Co- opetition in Network Tasks

1. Co-opetition in Network Tasks YoramBachrach, Peter Key, Jeff Rosenschein, MortezaZadimoghaddam, Ely Porat

2. Agenda

3. Negotiation

4. Negotiation “Collective Buying Power” Quota: 100 Buyers Reward: Discount of $10 (total saving 10*100=$1000) 50 Users 25 Users 70 Users 30 Users

5. Transferable Utility Games • Agents: • Coalition: • Characteristic function: • Simple coalitional games: • Win or Lose • Agreements (imputations): • A payoff vector • Efficiency: • Coalition’s payoff:

6. Solution Concepts GamE Imputation

7. Solution Concepts GamE Imputation

8. Solution Concepts GamE Imputation [

9. Solution Concepts GamE Imputation

10. Solving the Groupon Game Required: 100 Users • Average contribution across all permutations 50 Users 25 Users 70 Users 30 Users

11. Solving the Groupon Game Required: 100 Users • Average contribution across all permutations 50 Users 15 Users 70 Users 30 Users

12. Solving the Groupon Game Required: 100 Users • Core: no deviations • Cannot win without the 70 users 50 Users 15 Users 70 Users 30 Users

13. Display Advertising

14. Sponsored Search Advertising

15. Social Network Advertising

16. Social Advertising In Groupon

17. Connectivity Games t s

18. Connectivity Games Coalition t s

19. Connectivity Games Coalition t s

20. Connectivity Games Coalition t s

21. Connectivity Games Coalition t s

22. Richer Model p b p p

23. Network Reliability p b p p

24. Connectivity Games • Agents are vertices in a graph • Vertices are either primaryor backbone • wins if it connects all primary vertices • Using the graph induced by • Extension of single source-target vertices • Advertise to target audience • Allow reliable network communication p b p p

25. Example Network (1)

26. Example Network (2)

27. Hotspots and Bargaining • Fair payment for advertising? • Power indices reflect contribution • Probabilistic assumptions • Target vertex survives, other vertices fail with probability • Bargaining power • Core reflects stable agreements • Alternative coalitions and agreements • Empty unless veto vertices exist • Relaxation:

28. Computational Limitations

29. Network Security • Physical networks • Placing checkpoints • Locations for routine checks • Computer networks • Protecting servers and links from attacks • Various costs for different nodes and links • How easy it is to deploy a check point • Performance degradation for protected servers • What agreements would be reached regarding related budgets and rewards?

30. Security Crowdsourcing • Texas Virtual Boarder Watch • Individuals observe US-Mexico border for suspicious behavior

31. Blocking an adversary s t

32. Blocking an adversary s t

33. Blocking an adversary s t

34. Blocking an adversary s t

35. Blocking an adversary s t

36. Blocking an adversary s t

37. Blocking an adversary s t

38. Incorporating costs 3 1 2 s 2 t 8 5 2 3 7 2

39. Incorporating costs 3 1 2 s 2 t 8 5 2 3 7 2

40. Multiple Adversaries 3 t2 2 s1 2 t1 8 5 2 s2 7 2

41. Coalitions in Network Security • Agents must for coalitions to successfully block the adversary • How should they split costs and rewards? • Security resources are limited • Which node should be allocated these resources first? • Similar tools from Game Theory 3 1 2 s 2 t 8 5 2 3 7 2

42. Path Disruption Games • Games played on a graph G=<V,E> (a network) • Simple version (PDGs): coalition wins if it can block the adversary and loses otherwise • Model with costs (PDGCs): a coalition is guaranteed a reward r for blocking the adversary, but incurs the cost of its checkpoints

43. Computational Limitations

44. Related Models • Network Flow Games • C’s value: the maximal flow it can send between s and t • Collusion in network auctions • Procurer buys a path from s to t in an auction • C’s value: obtained price when rigging the auction

45. Conclusions 3 1 2 s p b 2 t p 8 5 2 3 7 2 p