Download - Trust Modeling ( Introduction)
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Trust Modeling
(Introduction)Ing. Arnotka NetrvalovSeptember 2008
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Trust modelingWhy? Where? What?Behaviour and trustTrust representationTrust visualizationTrust formingTrust, agents and MASCooperationResultsCan it be trusted?/ 25Fide, sed qui fidas, vide. It is an equal failing to trust everybody, and to trust nobody.[ChangingMinds.org]
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WHY? WHERE?Phenomenon of everyday lifeInternete-banking credibilitye-commerce trustworthiness of partnerse-service quality, promptnessPC and computing/25Trust modeling
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WHERE? WHAT?Computing and trust
P2P systems security (working together of nodes)GRID computing security (reliability of sources, users)AD HOC networks message integrity (node =server, router, client, malicious nodes, special protocols, cryptographic codes)MAS security dependability (malicious agent detection, migrating, selection of the best agent, systems optimization)Semantic web credibility of sources (machine information collection)/25Trust modeling
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Trust definition
Trust (or symmetrically, distrust) is a particular level of the subjective probability with which an agent will perform a particular action, both before we can monitor such an action (or independently of our capacity of ever to be able to monitor it) and in a context in which it affects our own action. /25Trust modelingGambetta's definition was derived as a summary of the contributions to the symposium on trust in Cambridge, England, 1988.
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Behaviour and trust
I trust him.How much do I trust him?How much I think, he trusts me ?
What does it mean? Can trust be measured? What is visual representation of trust?/25Trust modeling
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Basic trust levels/25Trust modeling
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Representation of trust value/25Trust modeling
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Hysteretic trust loopAbsolute distrustBlind trustTrust value/25Trust modelingInterval
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0.950.990.9
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Trust visualizationTrust square: two relation for couple and one value per relationship /25Trust modeling
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Trust visualizationBASIC:1 couple of reciprocal distrust 3 couple - one entity trusts the other one and the other entity distrustscompletely the first one5 couple - one entity trusts and the other one is indifferent7 couple - one entity is indifferent and the other distrusts the first one9 - both entities are indifferent to each other or no relationship between them/25Example: Trust in communityTrust modeling
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Trust types personal trust between entity- unilateral - reciprocal
phenomenal trust to phenomenon (product)Example: Representation of personal trust in group/25Trust modeling
- Personal trust forming - personal trust i-th entity to j-th entity- personal trust j-th entity to i-th entity - number of reciprocal contacts i-th and j-th entities - number of recommendations of j-th entity to i-th from others - knowledge (learning, testing set) - reputation of j-th entity at i-th entity - randomness, where 0
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Phenomenal trust forming/25 - trust i-th entity in k-th product - number of recommendation of k-th product to i-th entity
- reputation of k-th product at i-th entity
- randomness, where 0
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Trust model conceptBasic idea - intervention trust model/25---- control.. data communicationTrust modeling
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Trust, agents and MAS /25Trust modeling
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Software for agent modeling and simulation
RETSINA (Reusable Environment for Task-Structured Intelligent Networked Agents ) - Carnegie Mellon University Swarm (Swarm Intelligence) - Santa FE Research InstituteJADE (Java Agent DEvelopment Framework)
JADE - development of MAS(FIPA standards), middleware
Runtime environmentLibraries for development of agentGraphical tool package for administration and monitoring of agents/25Trust modeling
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Cooperation selection of partners Application
Graph theory Game theory Risk - caution index Reciprocal trust
Trust matrix/25Trust modeling
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Cooperation caution indexPayoff matrix r = (y -z) x= g = (x -y) w = (1- ) t = (w -x) z=(1- ) y= (1- ) (1- )/25Caution matrixCaution index Trust modeling
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Cooperation - criteria of couple selectionTrust modelingCriteria of couple selection
Minimum:
1. means both of caution index2. maximum of caution index of evaluated couplesReduced caution matrix(pre-selected pairs)
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Results personal trust (Trustor)/25Trust modeling
Graf8
0.270.140.340.840.740.79
s12 (r21)
s14 (r41)
s25 (r52)
s32 (r23)
s34 (r43)
s54 (r45)
S[i,j]
reputation
Trustee's reputation
tab
T-stept12t14t25t32t34t54
00.970.350.410.550.030.31
10.990.350.410.590.030.32
210.350.410.50.020.3
30.920.350.410.50.020.3
40.920.360.410.50.020.3
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C-stepc12c14c25c32c34c54
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D-stepd12d14d25d32d34d54
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Rs12 (r21)s14 (r41)s25 (r52)s32 (r23)s34 (r43)s54 (r45)
0.270.140.340.840.740.79
Trust
0.970.350.410.550.030.31
0.990.350.410.590.030.32
10.350.410.50.020.3
0.920.350.410.50.020.3
0.920.360.410.50.020.3
0.920.350.410.50.020.33
t12
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number of recommendations
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number of recommendations
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Recommendations - steps
Rep
0.270.140.340.840.740.79
s12 (r21)
s14 (r41)
s25 (r52)
s32 (r23)
s34 (r43)
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reputation
Reputation
Rep (2)
0.270.140.340.840.740.79
subject
s12 (r21)
s14 (r41)
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reputation
Trustee's reputation
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0.970.350.410.550.030.31
0.990.350.410.590.030.32
10.350.410.50.020.3
0.920.350.410.50.020.3
0.920.360.410.50.020.3
0.920.350.410.50.020.33
t12
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step
trust
0.270.140.340.840.740.79
s12 (r21)
s14 (r41)
s25 (r52)
s32 (r23)
s34 (r43)
s54 (r45)
S[i,j]
reputation
Trustee's reputation
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Results - cooperation
Example (n=15, =10, tij - random):
[0;6] c[0.45;0.15] t[0.96;0.82] [4;9] c[0.52;0.35] t[0.79;0.72] [4;13] c[0.19;0.51] t[0.78;0.94] [5;9] c[0.40;0.49] t[0.71;0.74] [5;10] c[0.36;0.50] t[0.72;0,79] [9;12] c[0.56;0.24] t[0.88;0.72] [12;14] c[0.40;0.36] t[0.83;0.81]/25Trust modeling
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Can it be trusted?Trust in Math
The classic proof that 2 = 1 runs thus.
First, let x = y = 1. Then: x = y x2 = xy x2 - y2 = xy - y2 (x + y)(x - y) = y (x - y) x + y = y 2 = 1
Now, you could look at that, and shrug, and say /25Trust modeling
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Dvra, prce a vsledkyTrust modelingMal dvra je pinou tenic a spor, asto vyvolanch neetickm i neprofesionlnm jednnm. Jejm projevem jsou skryt agendy a politikaen skupin. Bv zdrojem nezdrav rivality, vede k uvaovn vhra-prohra a st do defenzivn komunikace. Dsledkem je snen rychlosti a zven nmahy pi een kol.
Tm nejdleitjm faktorem ovlivujcm dvru jsou vsledky. Avak bt dvryhodnm, neznamen jen mt vsledky, ale tak doclit, aby o nich vdli i ostatn. Stephen M. R. Covey: Dvra: jedin vc, kter doke zmnit ve, Management Press, 2008[Stephen M. R. Covey: The Speed of Trust, Free Press, New York, 2006]/25
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Thank you for your attention.
Trust modelingTrust modeling