{"id":"https://openalex.org/W7124417726","doi":"https://doi.org/10.48550/arxiv.2601.10367","title":"Inverse Learning in $2\\times2$ Games: From Synthetic Interactions to Traffic Simulation","display_name":"Inverse Learning in $2\\times2$ Games: From Synthetic Interactions to Traffic Simulation","publication_year":2026,"publication_date":"2026-01-15","ids":{"openalex":"https://openalex.org/W7124417726","doi":"https://doi.org/10.48550/arxiv.2601.10367"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.10367","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.10367","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.2601.10367","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018581410","display_name":"Daniela Aguirre Salazar","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Salazar, Daniela Aguirre","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123247776","display_name":"Firas Moatemri","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Moatemri, Firas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5042936048","display_name":"Tatiana Tatarenko","orcid":"https://orcid.org/0000-0001-8951-112X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tatarenko, Tatiana","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5018581410"],"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":0.3034000098705292,"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":0.3034000098705292,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.2190999984741211,"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"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.06430000066757202,"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/estimator","display_name":"Estimator","score":0.7150999903678894},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.5702000260353088},{"id":"https://openalex.org/keywords/logit","display_name":"Logit","score":0.4733000099658966},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.40130001306533813},{"id":"https://openalex.org/keywords/behavioral-modeling","display_name":"Behavioral modeling","score":0.3693999946117401},{"id":"https://openalex.org/keywords/synthetic-data","display_name":"Synthetic data","score":0.3610999882221222},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.35019999742507935},{"id":"https://openalex.org/keywords/estimation-theory","display_name":"Estimation theory","score":0.3370000123977661}],"concepts":[{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.7150999903678894},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6291000247001648},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.5702000260353088},{"id":"https://openalex.org/C140331021","wikidata":"https://www.wikidata.org/wiki/Q1868104","display_name":"Logit","level":2,"score":0.4733000099658966},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.42579999566078186},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.40130001306533813},{"id":"https://openalex.org/C78639753","wikidata":"https://www.wikidata.org/wiki/Q3318160","display_name":"Behavioral modeling","level":2,"score":0.3693999946117401},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3610999882221222},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.35019999742507935},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3443000018596649},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.3370000123977661},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33480000495910645},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3149999976158142},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.31290000677108765},{"id":"https://openalex.org/C190669063","wikidata":"https://www.wikidata.org/wiki/Q5282043","display_name":"Discrete choice","level":2,"score":0.30649998784065247},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C8272713","wikidata":"https://www.wikidata.org/wiki/Q176737","display_name":"Stochastic process","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C55479107","wikidata":"https://www.wikidata.org/wiki/Q97663916","display_name":"Stochastic approximation","level":3,"score":0.2818000018596649},{"id":"https://openalex.org/C119043178","wikidata":"https://www.wikidata.org/wiki/Q320723","display_name":"Covariate","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C95057490","wikidata":"https://www.wikidata.org/wiki/Q6883984","display_name":"Mixed logit","level":3,"score":0.2786000072956085},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27730000019073486},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C135252773","wikidata":"https://www.wikidata.org/wiki/Q1567213","display_name":"Inverse problem","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C145912823","wikidata":"https://www.wikidata.org/wiki/Q113558","display_name":"Dynamics (music)","level":2,"score":0.25699999928474426},{"id":"https://openalex.org/C187523126","wikidata":"https://www.wikidata.org/wiki/Q17098330","display_name":"Inverse dynamics","level":3,"score":0.25290000438690186},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.10367","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.10367","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.2601.10367","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.10367","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Understanding":[0],"how":[1],"agents":[2],"coordinate":[3],"or":[4],"compete":[5],"from":[6],"limited":[7],"behavioral":[8,78,108],"data":[9],"is":[10],"central":[11],"to":[12],"modeling":[13],"strategic":[14],"interactions":[15],"in":[16,91],"traffic,":[17],"robotics,":[18],"and":[19,48,76,87,96,107],"other":[20],"multi-agent":[21],"systems.":[22],"In":[23],"this":[24],"work,":[25],"we":[26],"investigate":[27],"the":[28,70],"following":[29],"complementary":[30],"formulations":[31],"of":[32],"inverse":[33],"game-theoretic":[34],"learning:":[35],"(i)":[36],"a":[37,50],"Closed-form":[38],"Correlated":[39],"Equilibrium":[40],"Maximum-Likelihood":[41,54],"estimator":[42,55],"(CE-ML)":[43],"specialized":[44],"for":[45],"$2\\times2$":[46],"games;":[47],"(ii)":[49],"Logit":[51],"Best":[52],"Response":[53],"(LBR-ML)":[56],"that":[57],"captures":[58],"long-run":[59],"adaptation":[60],"dynamics":[61],"via":[62],"stochastic":[63],"response":[64],"processes.":[65],"Together,":[66],"these":[67],"approaches":[68],"span":[69],"spectrum":[71],"between":[72,103],"static":[73],"equilibrium":[74],"consistency":[75],"dynamic":[77],"realism.":[79],"We":[80],"evaluate":[81],"them":[82],"on":[83],"synthetic":[84],"\"chicken-dare\"":[85],"games":[86],"traffic-interaction":[88],"scenarios":[89],"simulated":[90],"SUMO,":[92],"comparing":[93],"parameter":[94],"recovery":[95],"distributional":[97],"fit.":[98],"Results":[99],"reveal":[100],"clear":[101],"trade-offs":[102],"interpretability,":[104],"computational":[105],"tractability,":[106],"expressiveness":[109],"across":[110],"models.":[111]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-01-17T00:00:00"}