- CyberAgent's Akihabara Lab analyzes large-scale data from social games and services to improve user experience and service quality. - The lab studied how newcomers integrate into social groups in a social network game. Regression models analyzed newcomers' cooperative behavior and how much cooperation they received. - Results showed that newcomers often cooperated with group members without needing reciprocation first, and were more likely to receive cooperation than existing members, suggesting humans readily accept strangers into cooperative relationships.

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. CyberAgent, Inc.
2

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. 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. 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. 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. CyberAgent, Inc.
Paradox of CooperaOon
Mutual cooperaOon increases our benefit.
Why is it the quesOon?
7
CooperaOng each other
The both get benefits

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. 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. CyberAgent, Inc.
Paradox of CooperaOon
However,
we cooperate each other
Human should have goEen
coopera(on mechanisms
during the evoluOonary
process 10

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. 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. 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. 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. CyberAgent, Inc.
Social Network Game
15
URL: hEp://vcard.ameba.jp
Lang: Japanese
Since 2012/10

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. 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. 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. CyberAgent, Inc.
Results
19
WEBDB Forum 2015

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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