{"id":"https://openalex.org/W2975995832","doi":"https://doi.org/10.1109/cig.2019.8848021","title":"Monte Carlo Strategies for Exploiting Fairness in N-player Ultimatum Games","display_name":"Monte Carlo Strategies for Exploiting Fairness in N-player Ultimatum Games","publication_year":2019,"publication_date":"2019-08-01","ids":{"openalex":"https://openalex.org/W2975995832","doi":"https://doi.org/10.1109/cig.2019.8848021","mag":"2975995832"},"language":"en","primary_location":{"id":"doi:10.1109/cig.2019.8848021","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cig.2019.8848021","pdf_url":null,"source":{"id":"https://openalex.org/S4306498491","display_name":"2019 IEEE Conference on Games (CoG)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Conference on Games (CoG)","raw_type":"proceedings-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/A5111498372","display_name":"Garrison W. Greenwood","orcid":null},"institutions":[{"id":"https://openalex.org/I126345244","display_name":"Portland State University","ror":"https://ror.org/00yn2fy02","country_code":"US","type":"education","lineage":["https://openalex.org/I126345244"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Garrison W. Greenwood","raw_affiliation_strings":["Dept. of Electrical & Computer Engineering, Portland State University, Portland, OR, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of Electrical & Computer Engineering, Portland State University, Portland, OR, USA","institution_ids":["https://openalex.org/I126345244"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080870632","display_name":"Daniel Ashlock","orcid":"https://orcid.org/0000-0003-2209-7504"},"institutions":[{"id":"https://openalex.org/I79817857","display_name":"University of Guelph","ror":"https://ror.org/01r7awg59","country_code":"CA","type":"education","lineage":["https://openalex.org/I79817857"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Daniel Ashlock","raw_affiliation_strings":["Dept. of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada"],"affiliations":[{"raw_affiliation_string":"Dept. of Mathematics and Statistics, University of Guelph, Guelph, Ontario, Canada","institution_ids":["https://openalex.org/I79817857"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5111498372"],"corresponding_institution_ids":["https://openalex.org/I126345244"],"apc_list":null,"apc_paid":null,"fwci":0.133,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.50396758,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11574","display_name":"Artificial Intelligence in Games","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T11574","display_name":"Artificial Intelligence in Games","score":0.9998000264167786,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9984999895095825,"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/T11197","display_name":"Digital Games and Media","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/3312","display_name":"Sociology and Political Science"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ultimatum-game","display_name":"Ultimatum game","score":0.8753588199615479},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6749171018600464},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.6376438140869141},{"id":"https://openalex.org/keywords/game-theory","display_name":"Game theory","score":0.4707014560699463},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.3778269290924072},{"id":"https://openalex.org/keywords/mathematical-economics","display_name":"Mathematical economics","score":0.34140002727508545},{"id":"https://openalex.org/keywords/economics","display_name":"Economics","score":0.14477947354316711},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1318468153476715},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09413769841194153}],"concepts":[{"id":"https://openalex.org/C59482028","wikidata":"https://www.wikidata.org/wiki/Q2295916","display_name":"Ultimatum game","level":2,"score":0.8753588199615479},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6749171018600464},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.6376438140869141},{"id":"https://openalex.org/C177142836","wikidata":"https://www.wikidata.org/wiki/Q44455","display_name":"Game theory","level":2,"score":0.4707014560699463},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.3778269290924072},{"id":"https://openalex.org/C144237770","wikidata":"https://www.wikidata.org/wiki/Q747534","display_name":"Mathematical economics","level":1,"score":0.34140002727508545},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.14477947354316711},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1318468153476715},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09413769841194153}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/cig.2019.8848021","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cig.2019.8848021","pdf_url":null,"source":{"id":"https://openalex.org/S4306498491","display_name":"2019 IEEE Conference on Games (CoG)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Conference on Games (CoG)","raw_type":"proceedings-article"},{"id":"pmh:oai:pdxscholar.library.pdx.edu:ece_fac-1587","is_oa":false,"landing_page_url":"https://pdxscholar.library.pdx.edu/ece_fac/581","pdf_url":null,"source":{"id":"https://openalex.org/S4377196300","display_name":"PDXScholar  (Portland State University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I126345244","host_organization_name":"Portland State University","host_organization_lineage":["https://openalex.org/I126345244"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Electrical and Computer Engineering Faculty Publications and Presentations","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1625390266","https://openalex.org/W1713503745","https://openalex.org/W1742378130","https://openalex.org/W1881419322","https://openalex.org/W2027020357","https://openalex.org/W2045744884","https://openalex.org/W2108794978","https://openalex.org/W2121863487","https://openalex.org/W2122253967","https://openalex.org/W2126316555","https://openalex.org/W2204079010","https://openalex.org/W2896356891","https://openalex.org/W4214717370","https://openalex.org/W4300077424","https://openalex.org/W6687944646"],"related_works":["https://openalex.org/W2165587830","https://openalex.org/W2125264022","https://openalex.org/W2590978407","https://openalex.org/W600636080","https://openalex.org/W3161993714","https://openalex.org/W2665111804","https://openalex.org/W1887395321","https://openalex.org/W4220815008","https://openalex.org/W2140017412","https://openalex.org/W2734389049"],"abstract_inverted_index":{"The":[0,43,210],"Ultimatum":[1],"Game":[2],"(UG)":[3],"is":[4,34,55,79,89,96,104,153,173],"studied":[5],"to":[6,38,105,147,155,199],"see":[7,106],"how":[8,37],"people":[9],"respond":[10],"in":[11,160],"bargaining":[12],"situations.":[13],"In":[14,98,140],"the":[15,49,53,70,92,99,102,108,156,161,165,181,208,225,241],"2-player":[16],"version":[17,101],"each":[18],"round":[19],"a":[20,24,27,30,40,84,112,127,232],"player":[21,152,172,216],"can":[22,110,131,217],"be":[23,218],"proposer":[25,31],"or":[26,47],"responder.":[28],"As":[29],"an":[32,145],"offer":[33,95,205],"made":[35],"on":[36,117],"split":[39,56],"monetary":[41],"amount.":[42],"responder":[44],"either":[45],"accepts":[46],"rejects":[48],"offer.":[50],"If":[51],"accepted,":[52],"money":[54],"as":[57,219,221],"proposed;":[58],"if":[59,107],"rejected":[60],"both":[61],"players":[62,183],"get":[63],"nothing.":[64],"Studies":[65],"have":[66,123],"found":[67],"over":[68],"time":[69],"offers":[71,119,133],"decrease":[72],"but":[73],"are":[74,120],"still":[75],"accepted":[76],"(getting":[77],"something":[78],"better":[80],"than":[81,224],"nothing)":[82],"until":[83],"subgame":[85],"perfect":[86],"Nash":[87],"equilibrium":[88],"reached":[90],"where":[91],"lowest":[93],"possible":[94],"accepted.":[97,121],"N-player":[100],"object":[103],"population":[109,157,226],"reach":[111],"state":[113],"of":[114,193,243],"fairness":[115,177],"where,":[116],"average,":[118],"We":[122,188],"previously":[124],"shown":[125],"that":[126,137],"(\u00b5/\u00b5,\u03bb)":[128],"evolution":[129],"strategy":[130],"evolve":[132],"and":[134,178],"acceptance":[135],"thresholds":[136],"promote":[138],"fairness.":[139],"this":[141,148,170,202,215],"paper":[142],"we":[143],"report":[144],"extension":[146],"previous":[149],"work.":[150],"One":[151],"added":[154],"who":[158],"interacts":[159],"same":[162],"manner":[163],"with":[164],"other":[166,182],"N":[167],"players.":[168],"However,":[169],"new":[171],"rational\u2014i.e.,":[174],"he":[175],"ignores":[176],"instead":[179],"exploits":[180],"by":[184],"maximizing":[185],"his":[186],"payoffs.":[187],"used":[189],"three":[190],"different":[191],"versions":[192],"Monte":[194],"Carlo":[195],"Tree":[196],"Search":[197],"(MCTS)":[198],"adaptively":[200],"control":[201],"rational":[203],"player\u2019s":[204],"levels":[206],"during":[207],"game.":[209],"results":[211],"indicate":[212],"payoffs":[213],"for":[214,240],"much":[220],"40%":[222],"higher":[223],"average":[227],"payoff.":[228],"Our":[229],"MCTS":[230],"introduces":[231],"novel":[233],"rollout":[234],"approach":[235],"making":[236],"it":[237],"ideally":[238],"suited":[239],"play":[242],"mathematical":[244],"games.":[245]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-10-10T00:00:00"}
