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Collaboration Models Lingjie Duan, Takeshi Kubo, Kohei Sugiyama, - PowerPoint PPT Presentation

Collaboration Models Lingjie Duan, Takeshi Kubo, Kohei Sugiyama, Jianwei Huang, Teruyuki Hasegawa, Jean Walrand Singapore SUTD, KDDI, CHUK, UC Berkeley DIMACS Workshop on the Economic Aspects of information Sharing 2/7/13 Overview Data


  1. Collaboration Models Lingjie Duan, Takeshi Kubo, Kohei Sugiyama, Jianwei Huang, Teruyuki Hasegawa, Jean Walrand Singapore SUTD, KDDI, CHUK, UC Berkeley DIMACS Workshop on the Economic Aspects of information Sharing – 2/7/13

  2. Overview  Data collection & cooperation on tasks  Incentives for collaboration?  We model cooperation in two situations:  Model 1: “Data Collection Game” : share reward if successful  Model 2: “Task collaboration”: contract 2

  3. Model 1 Value V Client If n ≥ n 0 collaborations C j C 1 C N Collaboration costs: iid, F(.) known N known 3

  4. Model 1 Value V Client If n ≥ n 0 collaborations Collaboration costs C j C 1 C N Scheme: Client proposes a reward R R is divided among collaborators, if n ≥ n 0 Questions: Whether to collaborate? How to choose R? 4

  5. Scheme: Client proposes a reward R Model 1 R is divided among collaborators Value V Client If n ≥ n 0 collaborations Collaboration costs C j C 1 C N Nash equilibrium: Collaborate if C i ≤ γ γ = unique solution of = 0 where m = B ( N, F ( γ )) 5

  6. Scheme: Client proposes a reward R Model 1 R is divided among collaborators N = 100 n 0 = 40 C i = U[0, 4] E.g., R = 100 →γ = 2 note that E[n] = 50 6

  7. Scheme: Client proposes a reward R Model 1 R is divided among collaborators Choosing R: where n = B ( N, F ( γ ∗ ( R ))) 7

  8. Scheme: Client proposes a reward R Model 1 R is divided among collaborators Choosing R: V = 100 n 0 = 30 C i = U[0, 3] 8

  9. Model 2 Client K j K 1 K N Collaboration costs, per unit of effort Type i: Reward r Effort t Client’s utility: 9

  10. Model 2 Client Proposes contract K j K 1 K N Selects j : I will produce t m units of effort for reward r m 10

  11. Model 2 Client N users, each user has type i w.p. q i Algorithm for optimal design of contract 11

  12. Model 2 N = 120 3 types q i = 1/3 θ i = 5 12

  13. Summary Design of collaborations 1) “Data Collection” Share R if n ≥ n 0  V for client Collaborate if cost ≤ γ * Calculate optimal R 2) “Collaboration on task” User of type i w.p. q i Design of optimal contract 13

  14. This presentation is based on the following paper: Lingjie Duan, Takeshi Kubo, Kohei Sugiyama, Jianwei Huang, Teruyuki Hasegawa, Jean Walrand, “Incentive Mechanisms for Smartphone Collaboration in Data Acquisition and Distributed Computing,” INFOCOM 201 2. 14

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