{"id":"https://openalex.org/W2904818558","doi":"https://doi.org/10.1609/aaai.v33i01.33012173","title":"Learning Deviation Payoffs in Simulation-Based Games","display_name":"Learning Deviation Payoffs in Simulation-Based Games","publication_year":2019,"publication_date":"2019-07-17","ids":{"openalex":"https://openalex.org/W2904818558","doi":"https://doi.org/10.1609/aaai.v33i01.33012173","mag":"2904818558"},"language":"en","primary_location":{"id":"doi:10.1609/aaai.v33i01.33012173","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33012173","pdf_url":"https://www.aaai.org/ojs/index.php/AAAI/article/download/4050/3928","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.aaai.org/ojs/index.php/AAAI/article/download/4050/3928","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5059261578","display_name":"Samuel Sokota","orcid":null},"institutions":[{"id":"https://openalex.org/I118020396","display_name":"Swarthmore College","ror":"https://ror.org/012dg8a96","country_code":"US","type":"education","lineage":["https://openalex.org/I118020396"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Samuel Sokota","raw_affiliation_strings":["Swarthmore College"],"affiliations":[{"raw_affiliation_string":"Swarthmore College","institution_ids":["https://openalex.org/I118020396"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019035605","display_name":"Caleb Ho","orcid":null},"institutions":[{"id":"https://openalex.org/I118020396","display_name":"Swarthmore College","ror":"https://ror.org/012dg8a96","country_code":"US","type":"education","lineage":["https://openalex.org/I118020396"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Caleb Ho","raw_affiliation_strings":["Swarthmore College"],"affiliations":[{"raw_affiliation_string":"Swarthmore College","institution_ids":["https://openalex.org/I118020396"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060948457","display_name":"Bryce Wiedenbeck","orcid":null},"institutions":[{"id":"https://openalex.org/I118020396","display_name":"Swarthmore College","ror":"https://ror.org/012dg8a96","country_code":"US","type":"education","lineage":["https://openalex.org/I118020396"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bryce Wiedenbeck","raw_affiliation_strings":["Swarthmore College"],"affiliations":[{"raw_affiliation_string":"Swarthmore College","institution_ids":["https://openalex.org/I118020396"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5059261578"],"corresponding_institution_ids":["https://openalex.org/I118020396"],"apc_list":null,"apc_paid":null,"fwci":9.0698,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.97567288,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"33","issue":"01","first_page":"2173","last_page":"2180"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11674","display_name":"Sports Analytics and Performance","score":0.9932000041007996,"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/T11674","display_name":"Sports Analytics and Performance","score":0.9932000041007996,"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/T11031","display_name":"Game Theory and Applications","score":0.98580002784729,"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":0.9836999773979187,"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/stochastic-game","display_name":"Stochastic game","score":0.7083388566970825},{"id":"https://openalex.org/keywords/nash-equilibrium","display_name":"Nash equilibrium","score":0.6765549778938293},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.640619158744812},{"id":"https://openalex.org/keywords/fraction","display_name":"Fraction (chemistry)","score":0.48167455196380615},{"id":"https://openalex.org/keywords/fictitious-play","display_name":"Fictitious play","score":0.45053714513778687},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.4437030553817749},{"id":"https://openalex.org/keywords/strategy","display_name":"Strategy","score":0.430765300989151},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.417178750038147},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.41429033875465393},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.324570894241333},{"id":"https://openalex.org/keywords/game-theory","display_name":"Game theory","score":0.2746322751045227},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24882018566131592},{"id":"https://openalex.org/keywords/mathematical-economics","display_name":"Mathematical economics","score":0.23627817630767822}],"concepts":[{"id":"https://openalex.org/C22171661","wikidata":"https://www.wikidata.org/wiki/Q1074380","display_name":"Stochastic game","level":2,"score":0.7083388566970825},{"id":"https://openalex.org/C46814582","wikidata":"https://www.wikidata.org/wiki/Q23389","display_name":"Nash equilibrium","level":2,"score":0.6765549778938293},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.640619158744812},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.48167455196380615},{"id":"https://openalex.org/C145071142","wikidata":"https://www.wikidata.org/wiki/Q1411116","display_name":"Fictitious play","level":3,"score":0.45053714513778687},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4437030553817749},{"id":"https://openalex.org/C88959737","wikidata":"https://www.wikidata.org/wiki/Q1546627","display_name":"Strategy","level":3,"score":0.430765300989151},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.417178750038147},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.41429033875465393},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.324570894241333},{"id":"https://openalex.org/C177142836","wikidata":"https://www.wikidata.org/wiki/Q44455","display_name":"Game theory","level":2,"score":0.2746322751045227},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24882018566131592},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.23627817630767822},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v33i01.33012173","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33012173","pdf_url":"https://www.aaai.org/ojs/index.php/AAAI/article/download/4050/3928","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v33i01.33012173","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v33i01.33012173","pdf_url":"https://www.aaai.org/ojs/index.php/AAAI/article/download/4050/3928","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2904818558.pdf","grobid_xml":"https://content.openalex.org/works/W2904818558.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W16124843","https://openalex.org/W129412289","https://openalex.org/W1517289518","https://openalex.org/W1577821753","https://openalex.org/W1807347157","https://openalex.org/W1987961481","https://openalex.org/W1988664393","https://openalex.org/W1996251656","https://openalex.org/W2028798910","https://openalex.org/W2035681301","https://openalex.org/W2043446118","https://openalex.org/W2144274908","https://openalex.org/W2149086825","https://openalex.org/W2152078617","https://openalex.org/W2154534427","https://openalex.org/W2546528467","https://openalex.org/W2574978968","https://openalex.org/W2594794854","https://openalex.org/W2604384918","https://openalex.org/W2624042483","https://openalex.org/W2766447205","https://openalex.org/W2789230735","https://openalex.org/W2791379113","https://openalex.org/W2964345382","https://openalex.org/W3024712393","https://openalex.org/W4285719527","https://openalex.org/W4297789683","https://openalex.org/W4297798105","https://openalex.org/W6605285891","https://openalex.org/W6628424236","https://openalex.org/W6647334321","https://openalex.org/W6659529313","https://openalex.org/W6681187623","https://openalex.org/W6681884964"],"related_works":["https://openalex.org/W4287644830","https://openalex.org/W3092557414","https://openalex.org/W4214835929","https://openalex.org/W4302081789","https://openalex.org/W4226086326","https://openalex.org/W1518504063","https://openalex.org/W2963864634","https://openalex.org/W2890426576","https://openalex.org/W1991899349","https://openalex.org/W4238645493"],"abstract_inverted_index":{"We":[0,61,109],"present":[1],"a":[2,22,46,50,63],"novel":[3],"approach":[4],"for":[5,65],"identifying":[6],"approximate":[7,118],"role-symmetric":[8],"Nash":[9,83],"equilibria":[10,102,119],"in":[11,77],"large":[12,58],"simulation-based":[13],"games.":[14,60],"Our":[15],"method":[16],"uses":[17],"neural":[18],"networks":[19],"to":[20,27,86],"learn":[21],"mapping":[23],"from":[24,36,43],"mixed-strategy":[25],"profiles":[26],"deviation":[28,93,113],"payoffs\u2014the":[29],"expected":[30],"values":[31],"of":[32,49,56,80,90,100],"playing":[33],"pure-strategy":[34],"deviations":[35],"those":[37],"profiles.":[38],"This":[39],"learning":[40,95,115],"can":[41,124],"generalize":[42],"data":[44,73],"about":[45],"tiny":[47],"fraction":[48],"game\u2019s":[51],"outcomes,":[52],"permitting":[53],"tractable":[54],"analysis":[55],"exponentially":[57],"normal-form":[59],"give":[62],"procedure":[64],"iteratively":[66],"refining":[67],"the":[68,78,87,91,98],"learned":[69],"model":[70],"with":[71,131],"new":[72],"produced":[74],"by":[75],"sampling":[76],"neighborhood":[79],"each":[81],"candidate":[82],"equilibrium.":[84],"Relative":[85],"existing":[88],"state":[89],"art,":[92],"payoff":[94,114],"dramatically":[96],"simplifies":[97],"task":[99],"computing":[101],"and":[103,123,136],"more":[104,126,133],"effectively":[105],"addresses":[106],"player":[107],"asymmetries.":[108],"demonstrate":[110],"empirically":[111],"that":[112],"identifies":[116],"better":[117],"than":[120],"previous":[121],"methods":[122],"handle":[125],"difficult":[127],"settings,":[128],"including":[129],"games":[130],"many":[132],"players,":[134],"strategies,":[135],"roles.":[137]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
