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How Do Newcomers Blend into a Group?: Study on a Social Network Game

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Masanori Takano, Kazuya Wada, and Ichiro Fukuda, "How Do Newcomers Blend into a Group?: Study on a Social Network Game", 3rd International Workshop on Data Oriented Constructive Mining and Multi-Agent Simulation (DOCMAS) & 7th International Workshop on Emergent Intelligence on Networked Agents (WEIN) (workshop at WI-IAT 2015), 2015.

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How Do Newcomers Blend into a Group?: Study on a Social Network Game

  1. 1. CyberAgent, Inc. How Do Newcomers Blend into a Group?: Study on a Social Network Game CyberAgent. Inc, Technical Dept. Akihabara Laboratory M. Takano, K. Wada, and I. Fukuda 1 DOCMAS & WEIN 2015 Workshop @ WI-IAT
  2. 2. CyberAgent, Inc. CooperaTve Behavior Coopera've behavior requires actors' costs, but give benefits to recipients. Important factors for human society. Big Ques'on in Evolu'on. 2
  3. 3. CyberAgent, Inc. Problem of CooperaTon Mutual cooperaTon increases our benefit. Why is its evoluTon the quesTon? 3 CooperaTng each other The both get benefits Cooperate Cooperate
  4. 4. CyberAgent, Inc. Problem of CooperaTon Mutual cooperaTon increases our benefit. Why is this evoluTon the quesTon? 4 But if one defects The defector gets higher benefit than another (cooperator). Defect Cooperate
  5. 5. CyberAgent, Inc. Problem of CooperaTon Mutual cooperaTon make benefit for all. But unilateral defecTon make more benefits to defectors. → Coopera've popula'on will become defec've popula'on 5 High Benefit Low Benefit Good relaTonship, but unstable. Stable, but bad relaTonship
  6. 6. CyberAgent, Inc. Problem of CooperaTon However, humans cooperate each other Human should have goen coopera'on mechanisms during the evoluTonary process 6
  7. 7. CyberAgent, Inc. Mechanisms of CooperaTon ü Kin SelecTon ü Direct Reciprocity ü Indirect Reciprocity ü SpaTal SelecTon ü MulT-level SelecTon ref. David G Rand, et al., Human cooperaTon. Trends in cogniTve sciences, Vol. 17, No. 8, pp. 413-25, 2013. These mechanisms generate assortments between cooperators and defectors to keep interacTon among cooperators by excluding strangers. i.e., cooperaTon mechanisms exclude strangers from cooperaTve groups. 7
  8. 8. CyberAgent, Inc. Problem of CooperaTon Mechanisms ü  Kin SelecTon ü  Direct Reciprocity ü  Indirect Reciprocity ü  SpaTal SelecTon ü  MulT-level SelecTon The reciprocal mechanisms require coopera've interac'on in first 'me mee'ng, because reciprocal cooperators cooperate others as the reacTon of their cooperaTon to avoid to cooperate defectors. ü  i.e., to increase reciprocal relaTonships, in first Tme meeTng, they should cooperate (not exclude) strangers to construct good relaTonships. There are interac'on risks, because they are unfamiliar each other. → How do humans construct reciprocal rela'onships with strangers? 8
  9. 9. CyberAgent, Inc. Our Approach We approached this problem based on data analysis of a social network game. Social Network Game (SNG): ü One type of the Online Games. ü A lot of players, and their all behavior was stored as log data. ü Players cooperate and compete each other. → We can observe their social behavior in detail. 9
  10. 10. CyberAgent, Inc. Previous Studies and Our Approach 10 ParTal and Biased Data Hard to Understand Clean and Detailed Data Easy to Understand MathemaTcal Model SimulaTon Experiments in Lab ✊ ✋ Data Analysis of SNGs ObservaTon Study The data analysis of the game data catch up other approaches. ü We can observe detail behavior data of massive players like mathemaTcal models and simulaTons. ü The data is more detail than observaTon studies. ü The game environment is more open-ended than others.
  11. 11. CyberAgent, Inc. Social Network Game 11 •  URL: hp://vcard.ameba.jp •  Lang: Japanese •  Since 2012/10 We analyzed this game data for 2 weeks (2013/03/25 to 2013/04/08).
  12. 12. CyberAgent, Inc. The Minimum Set of Game Rules 12 •  Players aim to get points and to rise a ranking based on the points •  Each player belonged to a group(The group size: 1〜50 players) •  The game system support joint works in groups. •  Players get advantages when they do joint work with their group members •  A player can migrate from a group to another group at any Tme. •  Players communicate by sending simple message (30 Japanese characters). •  Messaging: no-cost and non-beneficial behavior. → We analyzed 2 types social interacTon: cooperaTon and messaging 1: Smith(12040pt) 2: MarTn(11010pt) 3: Anderson(11005pt) 4: Ken(9015pt) ・・・ Migra'on Ranking Simple messaging Coopera'on
  13. 13. CyberAgent, Inc. CooperaTve Behavior ü We focus on a specific game situaTon like Leader game ü In the SNG, players behave variously. ü We cannot track all cooperaTve behavior. → We regarded A player's this cooperaTon frequency in the SNG ≒ the player's cooperaTveness ü Payoff Matrix of the situaTon like Leader game 13 Cooperate Defect Cooperate -, - 1, 3 Defect 3, 1 0, 0 Cooperator get 1 point. Defector get 3 point.
  14. 14. CyberAgent, Inc. InteracTon of First Time MeeTng ü We try to observe the construcTon process of reciprocal relaTonships. ü In this SNG, players constructed the relaTonships. ü ref: Masanori Takano, Kazuya Wada, and Ichiro Fukuda, "Reciprocal Altruism- based CooperaTon in a Social Network Game", New GeneraTon CompuTng (in press). hp://arxiv.org/abs/1510.06197 ü We observed players' behavior at the amer migraTon ü Did the newcomers and group members cooperate each other? ü How was the difference in social interacTon between the newcomers and the group members? 14 Migra'on
  15. 15. CyberAgent, Inc. Analysis and Results 15 DOCMAS & WEIN 2015 Workshop @ WI-IAT
  16. 16. CyberAgent, Inc. Two Regression Models ü A Model for Coopera'on to others ü Were newcomers cooperaTve? ü How was coopera'on to others influenced by recipients' behavior? ü A Model for Coopera'on from others ü Were newcomers cooperated? ü How was coopera'on from others influenced by actors' behavior? 16
  17. 17. CyberAgent, Inc. NegaTve Binomial Regression Model (GLM) 17 A Model for CooperaTon to Others ü This model is intended to explain the number of coopera'on by players' experiences and aVributes Response Variable: Number of CooperaTon to Others Explanatory Variables: Experiences and Aributes Sample Size: 400,000
  18. 18. CyberAgent, Inc. NegaTve Binomial Regression Model (GLM) 18 A Model for CooperaTon to Others ü Did newcomers cooperate? f: Newcomer Flag (0 or 1) •  f=1: Newcomer (first day of migraTon) •  f=0: ExisTng Group Members If β4 > 0 then it shows that newcomers more omen cooperate than exis'ng group members. Response Variable: Number of CooperaTon to Others
  19. 19. CyberAgent, Inc. NegaTve Binomial Regression Model (GLM) 19 A Model for CooperaTon to Others ü How did newcomers react others coopera'on? C' (1-f): (Exis'ng Group Member's ) Number of CooperaTon from Others If β6 > β5 > 0 then it shows newcomers were influenced by cooperaTon from others more than exis'ng group members. C' f: (Newcomer's) Number of CooperaTon from Others Response Variable: Number of CooperaTon to Others
  20. 20. CyberAgent, Inc. NegaTve Binomial Regression Model (GLM) 20 A Model for CooperaTon to Others ü How did newcomers react others messaging? g' (1-f): (Exis'ng Group Member's ) Number of Messaging from Others If β8 > β7 > 0 then it shows newcomers were influenced by messaging from others more than exis'ng group members. g' f: (Newcomer's) Number of Messaging from Others Response Variable: Number of CooperaTon to Others
  21. 21. CyberAgent, Inc. NegaTve Binomial Regression Model (GLM) 21 A Model for CooperaTon to Others ü The others were entered as covariates to control for the other effects. The others are covariates Response Variable: Number of CooperaTon to Others
  22. 22. CyberAgent, Inc. Results – Model for CooperaTon to Others •  Newcomers tended to cooperate others without others cooperaTon and messaging. •  Newcomers omen cooperated group members. ü β4 > 0 •  In comparison between newcomers and group members ü Newcomers didn't tend to be influenced by others social behavior. ü β5 > β6 > 0, β7 > β8 > 0 22 β4 β5 β6 β7 β8
  23. 23. CyberAgent, Inc. NegaTve Binomial Regression Model (GLM) 23 A Model for CooperaTon from others ü This model is intended the number of coopera'on from others by behavior and aVributes Response Variable: Number of CooperaTon from Others Explanatory Variables: Behaviors and Aributes Sample Size: 400,000
  24. 24. CyberAgent, Inc. Response Variable: Number of CooperaTon from Others NegaTve Binomial Regression Model (GLM) 24 A Model for CooperaTon from others ü Were newcomers cooperated? f: Newcomer Flag (0 or 1) •  f=1: Newcomer (first day of migraTon) •  f=0: ExisTng Group Members If β4 > 0 then it shows that newcomers were more omen cooperated than exis'ng group members.
  25. 25. CyberAgent, Inc. NegaTve Binomial Regression Model (GLM) 25 A Model for CooperaTon from others ü How did newcomers react others coopera'on? C (1-f): (Exis'ng Group Member's) Number of CooperaTon to Others If β6 > β5 > 0 then it shows Players were more sensiTve newcomers' cooperaTon than exis'ng members' cooperaTon. C f: (Newcomer's) Number of CooperaTon to Others Response Variable: Number of CooperaTon from Others
  26. 26. CyberAgent, Inc. NegaTve Binomial Regression Model (GLM) 26 A Model for CooperaTon from others ü How did newcomers react others messaging? g (1-f): (Exis'ng Group Member's) Number of Messaging to Others If β8 > β7 > 0 then it shows Players were more sensiTve newcomers' messaging than exis'ng members' messaging. g f: (Newcomer's) Number of Messaging to Others Response Variable: Number of CooperaTon from Others
  27. 27. CyberAgent, Inc. NegaTve Binomial Regression Model (GLM) 27 A Model for CooperaTon from others ü The others were entered as covariates to control for the other factors. The others are covariates Response Variable: Number of CooperaTon from Others
  28. 28. CyberAgent, Inc. Results – Model for CooperaTon from Others ü Newcomers tended to be cooperated, newcomers messaging is important to get cooperaTon. ü  Newcomers were omen cooperated by group members. ü  β4 > 0 ü  Players were less sensiTve to newcomers' cooperaTon than exis'ng group members' cooperaTon. ü  β5 > β6 > 0 ü  Players were more sensiTve to newcomers' messages than exis'ng group members' messages. ü  β8 > β7 > 0 28 β4 β5 β6 β7 β8
  29. 29. CyberAgent, Inc. Summary ü The SNG players resolved interacTon risk in first Tme meeTng. ü In first 'me mee'ng, they o]en cooperated each other. → They may have constructed reciprocal relaTonships. ü ref. Reciprocal relaTonships in this SNG. ü Masanori Takano, Kazuya Wada, and Ichiro Fukuda, "Reciprocal Altruism-based CooperaTon in a Social Network Game", New GeneraTon CompuTng (in press). hp://arxiv.org/abs/1510.06197 29
  30. 30. CyberAgent, Inc. Summary ü The difference between newcomers and exis'ng group members in messaging ü Players were more sensiTve newcomers' messaging than exis'ng members' messaging. ü Messaging is not risky ü Messaging: no-cost and non-beneficial behavior ü In the risky situaTon (first Tme meeTng), players may have use non-risky interacTon to construct reciprocal cooperaTon. 30
  31. 31. CyberAgent, Inc. Appendix 31
  32. 32. CyberAgent, Inc. Game Rule – Raid Bale 32 ①Search Enemies Player ⑤Their point gain increase by 1.5 Tmes ⑥Ranking Group Members ②Find an Enemy  → Bale Start ③Call for Help ④Aack 1: Smith(12040pt) 2: MarTn(11010pt) 3: Anderson(11005pt) 4: Ken(9015pt) ・・・ Players bale with enemies to get event point for the ranking •  Players acquire Event Point in proporTon to their power. •  The number of aack is finite. → Players have to effec'vely get the points for the ranking.
  33. 33. CyberAgent, Inc. Test Scenario 33 Aack Wait Aack - 1, 3 Wait 3, 1 0, 0 To simplify this, consider that two players baled the enemy A player wait another's aack to use effecTvely their resource. We regarded the AVack behavior as coopera'on. When the enemy's hit points are very few Aack HP Players acquire smaller points when "Aack power > Enemy's HP" than when "Aack power ≦ Enemy's HP"
  34. 34. CyberAgent, Inc. Assortment Cooperators and Defectors Density distribu'on of the propor'on of cooperators in each group. 34 0.0 0.1 0.2 0.3 0.00 0.25 0.50 0.75 1.00 Proportion of Cooperators Density Masanori Takano, Kazuya Wada, and Ichiro Fukuda, "Environmentally Driven MigraTon in a Social Network Game", ScienTfic Reports, 5, 12481; doi: 10.1038/srep12481 (2015).

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