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新参者は如何にして新たなグループになじむのか? ソーシャルゲームにおける分析事例 | WEBDB Forum 2015

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WEBDB Forum 2015 の技術報告セッション 発表資料

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新参者は如何にして新たなグループになじむのか? ソーシャルゲームにおける分析事例 | WEBDB Forum 2015

  1. 1. CyberAgent, Inc. 新参者は如何にして新たな グループになじむのか? ソーシャルゲームにおける分析事例 株式会社 サイバーエージェント 技術本部 秋葉原ラボ 高野雅典, 和田計也, 福田一郎 1 WEBDB Forum 2015
  2. 2. CyberAgent, Inc. 2
  3. 3. CyberAgent, Inc. 技術本部 秋葉原ラボ ミッション: 大規模データ処理、機械学習、検索、統計解析などの技術を 用いた(主に)自社サービスの発展 対象: スマフォゲーム、音楽/動画関連サービス、SNS、広告など全社横断 3 •  大規模データを集約し活かすための組織 •  2011/4に開設、現在27名が所属 1.  PDCAサイクルのための KPIの設計・集計・可視化 2.  ユーザ体験向上のための 推薦・検索・分類などの機能提供 3.  サービス健全化のための スパム検知・フィルタリング 4.  課題発見・解決のための 統計解析
  4. 4. CyberAgent, Inc. サイバーエージェント 技術本部 秋葉原ラボ 4 国内口頭発表は開設当初 から継続的に実施 査読付き論文や書籍・学会誌への 執筆・寄稿も増加中!! •  対外発表も(全社的に)推奨されている – 博士号所持者・在学者も増加傾向 ⤴ –  秋葉原ラボの発表一覧: hEp://www.cyberagent.co.jp/techinfo/labo/research_list/ ※ 2015/11 現在
  5. 5. CyberAgent, Inc. 新参者は如何にして新たな グループになじむのか? ソーシャルゲームにおける分析事例 株式会社 サイバーエージェント 技術本部 秋葉原ラボ 高野雅典, 和田計也, 福田一郎 5 WEBDB Forum 2015 本題
  6. 6. CyberAgent, Inc. CooperaOve Behavior Coopera(ve behavior costs actors, but benefits recipients. Important factors for human society. Big Ques(on in Evolu(on. 6
  7. 7. CyberAgent, Inc. Paradox of CooperaOon Mutual cooperaOon increases our benefit. Why is it the quesOon? 7 CooperaOng each other The both get benefits
  8. 8. CyberAgent, Inc. Paradox of CooperaOon Mutual cooperaOon increases our benefit. Why is it the quesOon? 8 But if one defects The defector gets higher benefit than another (cooperator). Defect Cooperate
  9. 9. CyberAgent, Inc. Paradox of CooperaOon Mutual cooperaOon make benefit for all. But one-sided defecOon make more benefits to defectors. → Coopera(ve popula(on will become defec(ve popula(on 9 High Benefit Low Benefit Good relaOonship, but unstable. Stable, but bad relaOonship
  10. 10. CyberAgent, Inc. Paradox of CooperaOon However, we cooperate each other Human should have goEen coopera(on mechanisms during the evoluOonary process 10
  11. 11. CyberAgent, Inc. Mechanisms of CooperaOon •  Kin SelecOon •  Direct Reciprocity •  Indirect Reciprocity •  SpaOal SelecOon •  MulO-level SelecOon ref. David G Rand, et al., Human cooperaOon. Trends in cogniOve sciences, Vol. 17, No. 8, pp. 413-25, 2013. These mechanisms generate assortments between fellows and strangers to keep interacOon between cooperators. •  i.e., cooperaOon mechanisms exclude strangers from cooperaOve groups. 11
  12. 12. CyberAgent, Inc. Problem of CooperaOon Mechanisms •  Kin SelecOon •  Direct Reciprocity •  Indirect Reciprocity •  SpaOal SelecOon •  MulO-level SelecOon The reciprocal mechanisms require coopera(ve interac(on in first (me mee(ng, because reciprocal cooperators cooperate others who have cooperated them to avoid to cooperate defectors. •  i.e., to increase reciprocal relaOonships, in first Ome meeOng, they should cooperate (not exclude) strangers to construct good relaOonships. There are interac(on risks, because they are unfamiliar each other. → How do humans take in strangers? 12
  13. 13. CyberAgent, Inc. Our Approach We approached this problem based on data analysis of a social network game. Social Network Game (SNG): •  One type of the Online Games. •  A lot of players. •  Players belong to groups (tens of players), they cooperate in the groups, and compete with all others. 13
  14. 14. CyberAgent, Inc. Previous Studies and Our Approach 14 ParOal and Biased Data Hard to Understand Clean and Detailed Data Easy to Understand MathemaOcal Model SimulaOon Experiments in Lab ✊ ✋ Data Analysis of SNGs ObservaOon Study SNGs is more open-ended than simulaOons and lab experiments, and we can get all players' behavior logs → We may expect to find new evidences for human sociality.
  15. 15. CyberAgent, Inc. Social Network Game 15 URL: hEp://vcard.ameba.jp Lang: Japanese Since 2012/10
  16. 16. CyberAgent, Inc. The Game Rules 16 •  Players aim to get points and to rise a ranking based on the points •  Players belonged to groups •  The group size: 1〜50 players •  Players cooperate each group member to get advantages in the game. •  A player can migrate from a group to another group at any Ome. •  Players communicate by sending simple message (30 Japanese characters). 1: Smith(12040pt) 2: MarOn(11010pt) 3: Anderson(11005pt) 4: Ken(9015pt) ・・・ Migra(on Ranking Simple messaging Coopera(on
  17. 17. CyberAgent, Inc. CooperaOve Behavior •  We focus on a game situaOon like Leader game – In the SNG, players behave variously. – We cannot track all cooperaOve behavior. → We regarded A player's this cooperaOon frequency in the SNG ≒ the player's cooperaOveness •  Payoff Matrix of the situaOon like Leader game 17 Cooperate Defect Cooperate -, - 1, 3 Defect 3, 1 0, 0 Cooperator get 1 point. Defector get 3 point.
  18. 18. CyberAgent, Inc. InteracOon of First Time MeeOng •  A player can migrate from a group to another group at any Ome. •  We observed players' behavior at the ajer migraOon –  Did the newcomers blend into a new group members? –  How did the newcomers interact with the the group members? –  How did the newcomers react to the group members' behavior? •  In our previous works, we showed –  Cooperators constructed reciprocal relaOonships in their groups. •  Masanori Takano, Kazuya Wada, and Ichiro Fukuda, "Reciprocal Altruism-based CooperaOon in a Social Network Game", New GeneraOon CompuOng (in press). hEp://arxiv.org/abs/1510.06197 18
  19. 19. CyberAgent, Inc. Results 19 WEBDB Forum 2015
  20. 20. CyberAgent, Inc. Two Regression Models •  A Model for Coopera(ve Behavior – Were newcomers cooperaOve? – How were newcomers influenced by group members' behavior? •  A Model for the Receipt of Coopera(on – Were newcomers cooperated? – How were players influence by newcomers' behavior? 20
  21. 21. CyberAgent, Inc. NegaOve Binomial Regression Model (GLM) 21 A Model for CooperaOve Behavior •  This model is intended to explain the number of coopera(on by players' experiences and aUributes Response Variable: Number of CooperaOon Explanatory Variables: Experiences and AEributes
  22. 22. CyberAgent, Inc. NegaOve Binomial Regression Model (GLM) 22 A Model for CooperaOve Behavior •  Did newcomers cooperate? Response Variable: Number of CooperaOon f: Flag (0 or 1) of Ajer MigraOon (Newcomer Flag) •  f=1: Newcomer •  f=0: ExisOng Group Members If β4 > 0 then it shows that newcomers more ojen cooperate than exis(ng group members.
  23. 23. CyberAgent, Inc. NegaOve Binomial Regression Model (GLM) 23 A Model for CooperaOve Behavior •  How did newcomers react others messaging? Response Variable: Number of CooperaOon C' (1-f): Number of Exis(ng Group Member's Receipt of CooperaOon If β6 > β5 > 0 then it shows newcomers more suscepOble to others cooperaOon than exis(ng group members. C' f: Number of Newcomer's Receipt of CooperaOon
  24. 24. CyberAgent, Inc. NegaOve Binomial Regression Model (GLM) 24 A Model for CooperaOve Behavior •  How did newcomers react others coopera(on? Response Variable: Number of CooperaOon C' (1-f): Number of Exis(ng Group Member's Receipt of Messaging If β8 > β7 > 0 then it shows newcomers more suscepOble to others messaging than exis(ng group members. C' f: Number of Newcomer's Receipt of Messaging
  25. 25. CyberAgent, Inc. NegaOve Binomial Regression Model (GLM) 25 A Model for CooperaOve Behavior •  The others were entered as covariates to control for the other factors. Response Variable: Number of CooperaOon The others are covariates
  26. 26. CyberAgent, Inc. Results •  β4 > 0 –  Newcomers ojen cooperated group members. •  β5 > β6 > 0, β7 > β8 > 0 –  Newcomers were less suscepOble to social interacOon. •  Newcomers tended to cooperate others without others cooperaOon and messaging. 26 β4 β5 β6 β7 β8
  27. 27. CyberAgent, Inc. NegaOve Binomial Regression Model (GLM) 27 A Model for the Receipt of CooperaOon •  This model is intended the number of the receipt of coopera(on by other behavior and aUributes Response Variable: Number of the Receipt of CooperaOon Explanatory Variables: Other Behaviors and AEributes
  28. 28. CyberAgent, Inc. Response Variable: Number of the Receipt of CooperaOon NegaOve Binomial Regression Model (GLM) 28 A Model for the Receipt of CooperaOon •  Were newcomers cooperated? f: Flag (0 or 1) of Ajer MigraOon (Newcomer Flag) •  f=1: Newcomer •  f=0: ExisOng Group Members If β4 > 0 then it shows that newcomers more ojen receive cooperaOon than exis(ng group members.
  29. 29. CyberAgent, Inc. NegaOve Binomial Regression Model (GLM) 29 A Model for the Receipt of CooperaOon •  How did newcomers react others coopera(on? C (1-f): Number of Exis(ng Group Member's CooperaOon If β6 > β5 > 0 then it shows newcomers were more sensiOve to others' cooperaOon than exis(ng group members. C f: Number of Newcomer's CooperaOon Response Variable: Number of the Receipt of CooperaOon
  30. 30. CyberAgent, Inc. NegaOve Binomial Regression Model (GLM) 30 A Model for the Receipt of CooperaOon •  How did newcomers react others messaging? C' (1-f): Number of Exis(ng Group Member's Messaging If β8 > β7 > 0 then it shows newcomers were more sensiOve to others' messaging than exis(ng group members. C' f: Number of Newcomer's Messaging Response Variable: Number of the Receipt of CooperaOon
  31. 31. CyberAgent, Inc. NegaOve Binomial Regression Model (GLM) 31 A Model for the Receipt of CooperaOon •  The others were entered as covariates to control for the other factors. The others are covariates Response Variable: Number of the Receipt of CooperaOon
  32. 32. CyberAgent, Inc. Results •  β4 > 0 –  Newcomers were ojen cooperated by group members. •  β5 > β6 > 0 –  Players were less sensiOve to newcomers' cooperaOon than exis(ng group members' cooperaOon. •  β8 > β7 > 0 •  Players were more sensiOve to newcomers' messages than exis(ng group members' messages. 32 β4 β5 β6 β7 β8
  33. 33. CyberAgent, Inc. Summary •  The SNG players resolved interacOon risk in first Ome meeOng. – In first (me mee(ng, they oen cooperated each other. → They may have constructed reciprocal relaOonships. – ref. Reciprocal relaOonships in this SNG. •  Masanori Takano, Kazuya Wada, and Ichiro Fukuda, "Reciprocal Altruism-based CooperaOon in a Social Network Game", New GeneraOon CompuOng (in press). hEp://arxiv.org/abs/1510.06197 33
  34. 34. CyberAgent, Inc. Summary •  The difference between newcomers and exis(ng group members in messaging – Players were more sensiOve newcomers' messaging than exis(ng members' messaging. – The messaging may have worked as social grooming •  Social grooming is tool to make and maintain social relaOonships. •  It includes cooperaOon, unproducOve conversaOons (gossips), and various social behaviors. Especially in first (me mee(ng, social grooming was important to resolve the risk in first (me mee(ng. 34

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