1. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge1 A New Approach for Trust Calculation in Social Networks Mehrdad Nojoumian (student) Timothy C. Lethbridge (supervisor) University of Ottawa, Canada tcl@site.uottawa.ca

2. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge2 Objectives of this talk Explore the behavior of various trust calculation approaches Describe an approach that has an improved combination of characteristics.

3. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge3 Some definitions Social network  Nodes are actors (buyers, sellers, partners, brokers)  Arcs are relationships (buying, selling, advising, consulting, sharing, etc.) Reputation: Perception an agent has of another’s intentions  Derived from one’s own observations and those in one’s social network  Reputation is a social quantity, but everyone has their own perception of it

4. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge4 Trust Personal expectation about another’s behavior in a particular encounter (Mui 2002)  Derived from reputation Parties in a transaction must establish trust to do business effectively If party A has low trust of party B, party A will be willing to pay party B less, and will need to consider insurance  So party B has an incentive to be trustworthy

5. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge5 Reputation systems Gather experiences from participants as transactions take place  Trustworthy agents increase in reputation  Untrustworthy agents drop in reputation Reputation systems can be ‘centralized’  E.g. in EBay, sellers receive ratings (-1, 0,1) for reliability.  Reputation can be the sum or some other function of those ratings

6. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge6 Decentralized reputation systems A1 can query others who have transacted with A2 Overall reputation can be a combination of A1’s:  Direct experience with A2  Feedback from others who have interacted with A2  Reputation of others (A3, A4 and A5) as witnesses

7. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge7 Trust is built up over time Through a series of transactions  Co-operations (C) = good experiences with the agent in question Delivery occurred in a timely manner Merchandise was as advertised Payment was received in full and on time Acted as a truthful or reliable witness  Defections (D) = bad experiences Delivery excessively late Merchandise wrong or inferior to expectation Payment excessively late or not received Acted as an untruthful or unreliable witness

8. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge8 A sample trust function from the literature Y&S: Yu and Singh (2000)  Compute T t+1 = f(T t, CorD)

9. Aug 10, 20069 Effect of Y&S  = 0.1 and  = -0.2 Trust value after cooperation Trust value before transaction Trust value after defection Increment in trust after cooperation Decrement in trust after defection Yellow region: The better you are, the less co-operation benefits Yellow region: The worse you are, the less defection costs

10. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge10 Y&S ‘Increment’ view  = 0.1 and  = -0.2 Increment on defection Increment on cooperation Trust value, T t

11. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge11 Y&S ‘Next value’ (T t+1 ) view  = 0.1 and  = -0.2 Trust value, T t Next value on defection Next value on cooperation

12. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge12 Y&S ‘Sequence’ view  = 0.1 and  = -0.2 Sequences of  30 cooperates  10 cooperates + 10 defects + 10 cooperates  30 defects Inflection point Penalty for D after C

13. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge13 A new proposed formula family: N&L (Nojoumian and Lethbridge) Key changes:  As trust increases above threshold , keep increasing the reward for co-operation Up to the maximum  As trust decreases below threshold  keep increasing the cost of defection Down to the minimum  Between  and  keep cost and reward fixed

14. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge14 The N&L trust function: In case of Cooperation T t in [-1,  )  Bad agent (for now): Encourage  Reward increases linearly from Xencourage (default 0.01) to Xgive (default 0.05) T t in [ ,  ]  Agent about which you are indifferent: Give Xgive T t in ( , +1]  Good agent: Reward  Reward increases linearly from Xgive to Xreward (default 0.09)

15. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge15 The N&L trust function: In case of Defection T t in [-1,  )  Bad agent: Penalize  Increment increases linearly from Xpenalize (default -0.09) to Xtake (default -0.05) T t in [ ,  ]  Agent about which you are indifferent: Take Xtake T t in ( , +1]  Good agent (for now): Discourage  Increment increases linearly from Xtake to Xdiscourage (default -0.01)

16. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge16 Comparison of ‘increment’ views  = 0.1 and  = -0.2 Y&SN&L Next value on defection Next value on cooperation -1 Trust value, T t +1

17. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge17 Comparison of ‘next value’ (T t+1 ) views  = 0.1 and  = -0.2 Y&S N&L Increment on defection Increment on cooperation -1 Trust value, T t +1

18. Aug 10, 200618 Comparison of ‘sequence’ views  = 0.1 and  = -0.2 Yellow: 10C 10D 10C Y&S N&L Inflected asymptotic Less severe penalty for D after C, but can be adjusted ‘Maxed out’

19. Aug 10, 200619 Effect of adjusting N&L parameters:  0.1 to 0.3 and  -0.2 to -0.4 OriginalResult Slight delay only Longer indifferent period

20. Aug 10, 200620 Effect of adjusting N&L parameters:  Xencourage 0.01 to 0.015 and  Xpenalize -0.09 to -0.15 OriginalResult Larger D after C penalty Effect of increased penalty Slight effect of increased encouragement

21. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge21 Effect of different N&L sequences Xencourage remains 0.015 and Xpenalize remains -0.15 20C 10D end 0 10D 20C end <0 10C 20D 10D 10C 10D

22. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge22 Same sequences from Y&S function 20C 10D end 0 10C 20D 10D 10C 10D

23. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge23 Microsoft Excel formula for calculating N&L trust values =prevTrustValue+(IF(CorD="C", MIN(1-PrevTrustValue, IF(GoodOrBad="B", X_encourage+(PrevTrustValue+1)/(beta2--1)*(X_give-X_encourage), IF(GoodORBad="I", X_give, X_give+(PrevTrustValue-alpha2)/(1-alpha2)*(X_reward-X_give) ))), MAX(-1-Y50, IF(GoodORBad="B", X_penalize+(PrevTrustValue+1)/(beta2--1)*(X_take-X_penalize), IF(GoodORBad="I", X_take, X_take+(PrevTrustValue-alpha2)/(1-alpha2)*(X_discourage-X_give) ))) ))

24. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge24 You can simplify calculations by using an approximation Results of quadratic regression for the N&L for default parameters

25. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge25 Varying the function for varying transaction value E.g. You could apply the formula N=floor(Log 10 (V)) times where V is the transaction value  I.e. $10-$99 - Apply once $100-$999 - Apply twice $1000-$9999 - Apply 3 times Etc. The base of the logarithm can be changed for different effects

26. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge26 Main drawback of N&L ‘Maxing out’ or ‘hitting rock bottom’  No further increase in trust after you reach 1  No further decrease in trust after you reach -1 Asymptotic approach corresponds to ‘diminishing returns’ Could be rectified by making the function open-ended

27. Conclusions - It seems reasonable to consider that trust functions should  Reward more (or same) the better an agent is  Penalize more (or same) the worse an agent is Y&C trust function does not have these properties  But has asymptotic approach / diminishing returns

28. Aug 10, 2006Trust Calculation - Nojoumian and Lethbridge28 Conclusions - 2 We propose a family of trust functions  Reward always increases the better an agent is, and vice-versa  Eight parameters can be adjusted to fine tune behavior Future work:  Empirically evaluate the ability of the variously parameterized Y&C or N&L functions to predict actual trustworthiness