{"id":"https://openalex.org/W3134994861","doi":"https://doi.org/10.1145/3572885.3572889","title":"On modeling human perceptions of allocation policies with uncertain outcomes","display_name":"On modeling human perceptions of allocation policies with uncertain outcomes","publication_year":2022,"publication_date":"2022-07-01","ids":{"openalex":"https://openalex.org/W3134994861","doi":"https://doi.org/10.1145/3572885.3572889","mag":"3134994861"},"language":"en","primary_location":{"id":"doi:10.1145/3572885.3572889","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3572885.3572889","pdf_url":null,"source":{"id":"https://openalex.org/S4210233839","display_name":"ACM SIGecom Exchanges","issn_l":"1551-9031","issn":["1551-9031"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM SIGecom Exchanges","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5037735812","display_name":"Hoda Heidari","orcid":"https://orcid.org/0000-0003-3710-4076"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hoda Heidari","raw_affiliation_strings":["Carnegie Mellon University"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074825066","display_name":"Solon Barocas","orcid":"https://orcid.org/0000-0003-4577-466X"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]},{"id":"https://openalex.org/I4210164937","display_name":"Microsoft Research (United Kingdom)","ror":"https://ror.org/05k87vq12","country_code":"GB","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210164937"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Solon Barocas","raw_affiliation_strings":["Microsoft Research & Cornell University"],"affiliations":[{"raw_affiliation_string":"Microsoft Research & Cornell University","institution_ids":["https://openalex.org/I4210164937","https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055710645","display_name":"Jon Kleinberg","orcid":"https://orcid.org/0000-0002-1929-2512"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jon Kleinberg","raw_affiliation_strings":["Cornell University"],"affiliations":[{"raw_affiliation_string":"Cornell University","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040228064","display_name":"Karen Levy","orcid":"https://orcid.org/0000-0003-3806-9161"},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Karen Levy","raw_affiliation_strings":["Cornell University"],"affiliations":[{"raw_affiliation_string":"Cornell University","institution_ids":["https://openalex.org/I205783295"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5037735812"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":0.3498,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.48925501,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"20","issue":"1","first_page":"47","last_page":"54"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10315","display_name":"Decision-Making and Behavioral Economics","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1800","display_name":"General Decision Sciences"},"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/T10315","display_name":"Decision-Making and Behavioral Economics","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/1800","display_name":"General Decision Sciences"},"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/T10841","display_name":"Economic and Environmental Valuation","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10804","display_name":"Health Systems, Economic Evaluations, Quality of Life","score":0.9889000058174133,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/harm","display_name":"Harm","score":0.7743837833404541},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.7528846263885498},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5675672888755798},{"id":"https://openalex.org/keywords/value","display_name":"Value (mathematics)","score":0.4773550033569336},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.46435976028442383},{"id":"https://openalex.org/keywords/expected-utility-hypothesis","display_name":"Expected utility hypothesis","score":0.4562981128692627},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.4424421787261963},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.42638713121414185},{"id":"https://openalex.org/keywords/actuarial-science","display_name":"Actuarial science","score":0.4043664336204529},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.3726298213005066},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.3713289499282837},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.19377073645591736},{"id":"https://openalex.org/keywords/mathematical-economics","display_name":"Mathematical economics","score":0.16560932993888855},{"id":"https://openalex.org/keywords/social-psychology","display_name":"Social psychology","score":0.1411871314048767},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.09197866916656494}],"concepts":[{"id":"https://openalex.org/C2777363581","wikidata":"https://www.wikidata.org/wiki/Q15098235","display_name":"Harm","level":2,"score":0.7743837833404541},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.7528846263885498},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5675672888755798},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.4773550033569336},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.46435976028442383},{"id":"https://openalex.org/C205706631","wikidata":"https://www.wikidata.org/wiki/Q2319304","display_name":"Expected utility hypothesis","level":2,"score":0.4562981128692627},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.4424421787261963},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.42638713121414185},{"id":"https://openalex.org/C162118730","wikidata":"https://www.wikidata.org/wiki/Q1128453","display_name":"Actuarial science","level":1,"score":0.4043664336204529},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.3726298213005066},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3713289499282837},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.19377073645591736},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.16560932993888855},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.1411871314048767},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.09197866916656494},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3572885.3572889","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3572885.3572889","pdf_url":null,"source":{"id":"https://openalex.org/S4210233839","display_name":"ACM SIGecom Exchanges","issn_l":"1551-9031","issn":["1551-9031"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM SIGecom Exchanges","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W33891176","https://openalex.org/W1973034164","https://openalex.org/W2064313477","https://openalex.org/W2089012547","https://openalex.org/W2099161251","https://openalex.org/W2114969359","https://openalex.org/W2123112903","https://openalex.org/W2147961775","https://openalex.org/W2887324295","https://openalex.org/W2990271538","https://openalex.org/W3122760232","https://openalex.org/W4237077787","https://openalex.org/W6678272429","https://openalex.org/W6743716967","https://openalex.org/W6753690519"],"related_works":["https://openalex.org/W2180954594","https://openalex.org/W2052835778","https://openalex.org/W2049003611","https://openalex.org/W2127804977","https://openalex.org/W2108418243","https://openalex.org/W164103134","https://openalex.org/W2787352659","https://openalex.org/W4206560911","https://openalex.org/W1970611213","https://openalex.org/W1707372784"],"abstract_inverted_index":{"Many":[0],"policies":[1,30,48,177,205],"allocate":[2],"harms":[3],"or":[4,26,68],"benefits":[5],"that":[6,43,127,145,164,188,206],"are":[7,52],"uncertain":[8],"in":[9,17,44,50,120,153,225,232],"nature:":[10],"they":[11,70],"produce":[12],"distributions":[13,140],"over":[14,94,113,138],"the":[15,47,61,65,75,103,109,190],"population":[16],"which":[18,112],"individuals":[19],"have":[20],"different":[21,29],"probabilities":[22],"of":[23,35,64,105,141,156,193,203],"incurring":[24],"harm":[25,67,142,182],"benefit.":[27],"Comparing":[28],"thus":[31],"involves":[32],"a":[33,81,88,154,201],"comparison":[34],"their":[36],"corresponding":[37],"probability":[38,106,128,194],"distributions,":[39],"and":[40,143,152,183,198],"we":[41,98,174,199],"observe":[42],"many":[45],"instances":[46],"selected":[49],"practice":[51],"hard":[53,166],"to":[54,133,167,228],"explain":[55],"by":[56,169],"preferences":[57,93,137,163],"based":[58,101],"only":[59],"on":[60,102],"expected":[62,76],"value":[63,77],"total":[66,181,186],"benefit":[69,144,187],"produce.":[71],"In":[72,172],"cases":[73,157],"where":[74],"analysis":[78,212],"is":[79,222],"not":[80,214],"sufficient":[82],"explanatory":[83],"framework,":[84],"what":[85],"would":[86],"be":[87,131],"reasonable":[89],"model":[90],"for":[91,161,178,218],"societal":[92],"these":[95],"distributions?":[96],"Here":[97],"investigate":[99],"explanations":[100,160],"framework":[104],"weighting":[107,129,195],"from":[108,149],"behavioral":[110],"sciences,":[111],"several":[114],"decades":[115],"has":[116],"identified":[117],"systematic":[118],"biases":[119],"how":[121],"people":[122],"perceive":[123],"probabilities.":[124],"We":[125],"show":[126],"can":[130],"used":[132],"make":[134],"predictions":[135],"about":[136],"probabilistic":[139],"function":[146],"quite":[147],"differently":[148],"expected-value":[150],"analysis,":[151],"number":[155,202],"provide":[158,215],"potential":[159],"policy":[162,219,233],"appear":[165],"motivate":[168],"other":[170],"means.":[171],"particular,":[173],"identify":[175],"optimal":[176],"minimizing":[179],"perceived":[180,185],"maximizing":[184],"take":[189],"distorting":[191],"effects":[192],"into":[196],"account,":[197],"discuss":[200],"real-world":[204],"resemble":[207],"such":[208],"allocational":[209],"strategies.":[210],"Our":[211],"does":[213],"specific":[216],"recommendations":[217],"choices,":[220],"but":[221],"instead":[223],"interpretive":[224],"nature,":[226],"seeking":[227],"describe":[229],"observed":[230],"phenomena":[231],"choices.":[234]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}