Over-contribution in discretionary databases

Transcription

1 Over-contribution in discretionary databases Mike Klaas Faculty of Computer Science University of British Columbia

2 Outline Over-contribution in discretionary databases p.1/1

3 Outline Social dilemmas in discretionary databases Over-contribution in discretionary databases p.1/1

4 Outline Social dilemmas in discretionary databases Utility model Over-contribution in discretionary databases p.1/1

5 Outline Social dilemmas in discretionary databases Utility model Agent behaviour in Usenet Over-contribution in discretionary databases p.1/1

6 Outline Social dilemmas in discretionary databases Utility model Agent behaviour in Usenet Group behaviour in Usenet Over-contribution in discretionary databases p.1/1

7 Outline Social dilemmas in discretionary databases Utility model Agent behaviour in Usenet Group behaviour in Usenet Value of an item Over-contribution in discretionary databases p.1/1

8 Outline Social dilemmas in discretionary databases Utility model Agent behaviour in Usenet Group behaviour in Usenet Value of an item Global resource allocation Over-contribution in discretionary databases p.1/1

9 Outline Social dilemmas in discretionary databases Utility model Agent behaviour in Usenet Group behaviour in Usenet Value of an item Global resource allocation Conclusion Over-contribution in discretionary databases p.1/1

10 Discretionary Databases Over-contribution in discretionary databases p.2/1

11 Discretionary Databases Social dilemma: self interest collective interest Over-contribution in discretionary databases p.2/1

12 Discretionary Databases Social dilemma: self interest collective interest ex. the notorious free-rider problem (Sweeney 1973) Over-contribution in discretionary databases p.2/1

13 Discretionary Databases Social dilemma: self interest collective interest ex. the notorious free-rider problem (Sweeney 1973) Game-theoretic approaches (Golle et al. 2001) (Buragohain et al. 2003) emphasizing P2P and free-ridership Over-contribution in discretionary databases p.2/1

14 Discretionary Databases Social dilemma: self interest collective interest ex. the notorious free-rider problem (Sweeney 1973) Game-theoretic approaches (Golle et al. 2001) (Buragohain et al. 2003) emphasizing P2P and free-ridership Performance models (Fuqua et al. 2003) (Mfeldman et al. 2003) still use incentive models Over-contribution in discretionary databases p.2/1

15 Discretionary Databases Social dilemma: self interest collective interest ex. the notorious free-rider problem (Sweeney 1973) Game-theoretic approaches (Golle et al. 2001) (Buragohain et al. 2003) emphasizing P2P and free-ridership Performance models (Fuqua et al. 2003) (Mfeldman et al. 2003) still use incentive models Our goal: analyze over-contribution; reputation Over-contribution in discretionary databases p.2/1

16 Usenet Overview Over-contribution in discretionary databases p.3/1

17 Usenet Overview 1970 = little text traffic 2004 = 1TB/day binary data contributed Over-contribution in discretionary databases p.3/1

18 Usenet Overview 1970 = little text traffic 2004 = 1TB/day binary data contributed Self-organized distributed file-sharing system Over-contribution in discretionary databases p.3/1

19 Usenet Overview 1970 = little text traffic 2004 = 1TB/day binary data contributed Self-organized distributed file-sharing system Differences from P2P: Over-contribution in discretionary databases p.3/1

20 Usenet Overview 1970 = little text traffic 2004 = 1TB/day binary data contributed Self-organized distributed file-sharing system Differences from P2P: free-riding not death-knell Over-contribution in discretionary databases p.3/1

21 Usenet Overview 1970 = little text traffic 2004 = 1TB/day binary data contributed Self-organized distributed file-sharing system Differences from P2P: free-riding not death-knell shared files always consume resources Over-contribution in discretionary databases p.3/1

22 Usenet Overview 1970 = little text traffic 2004 = 1TB/day binary data contributed Self-organized distributed file-sharing system Differences from P2P: free-riding not death-knell shared files always consume resources community spirit & feedback Over-contribution in discretionary databases p.3/1

23 Usenet Overview 1970 = little text traffic 2004 = 1TB/day binary data contributed Self-organized distributed file-sharing system Differences from P2P: free-riding not death-knell shared files always consume resources community spirit & feedback more significant sharing disincentives Over-contribution in discretionary databases p.3/1

24 Usenet Overview 1970 = little text traffic 2004 = 1TB/day binary data contributed Self-organized distributed file-sharing system Differences from P2P: free-riding not death-knell shared files always consume resources community spirit & feedback more significant sharing disincentives over-contribution Over-contribution in discretionary databases p.3/1

25 Model overview Over-contribution in discretionary databases p.4/1

26 Model overview Bayesian game with I = {a 1,a 2,...,a n } Over-contribution in discretionary databases p.4/1

27 Model overview Bayesian game with I = {a 1,a 2,...,a n } Consuming actions eg., downloading a file u DN i a i s consumption utility Over-contribution in discretionary databases p.4/1

28 Model overview Bayesian game with I = {a 1,a 2,...,a n } Consuming actions eg., downloading a file u DN i a i s consumption utility Contributing actions eg., uploading a file u UP i a i s contributory utility Over-contribution in discretionary databases p.4/1

29 Model overview Bayesian game with I = {a 1,a 2,...,a n } Consuming actions eg., downloading a file u DN i a i s consumption utility Contributing actions eg., uploading a file u UP i a i s contributory utility Total utility u i u UP i + u DN i Over-contribution in discretionary databases p.4/1

30 Consumption utility (u DN i ) Over-contribution in discretionary databases p.5/1

31 Consumption utility (u DN i ) Factors: Content Retrieved possibly capped Over-contribution in discretionary databases p.5/1

32 Consumption utility (u DN i ) Factors: Content Retrieved possibly capped Variety Over-contribution in discretionary databases p.5/1

33 Consumption utility (u DN i ) Factors: Content Retrieved possibly capped Variety Heterogeneity Over-contribution in discretionary databases p.5/1

34 Consumption utility (u DN i ) Factors: Content Retrieved possibly capped Variety Heterogeneity Implicit/Explicit Cost Over-contribution in discretionary databases p.5/1

35 Consumption utility (u DN i ) Factors: Content Retrieved possibly capped Variety Heterogeneity Implicit/Explicit Cost Models: Let Q be all content, C Q Over-contribution in discretionary databases p.5/1

36 Consumption utility (u DN i ) Factors: Content Retrieved possibly capped Variety Heterogeneity Implicit/Explicit Cost Models: Let Q be all content, C Q previously, mostly linear: u DN i (C) size(c) Over-contribution in discretionary databases p.5/1

37 Consumption utility (u DN i ) Factors: Content Retrieved possibly capped Variety Heterogeneity Implicit/Explicit Cost Models: Let Q be all content, C Q previously, mostly linear: u DN i (C) size(c) problems: doesn t model variety or interest size isn t linear (cd-image 2 29 vs picture 2 18 ) Over-contribution in discretionary databases p.5/1

38 Consumption utility (framework) Over-contribution in discretionary databases p.6/1

39 Consumption utility (framework) Partition Q into {Q i }, pick C i Q i variety through substitutability Over-contribution in discretionary databases p.6/1

40 Consumption utility (framework) Partition Q into {Q i }, pick C i Q i variety through substitutability Interest matrix W w ij is a i s interest in C j Over-contribution in discretionary databases p.6/1

41 Consumption utility (framework) Partition Q into {Q i }, pick C i Q i variety through substitutability Interest matrix W w ij is a i s interest in C j Class utility function θ i ex. x, log(1 + x) Over-contribution in discretionary databases p.6/1

42 Consumption utility (framework) Partition Q into {Q i }, pick C i Q i variety through substitutability Interest matrix W w ij is a i s interest in C j Class utility function θ i ex. x, log(1 + x) Cost function cost DN i Over-contribution in discretionary databases p.6/1

43 Consumption utility (framework) Partition Q into {Q i }, pick C i Q i variety through substitutability Interest matrix W w ij is a i s interest in C j Class utility function θ i ex. x, log(1 + x) Cost function cost DN i Through the combination of these, we can model virtually any cost function u DN i (C) = cost DN i (C) + m j=1 w ijθ i (size(c j )) Over-contribution in discretionary databases p.6/1

44 Contribution utility (u UP i ) Over-contribution in discretionary databases p.7/1

45 Contribution utility (u UP i ) Factors: Model: Over-contribution in discretionary databases p.7/1

46 Contribution utility (u UP i ) Factors: Inherent Cost Model: cost function cost UP i Over-contribution in discretionary databases p.7/1

47 Contribution utility (u UP i ) Factors: Inherent Cost Inherent Contribution Preference Model: cost function cost UP i benefit function gain UP i Over-contribution in discretionary databases p.7/1

48 Contribution utility (u UP i ) Factors: Inherent Cost Inherent Contribution Preference Explicit Reward Model: cost function cost UP i benefit function gain UP i Over-contribution in discretionary databases p.7/1

49 Contribution utility (u UP i ) Factors: Inherent Cost Inherent Contribution Preference Explicit Reward Reputation / Feedback Model: cost function cost UP i benefit function gain UP i sum of utility provided to other agents: n j i v j(a i ) Over-contribution in discretionary databases p.7/1

50 Contribution utility (u UP i ) Factors: Inherent Cost Inherent Contribution Preference Explicit Reward Reputation / Feedback Model: cost function cost UP i benefit function gain UP i sum of utility provided to other agents: n j i v j(a i ) E(u UP i ) = gain UP i (C) cost UP i (C) + n j i v j(a i ) Over-contribution in discretionary databases p.7/1

51 Usenet utility model Over-contribution in discretionary databases p.8/1

52 Usenet utility model Assumptions: Formulæ: Consumption: = cost DN i u DN i Contribution: u UP i = gain UP i (C) + m j=1 w ijθ i (size(c j )) (C) cost UP i (C) + n j i v j(a i ) Over-contribution in discretionary databases p.8/1

53 Usenet utility model Assumptions: linearity of cost and inherent gain Formulæ: Consumption: u DN i = m j=1 (w ijθ i (size(c j )) γ i size(c j )) Contribution: u UP i = (λ i γ i )size(c) + n j i v j(a i ) Over-contribution in discretionary databases p.8/1

54 Usenet utility model Assumptions: linearity of cost and inherent gain symmetry of θ i Formulæ: Consumption: u DN i = m j=1 (w ijθ(size(c j )) γ i size(c j )) Contribution: u UP i = (λ i γ i )size(c) + n j i v j(a i ) Over-contribution in discretionary databases p.8/1

55 Usenet utility model Assumptions: linearity of cost and inherent gain symmetry of θ i per-user partitioning of Q Formulæ: Consumption: u DN i = n j=1 (b ijθ(c j ) γ i c j ) Contribution: u UP i = (λ i γ i )c i + n j i v j(a i ) Over-contribution in discretionary databases p.8/1

56 Usenet utility model Assumptions: linearity of cost and inherent gain symmetry of θ i per-user partitioning of Q feedback is simply utility derived from the agent Formulæ: Consumption: u DN i = n j=1 ˆvDN ij, ˆv DN ij = (b ij θ(c j ) γ i c j ) Contribution: u UP i = (λ i γ i )c i + n j i ˆvDN ij Over-contribution in discretionary databases p.8/1

57 Reputation un-motivated agents Over-contribution in discretionary databases p.9/1

58 Reputation un-motivated agents Assume that a i has a bound on contribution k i Over-contribution in discretionary databases p.9/1

59 Reputation un-motivated agents Assume that a i has a bound on contribution k i Then is an equilibrium. i, c i = { k i if λ i > γ i 0 otherwise Over-contribution in discretionary databases p.9/1

60 Reputation motivated agents Over-contribution in discretionary databases p.10/1

61 Reputation motivated agents For reputation-motivated agents: There exist fixed c i such that i, c i = min {c i,k i} is a unique Nash equilibrium. Given θ there exists a threshold τ such that if n b ij τ n γ k (2) j i k then c i > 0. Otherwise, c i = 0. Over-contribution in discretionary databases p.10/1

62 Reputation motivated agents For reputation-motivated agents: There exist fixed c i such that i, c i = min {c i,k i} is a unique Nash equilibrium. Given θ there exists a threshold τ such that if n b ij τ n γ k (3) j i k then c i > 0. Otherwise, c i = 0. Feedback in a group of users can regulate individual action to maximize collective welfare. Over-contribution in discretionary databases p.10/1

63 Congestion Over-contribution in discretionary databases p.11/1

64 Resource competition game Over-contribution in discretionary databases p.12/1

65 Resource competition game Benefit matrix B has structure B = Over-contribution in discretionary databases p.12/1

66 Resource competition game Benefit matrix B has structure B = Collective action breaks down over entire system Over-contribution in discretionary databases p.12/1

67 Resource competition game Benefit matrix B has structure B = Collective action breaks down over entire system Assume we have some finite resource with limit κ: if sum of content less than κ, no change otherwise, drop content until sum is less than κ Over-contribution in discretionary databases p.12/1

68 Resource competition (cont) Over-contribution in discretionary databases p.13/1

69 Resource competition (cont) Consider groups as players action: upload c i [0,k i ] of content utility: proportional to non-dropped content Over-contribution in discretionary databases p.13/1

70 Resource competition (cont) Consider groups as players action: upload c i [0,k i ] of content utility: proportional to non-dropped content Assume content is dropped uniformly: u i κc i j c j Over-contribution in discretionary databases p.13/1

71 Resource competition (cont) Consider groups as players action: upload c i [0,k i ] of content utility: proportional to non-dropped content Assume content is dropped uniformly: u i κc i j c j {k 1,k 2,...,k n } is a Bayes-Nash equilibrium... regardless of κ Over-contribution in discretionary databases p.13/1

72 Resource competition (cont) Consider groups as players action: upload c i [0,k i ] of content utility: proportional to non-dropped content Assume content is dropped uniformly: u i κc i j c j {k 1,k 2,...,k n } is a Bayes-Nash equilibrium... regardless of κ Significant problem currently for Usenet servers Over-contribution in discretionary databases p.13/1

73 Contribution valuation Over-contribution in discretionary databases p.14/1

74 Contribution valuation Difficult to quantify eg., how to measure w ij?, partitioning {Q i }? Over-contribution in discretionary databases p.14/1

75 Contribution valuation Difficult to quantify eg., how to measure w ij?, partitioning {Q i }? What can be measured? contributions: f up consumptions: f down size: f size Over-contribution in discretionary databases p.14/1

76 Contribution valuation Difficult to quantify eg., how to measure w ij?, partitioning {Q i }? What can be measured? contributions: f up consumptions: f down size: f size All contributions of f are in the same class, so v(f) = f down f up θ(f size )dt Over-contribution in discretionary databases p.14/1

77 Global resource allocation Over-contribution in discretionary databases p.15/1

78 Global resource allocation Usenet: Bandwidth, retention Over-contribution in discretionary databases p.15/1

79 Global resource allocation Usenet: Bandwidth, retention Utility model for retention: sub-linear Over-contribution in discretionary databases p.15/1

80 Global resource allocation Usenet: Bandwidth, retention Utility model for retention: sub-linear Micro-economic vs. differential service Over-contribution in discretionary databases p.15/1

81 Global resource allocation Usenet: Bandwidth, retention Utility model for retention: sub-linear Micro-economic vs. differential service Current: manually per-group; volume-based Over-contribution in discretionary databases p.15/1

82 Global resource allocation Usenet: Bandwidth, retention Utility model for retention: sub-linear Micro-economic vs. differential service Current: manually per-group; volume-based Proposed: value over size v(f)/f size Over-contribution in discretionary databases p.15/1

83 Conclusions Over-contribution in discretionary databases p.16/1

84 Conclusions We have found that: Future directions: Over-contribution in discretionary databases p.16/1

85 Conclusions We have found that: reputation can mitigate the effects of social dilemmas Future directions: Over-contribution in discretionary databases p.16/1

86 Conclusions We have found that: reputation can mitigate the effects of social dilemmas reputation is inadequate globally Future directions: Over-contribution in discretionary databases p.16/1

87 Conclusions We have found that: reputation can mitigate the effects of social dilemmas reputation is inadequate globally Explicit methods based on item value Future directions: Over-contribution in discretionary databases p.16/1

88 Conclusions We have found that: reputation can mitigate the effects of social dilemmas reputation is inadequate globally Explicit methods based on item value Future directions: analysis of mix of reputation-sensitivity Over-contribution in discretionary databases p.16/1

89 Conclusions We have found that: reputation can mitigate the effects of social dilemmas reputation is inadequate globally Explicit methods based on item value Future directions: analysis of mix of reputation-sensitivity non-stationary repeated setting Over-contribution in discretionary databases p.16/1

90 Questions? Over-contribution in discretionary databases p.17/1