{"id":"https://openalex.org/W3034572225","doi":"https://doi.org/10.24963/ijcai.2020/25","title":"Prophet Inequalities for Bayesian Persuasion","display_name":"Prophet Inequalities for Bayesian Persuasion","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3034572225","doi":"https://doi.org/10.24963/ijcai.2020/25","mag":"3034572225"},"language":"en","primary_location":{"id":"doi:10.24963/ijcai.2020/25","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/25","pdf_url":"https://www.ijcai.org/proceedings/2020/0025.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.ijcai.org/proceedings/2020/0025.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5086569853","display_name":"Niklas Hahn","orcid":"https://orcid.org/0000-0002-4929-0542"},"institutions":[{"id":"https://openalex.org/I114090438","display_name":"Goethe University Frankfurt","ror":"https://ror.org/04cvxnb49","country_code":"DE","type":"education","lineage":["https://openalex.org/I114090438"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Niklas Hahn","raw_affiliation_strings":["Goethe University Frankfurt, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Goethe University Frankfurt, Germany","institution_ids":["https://openalex.org/I114090438"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065332233","display_name":"Martin Hoefer","orcid":"https://orcid.org/0000-0003-0131-5605"},"institutions":[{"id":"https://openalex.org/I114090438","display_name":"Goethe University Frankfurt","ror":"https://ror.org/04cvxnb49","country_code":"DE","type":"education","lineage":["https://openalex.org/I114090438"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Martin Hoefer","raw_affiliation_strings":["Goethe University Frankfurt, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Goethe University Frankfurt, Germany","institution_ids":["https://openalex.org/I114090438"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5046418946","display_name":"Rann Smorodinsky","orcid":"https://orcid.org/0000-0001-7447-5605"},"institutions":[{"id":"https://openalex.org/I174306211","display_name":"Technion \u2013 Israel Institute of Technology","ror":"https://ror.org/03qryx823","country_code":"IL","type":"education","lineage":["https://openalex.org/I174306211"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Rann Smorodinsky","raw_affiliation_strings":["Technion, Israel"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technion, Israel","institution_ids":["https://openalex.org/I174306211"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5065332233"],"corresponding_institution_ids":["https://openalex.org/I114090438"],"apc_list":null,"apc_paid":null,"fwci":1.7545,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.86196823,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"175","last_page":"181"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11182","display_name":"Auction Theory and Applications","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/T11182","display_name":"Auction Theory and Applications","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/T11031","display_name":"Game Theory and Applications","score":0.9994999766349792,"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/T11161","display_name":"Consumer Market Behavior and Pricing","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/persuasion","display_name":"Persuasion","score":0.7856522798538208},{"id":"https://openalex.org/keywords/communication-source","display_name":"Communication source","score":0.7494102120399475},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.6131727695465088},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6113527417182922},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5243834853172302},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5151018500328064},{"id":"https://openalex.org/keywords/valuation","display_name":"Valuation (finance)","score":0.495686411857605},{"id":"https://openalex.org/keywords/discounting","display_name":"Discounting","score":0.4709150493144989},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4701393246650696},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.44064202904701233},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.41229143738746643},{"id":"https://openalex.org/keywords/mathematical-economics","display_name":"Mathematical economics","score":0.3744884133338928},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3647152781486511},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.359866201877594},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.33860790729522705},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.33209818601608276},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2854089140892029},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.26930326223373413},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.13351434469223022},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.10130023956298828}],"concepts":[{"id":"https://openalex.org/C2781310500","wikidata":"https://www.wikidata.org/wiki/Q1231428","display_name":"Persuasion","level":2,"score":0.7856522798538208},{"id":"https://openalex.org/C198104137","wikidata":"https://www.wikidata.org/wiki/Q974688","display_name":"Communication source","level":2,"score":0.7494102120399475},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6131727695465088},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6113527417182922},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5243834853172302},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5151018500328064},{"id":"https://openalex.org/C186027771","wikidata":"https://www.wikidata.org/wiki/Q4008379","display_name":"Valuation (finance)","level":2,"score":0.495686411857605},{"id":"https://openalex.org/C6177178","wikidata":"https://www.wikidata.org/wiki/Q10998070","display_name":"Discounting","level":2,"score":0.4709150493144989},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4701393246650696},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.44064202904701233},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.41229143738746643},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.3744884133338928},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3647152781486511},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.359866201877594},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33860790729522705},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33209818601608276},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2854089140892029},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.26930326223373413},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.13351434469223022},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.10130023956298828},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.24963/ijcai.2020/25","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/25","pdf_url":"https://www.ijcai.org/proceedings/2020/0025.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.24963/ijcai.2020/25","is_oa":true,"landing_page_url":"https://doi.org/10.24963/ijcai.2020/25","pdf_url":"https://www.ijcai.org/proceedings/2020/0025.pdf","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.550000011920929}],"awards":[{"id":"https://openalex.org/G2031634505","display_name":null,"funder_award_id":"2016734","funder_id":"https://openalex.org/F4320320950","funder_display_name":"United States-Israel Binational Science Foundation"},{"id":"https://openalex.org/G575664632","display_name":null,"funder_award_id":"2016734","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"},{"id":"https://openalex.org/F4320320950","display_name":"United States-Israel Binational Science Foundation","ror":"https://ror.org/00j8z2m73"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3034572225.pdf","grobid_xml":"https://content.openalex.org/works/W3034572225.grobid-xml"},"referenced_works_count":26,"referenced_works":["https://openalex.org/W1594152038","https://openalex.org/W1984877922","https://openalex.org/W1986767499","https://openalex.org/W2024679443","https://openalex.org/W2077124610","https://openalex.org/W2080134044","https://openalex.org/W2080956807","https://openalex.org/W2149247676","https://openalex.org/W2178781446","https://openalex.org/W2179880041","https://openalex.org/W2188039674","https://openalex.org/W2194892124","https://openalex.org/W2491792521","https://openalex.org/W2675314026","https://openalex.org/W2913317949","https://openalex.org/W2947806810","https://openalex.org/W2950397526","https://openalex.org/W2963457633","https://openalex.org/W2963515037","https://openalex.org/W2964688751","https://openalex.org/W2997617434","https://openalex.org/W3042130898","https://openalex.org/W3121952712","https://openalex.org/W3123076852","https://openalex.org/W3124085812","https://openalex.org/W4302796818"],"related_works":["https://openalex.org/W1193337282","https://openalex.org/W1662240627","https://openalex.org/W2161303371","https://openalex.org/W3169835994","https://openalex.org/W1546646725","https://openalex.org/W1974065322","https://openalex.org/W1536610849","https://openalex.org/W1480504010","https://openalex.org/W2168899451","https://openalex.org/W2491046942"],"abstract_inverted_index":{"We":[0,98,134,165],"study":[1],"an":[2,12,70,119,136],"information-structure":[3],"design":[4,99],"problem":[5],"(i.e.,":[6],"a":[7,22,61,124,145,151],"Bayesian":[8,142],"persuasion":[9],"problem)":[10],"in":[11,67,132],"online":[13,101,141],"scenario.":[14],"Inspired":[15],"by":[16,32,123],"the":[17,40,44,51,56,64,79,85,105,109,113,148,167],"classic":[18],"gambler's":[19],"problem,":[20],"consider":[21],"set":[23],"of":[24,87,118],"candidates":[25,92],"who":[26,127],"arrive":[27],"sequentially":[28],"and":[29,172,181],"are":[30,158],"evaluated":[31],"one":[33],"agent":[34,38,84],"(the":[35],"sender).":[36],"This":[37],"learns":[39],"value":[41,52],"from":[42],"hiring":[43],"candidate":[45,130],"to":[46,53,63,77,116,169],"herself":[47],"as":[48,50],"well":[49],"another":[54],"agent,":[55],"receiver.":[57],"The":[58],"sender":[59,126],"provides":[60],"signal":[62],"receiver":[65],"who,":[66],"turn,":[68],"makes":[69],"irrevocable":[71],"decision":[72],"on":[73],"whether":[74],"or":[75],"not":[76,95],"hire":[78],"candidate.":[80],"A-priori,":[81],"for":[82,104,140,176],"each":[83],"distribution":[86],"valuation":[88],"is":[89],"independent":[90],"across":[91],"but":[93],"may":[94],"be":[96],"identical.":[97],"good":[100],"signaling":[102],"schemes":[103],"sender.":[106],"To":[107],"assess":[108],"performance,":[110],"we":[111],"compare":[112],"expected":[114],"utility":[115],"that":[117],"optimal":[120,137],"offline":[121],"scheme":[122],"prophet":[125,138,174],"knows":[128],"all":[129],"realizations":[131],"advance.":[133],"show":[135],"inequality":[139],"persuasion,":[143],"with":[144,178],"1/2-approximation":[146],"when":[147],"instance":[149],"satisfies":[150],"\"satisfactory-status-quo\"":[152],"assumption.":[153],"Without":[154],"this":[155],"assumption,":[156],"there":[157],"instances":[159],"without":[160],"any":[161],"finite":[162],"approximation":[163],"factor.":[164],"extend":[166],"results":[168],"combinatorial":[170],"domains":[171],"obtain":[173],"inequalities":[175],"matching":[177],"multiple":[179,182],"hires":[180],"receivers.":[183]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}