","last_page":"70"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11182","display_name":"Auction Theory and Applications","score":0.9983000159263611,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12072","display_name":"Machine Learning and Algorithms","score":0.9932000041007996,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/regret","display_name":"Regret","score":0.8923909664154053},{"id":"https://openalex.org/keywords/hindsight-bias","display_name":"Hindsight bias","score":0.8693608045578003},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7784234285354614},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7015120387077332},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5797831416130066},{"id":"https://openalex.org/keywords/stackelberg-competition","display_name":"Stackelberg competition","score":0.5777261853218079},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5376551747322083},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16270193457603455},{"id":"https://openalex.org/keywords/mathematical-economics","display_name":"Mathematical economics","score":0.08860918879508972},{"id":"https://openalex.org/keywords/cognitive-psychology","display_name":"Cognitive psychology","score":0.07525372505187988},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.07396736741065979}],"concepts":[{"id":"https://openalex.org/C50817715","wikidata":"https://www.wikidata.org/wiki/Q79895177","display_name":"Regret","level":2,"score":0.8923909664154053},{"id":"https://openalex.org/C10347200","wikidata":"https://www.wikidata.org/wiki/Q1960297","display_name":"Hindsight bias","level":2,"score":0.8693608045578003},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7784234285354614},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7015120387077332},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5797831416130066},{"id":"https://openalex.org/C199510392","wikidata":"https://www.wikidata.org/wiki/Q1184602","display_name":"Stackelberg competition","level":2,"score":0.5777261853218079},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5376551747322083},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16270193457603455},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.08860918879508972},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.07525372505187988},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.07396736741065979}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3219166.3219193","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3219166.3219193","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3219166.3219193","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM Conference on Economics and Computation","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3219166.3219193","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3219166.3219193","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3219166.3219193","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM Conference on Economics and Computation","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.41999998688697815,"id":"https://metadata.un.org/sdg/4"}],"awards":[{"id":"https://openalex.org/G1700429692","display_name":null,"funder_award_id":"CNS-1253345","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2963032716.pdf","grobid_xml":"https://content.openalex.org/works/W2963032716.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W568984285","https://openalex.org/W1568307856","https://openalex.org/W1596763386","https://openalex.org/W1780330426","https://openalex.org/W1965035964","https://openalex.org/W1966912382","https://openalex.org/W2004001705","https://openalex.org/W2029010138","https://openalex.org/W2097860933","https://openalex.org/W2097895487","https://openalex.org/W2118508075","https://openalex.org/W2121949863","https://openalex.org/W2145901875","https://openalex.org/W2148825261","https://openalex.org/W2149836368","https://openalex.org/W2241592413","https://openalex.org/W2293768274","https://openalex.org/W2410251583","https://openalex.org/W2913525780","https://openalex.org/W2962775797","https://openalex.org/W2963326510","https://openalex.org/W2963436662","https://openalex.org/W2963831922","https://openalex.org/W2964236408","https://openalex.org/W3018604818","https://openalex.org/W4206742934","https://openalex.org/W4249667877","https://openalex.org/W4253730333"],"related_works":["https://openalex.org/W3197854638","https://openalex.org/W3140454661","https://openalex.org/W4245029315","https://openalex.org/W1492315459","https://openalex.org/W1512434910","https://openalex.org/W2540910169","https://openalex.org/W3148904318","https://openalex.org/W2139970489","https://openalex.org/W2022803902","https://openalex.org/W2105474389"],"abstract_inverted_index":{"We":[0],"study":[1],"an":[2,21,36],"online":[3],"linear":[4],"classification":[5,26],"problem":[6],"in":[7,20,138],"which":[8,68],"the":[9,25,30,50,61,75,86,124,135,143,152],"data":[10],"is":[11,77,108,126],"generated":[12],"by":[13],"strategic":[14],"agents":[15,146],"who":[16],"manipulate":[17],"their":[18,82],"features":[19,45],"effort":[22],"to":[23,46,49,80,110,151],"change":[24],"outcome.":[27],"In":[28],"rounds,":[29],"learner":[31,56,76,125],"deploys":[32],"a":[33,93,102,116],"classifier,":[34],"then":[35],"adversarially":[37],"chosen":[38],"agent":[39,97],"arrives":[40],"and":[41],"possibly":[42],"manipulates":[43],"her":[44],"optimally":[47],"respond":[48],"learner's":[51],"choice":[52],"of":[53,60,96,118,134],"classifier.":[54,154],"The":[55],"has":[57],"no":[58],"knowledge":[59],"agents'":[62],"utility":[63],"functions":[64],"or":[65],"\"real\"":[66],"features,":[67],"may":[69],"vary":[70],"widely":[71],"across":[72],"agents.":[73],"Instead,":[74],"only":[78],"able":[79,109],"observe":[81],"\"revealed":[83],"preferences\",":[84],"i.e.,":[85],"manipulated":[87],"feature":[88],"vectors":[89],"they":[90],"provide.":[91],"For":[92],"broad":[94],"family":[95],"cost":[98],"functions,":[99],"we":[100],"give":[101],"computationally":[103],"efficient":[104],"learning":[105],"algorithm":[106],"that":[107,121,123,133,145],"obtain":[111],"diminishing":[112],"\"Stackelberg":[113],"regret\"":[114],"---":[115],"form":[117],"policy":[119],"regret":[120],"guarantees":[122],"realizing":[127],"loss":[128],"nearly":[129],"as":[130,132],"small":[131],"best":[136],"classifier":[137],"hindsight,":[139],"even":[140],"allowing":[141],"for":[142],"fact":[144],"would":[147],"have":[148],"best-responded":[149],"differently":[150],"optimal":[153]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":31},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":18},{"year":2020,"cited_by_count":21},{"year":2019,"cited_by_count":9},{"year":2018,"cited_by_count":4}],"updated_date":"2026-06-24T13:16:06.693445","created_date":"2025-10-10T00:00:00"}