{"id":"https://openalex.org/W7161064146","doi":"https://doi.org/10.48550/arxiv.2605.12111","title":"Adaptive Multi-Round Allocation with Stochastic Arrivals","display_name":"Adaptive Multi-Round Allocation with Stochastic Arrivals","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7161064146","doi":"https://doi.org/10.48550/arxiv.2605.12111"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.12111","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12111","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.12111","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136040466","display_name":"Yuqi Pan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pan, Yuqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136021360","display_name":"Davin Choo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Choo, Davin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136011927","display_name":"Haichuan Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Haichuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136067014","display_name":"Milind Tambe","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tambe, Milind","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005065579","display_name":"Alastair van Heerden","orcid":"https://orcid.org/0000-0003-2530-6885"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"van Heerden, Alastair","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136054990","display_name":"Cheryl Johnson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Johnson, Cheryl","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11031","display_name":"Game Theory and Applications","score":0.22550000250339508,"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/T11031","display_name":"Game Theory and Applications","score":0.22550000250339508,"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/T12288","display_name":"Optimization and Search Problems","score":0.12479999661445618,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.1168999969959259,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6179999709129333},{"id":"https://openalex.org/keywords/recursion","display_name":"Recursion (computer science)","score":0.5231000185012817},{"id":"https://openalex.org/keywords/resource-allocation","display_name":"Resource allocation","score":0.48809999227523804},{"id":"https://openalex.org/keywords/inefficiency","display_name":"Inefficiency","score":0.4830999970436096},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4334999918937683},{"id":"https://openalex.org/keywords/bellman-equation","display_name":"Bellman equation","score":0.4106000065803528},{"id":"https://openalex.org/keywords/decision-problem","display_name":"Decision problem","score":0.3646000027656555},{"id":"https://openalex.org/keywords/dynamic-programming","display_name":"Dynamic programming","score":0.3564000129699707},{"id":"https://openalex.org/keywords/computational-complexity-theory","display_name":"Computational complexity theory","score":0.35339999198913574}],"concepts":[{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.6238999962806702},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6179999709129333},{"id":"https://openalex.org/C168773036","wikidata":"https://www.wikidata.org/wiki/Q264164","display_name":"Recursion (computer science)","level":2,"score":0.5231000185012817},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5105000138282776},{"id":"https://openalex.org/C29202148","wikidata":"https://www.wikidata.org/wiki/Q287260","display_name":"Resource allocation","level":2,"score":0.48809999227523804},{"id":"https://openalex.org/C2778869765","wikidata":"https://www.wikidata.org/wiki/Q6028363","display_name":"Inefficiency","level":2,"score":0.4830999970436096},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4334999918937683},{"id":"https://openalex.org/C14646407","wikidata":"https://www.wikidata.org/wiki/Q1430750","display_name":"Bellman equation","level":2,"score":0.4106000065803528},{"id":"https://openalex.org/C115988155","wikidata":"https://www.wikidata.org/wiki/Q3262192","display_name":"Decision problem","level":2,"score":0.3646000027656555},{"id":"https://openalex.org/C37404715","wikidata":"https://www.wikidata.org/wiki/Q380679","display_name":"Dynamic programming","level":2,"score":0.3564000129699707},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.35339999198913574},{"id":"https://openalex.org/C141042865","wikidata":"https://www.wikidata.org/wiki/Q200125","display_name":"Expected value","level":2,"score":0.34860000014305115},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3479999899864197},{"id":"https://openalex.org/C51823790","wikidata":"https://www.wikidata.org/wiki/Q504353","display_name":"Greedy algorithm","level":2,"score":0.3352999985218048},{"id":"https://openalex.org/C8505890","wikidata":"https://www.wikidata.org/wiki/Q605095","display_name":"Budget constraint","level":2,"score":0.3328000009059906},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32679998874664307},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.3222000002861023},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.3028999865055084},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.3018999993801117},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C311688","wikidata":"https://www.wikidata.org/wiki/Q2393193","display_name":"Time complexity","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C117410459","wikidata":"https://www.wikidata.org/wiki/Q1895266","display_name":"Marginal value","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C113336015","wikidata":"https://www.wikidata.org/wiki/Q574010","display_name":"Complete information","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.2793999910354614},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.12111","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12111","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.12111","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.12111","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,49,129],"study":[1],"a":[2,14,91,119,137,144,150],"sequential":[3],"resource":[4],"allocation":[5,55],"problem":[6,56],"motivated":[7],"by":[8],"adaptive":[9],"network":[10],"recruitment,":[11],"in":[12,125],"which":[13],"limited":[15],"budget":[16,102],"of":[17,83],"identical":[18],"resources":[19,42],"must":[20],"be":[21],"allocated":[22],"over":[23],"multiple":[24],"rounds":[25],"to":[26,43,78],"individuals":[27],"with":[28,122],"stochastic":[29],"referral":[30],"capacity.":[31],"Successful":[32],"referrals":[33],"endogenously":[34],"generate":[35],"future":[36],"decision":[37],"opportunities":[38],"while":[39],"allocating":[40],"additional":[41],"an":[44,58,109],"individual":[45],"exhibits":[46],"diminishing":[47],"returns.":[48],"first":[50],"show":[51],"that":[52,96,141],"the":[53,68,71,79,84,100,126],"single-round":[54,146],"admits":[57],"exact":[59,110],"greedy":[60],"solution":[61],"based":[62],"on":[63,99,159],"marginal":[64],"survival":[65],"probabilities.":[66],"In":[67],"multi-round":[69,138],"setting,":[70],"resulting":[72],"Bellman":[73],"recursion":[74],"is":[75],"intractable":[76],"due":[77],"stochastic,":[80],"high-dimensional":[81],"evolution":[82],"frontier.":[85],"To":[86],"address":[87],"this,":[88],"we":[89,155],"introduce":[90],"population-level":[92,151],"surrogate":[93,107],"value":[94],"function":[95],"depends":[97],"only":[98],"remaining":[101],"and":[103,149],"frontier":[104,147],"size.":[105],"This":[106],"enables":[108],"dynamic":[111],"program":[112],"via":[113],"truncated":[114],"probability":[115],"generating":[116],"functions,":[117],"yielding":[118],"planning":[120],"algorithm":[121],"polynomial":[123],"complexity":[124],"total":[127],"budget.":[128],"further":[130],"analyze":[131],"robustness":[132],"under":[133],"model":[134],"misspecification,":[135],"proving":[136],"error":[139,148],"bound":[140],"decomposes":[142],"into":[143],"tight":[145],"transition":[152],"error.":[153],"Finally,":[154],"evaluate":[156],"our":[157],"method":[158],"real-world":[160],"inspired":[161],"recruitment":[162],"scenarios.":[163]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-14T00:00:00"}
