{"id":"https://openalex.org/W7130421626","doi":"https://doi.org/10.48550/arxiv.2602.15572","title":"Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model","display_name":"Neural Network-Based Parameter Estimation of a Labour Market Agent-Based Model","publication_year":2026,"publication_date":"2026-02-17","ids":{"openalex":"https://openalex.org/W7130421626","doi":"https://doi.org/10.48550/arxiv.2602.15572"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.15572","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15572","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2602.15572","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126336335","display_name":"M. Lopes Alves","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Alves, M Lopes","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006492439","display_name":"Joel Dyer","orcid":"https://orcid.org/0000-0002-8304-8450"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dyer, Joel","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082930180","display_name":"Doyne Farmer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Farmer, Doyne","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126326938","display_name":"Michael Wooldridge","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wooldridge, Michael","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5020492975","display_name":"Anisoara Calinescu","orcid":"https://orcid.org/0000-0003-2082-734X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Calinescu, Anisoara","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5126336335"],"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/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.33550000190734863,"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"}},"topics":[{"id":"https://openalex.org/T11270","display_name":"Complex Systems and Time Series Analysis","score":0.33550000190734863,"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/T12659","display_name":"Innovation Diffusion and Forecasting","score":0.0348999984562397,"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/T12592","display_name":"Opinion Dynamics and Social Influence","score":0.03269999846816063,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.6468999981880188},{"id":"https://openalex.org/keywords/limit","display_name":"Limit (mathematics)","score":0.5803999900817871},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5691999793052673},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5555999875068665},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4941999912261963},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.476500004529953},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4090000092983246},{"id":"https://openalex.org/keywords/statistical-inference","display_name":"Statistical inference","score":0.3917999863624573}],"concepts":[{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.6468999981880188},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.5803999900817871},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5691999793052673},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5555999875068665},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5529000163078308},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.5315999984741211},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4941999912261963},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.476500004529953},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4090000092983246},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4066999852657318},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39629998803138733},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.3917999863624573},{"id":"https://openalex.org/C2983447341","wikidata":"https://www.wikidata.org/wiki/Q1413083","display_name":"Model parameter","level":2,"score":0.3700999915599823},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.3691999912261963},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.34209999442100525},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3346000015735626},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3278999924659729},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.32089999318122864},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.27959999442100525},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C114118609","wikidata":"https://www.wikidata.org/wiki/Q3036837","display_name":"Market data","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.2741999924182892}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.15572","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15572","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.15572","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.15572","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"score":0.6971174478530884,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Agent-based":[0],"modelling":[1],"(ABM)":[2],"is":[3,40,75,90],"a":[4,59,111],"widespread":[5],"approach":[6,129],"to":[7,48,77,146],"simulate":[8],"complex":[9],"systems.":[10],"Advancements":[11],"in":[12,43],"computational":[13,49],"processing":[14],"and":[15,95,142],"storage":[16],"have":[17],"facilitated":[18],"the":[19,53,96,104,127,131],"adoption":[20],"of":[21,106,113],"ABMs":[22,27],"across":[23,138],"many":[24],"fields;":[25],"however,":[26],"face":[28],"challenges":[29],"that":[30,65,117,126],"limit":[31],"their":[32],"use":[33],"as":[34],"decision-support":[35],"tools.":[36],"A":[37],"significant":[38],"issue":[39],"parameter":[41,54,71],"estimation":[42],"large-scale":[44],"ABMs,":[45],"particularly":[46],"due":[47],"constraints":[50],"on":[51,84],"exploring":[52],"space.":[55],"This":[56,73],"study":[57],"evaluates":[58],"state-of-the-art":[60],"simulation-based":[61],"inference":[62],"(SBI)":[63],"framework":[64,74],"uses":[66],"neural":[67],"networks":[68],"(NN)":[69],"for":[70],"estimation.":[72],"applied":[76],"an":[78,120],"established":[79],"labour":[80,99],"market":[81],"ABM":[82,89],"based":[83],"job":[85],"transition":[86],"networks.":[87],"The":[88,123],"initiated":[91],"with":[92,116],"synthetic":[93],"datasets":[94],"real":[97],"U.S.":[98],"market.":[100],"Next,":[101],"we":[102],"compare":[103],"effectiveness":[105],"summary":[107],"statistics":[108],"derived":[109],"from":[110],"list":[112],"statistical":[114],"measures":[115],"learned":[118],"by":[119],"embedded":[121],"NN.":[122],"results":[124],"demonstrate":[125],"NN-based":[128],"recovers":[130],"original":[132],"parameters":[133],"when":[134],"evaluating":[135],"posterior":[136],"distributions":[137],"various":[139],"dataset":[140],"scales":[141],"improves":[143],"efficiency":[144],"compared":[145],"traditional":[147],"Bayesian":[148],"methods.":[149]},"counts_by_year":[],"updated_date":"2026-02-19T06:31:58.851227","created_date":"2026-02-19T00:00:00"}