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Trust Modeling ( Introduction)

Ing. Arnoštka Netrvalová. Trust Modeling ( Introduction). September 2008. Trust modeling. Fide, sed qui fidas , vide. It is an equal failing to trust everybody, and to trust nobody. Why ? W here? What? Behaviour and t rust Trust representation Trust visualization Trust forming

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Trust Modeling ( Introduction)

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  1. Ing. Arnoštka Netrvalová Trust Modeling(Introduction) September 2008

  2. Trust modeling Fide, sed qui fidas, vide. It is an equal failing to trusteverybody, and to trust nobody. • Why? Where?What? • Behaviour and trust • Trust representation • Trust visualization • Trust forming • Trust, agents and MAS • Cooperation • Results • Can it be trusted? [ChangingMinds.org] / 25

  3. Trust modeling WHY? WHERE? • Phenomenon of everyday life • Internet • e-banking – credibility • e-commerce – trustworthiness of partners • e-service – quality, promptness • PC and computing /25

  4. Trust modeling 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, system’s optimization) • Semantic web – credibility of sources (machine information collection) /25

  5. Trust modeling Trust definition Gambetta's definition was derived as a summary of the contributions to the symposium on trust in Cambridge,England, 1988. 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. /25

  6. Trust modeling 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? /25

  7. Trust modeling  Blind trust    Ignorance    Absolute distrust   Basic trust levels /25

  8. Trust modeling 1 0 0.95 0.7 0.5  0.025 0.3 0.05 Absolute distrust High trust High distrust Blind trust Low trust Low distrust Ignorance Representation of trust value /25

  9. Trust modeling Hysteretic trust loop Trust value Absolute distrust Interval Blind trust /25

  10. Trust modeling trust (1, 0) (1, 1) Subject A (0.5, 0.5) (0, 1) distrust (0, 0) distrust trust Subject B Trust visualization „Trust square“: two relationfor coupleandonevalueper relationship /25

  11. Trust modeling 1 2 3 4 5 6 7 8 9 Trust visualization BASIC: 1 coupleof reciprocal distrust 3 couple - one entity trusts the other one and the other entity distrusts completely the first one 5 couple - one entity trusts and the other one is indifferent 7 couple - one entity is indifferent and the other distrusts the first one 9 -both entities are indifferent to each other or no relationship between them Example: Trust in community /25

  12. Trust modeling A 0.9 0.8 0.6 B C 0.5 Trust types • personal – trust between entity -unilateral -reciprocal • phenomenal – trust to phenomenon (product) Example: Representation of personal trust in group /25

  13. Trust 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<<1 - trust difference (trust acquisition,trust loss) /25

  14. Trust modeling Phenomenal trust forming -trust i-th entity in k-th product -number of recommendationof k-th product toi-thentity - reputation of k-th product at i-th entity -randomness, where 0<<1 - trust difference (trust acquisition,trust loss) /25

  15. Application support World    Consumers     Producers      Dominator Trust modeling Trust model concept Basic idea - interventiontrust model  ---- control ….. data  communication /25

  16. Trust modeling Context Agents Agent Trust Knowledge base Evaluation Communication Environment Reputations Recommendations Agent Learning Perception Representation Knowledge base Decision making Planning Action Trust,agents and MAS /25

  17. Trust modeling Software for agent modeling and simulation • RETSINA (Reusable Environment for Task-Structured Intelligent Networked Agents ) - Carnegie Mellon University • Swarm (Swarm Intelligence) - Santa FE Research Institute • JADE (Java Agent DEvelopment Framework) JADE - developmentof MAS(FIPAstandards), middleware • Runtime environment • Librariesfordevelopment of agent • Graphical tool package for administration and monitoring of agents /25

  18. Trust modeling Cooperation – selection of partners Application • Graph theory • Game theory • Risk -“caution index” • Reciprocal trust Trust matrix /25

  19. Trust modeling Cooperation – caution index Payoff matrixr = (y -z)x =  g=(x -y) w =(1-) t = (w -x)z = (1-) y = (1-) (1-) Caution matrix Caution index /25

  20. Trust modeling Cooperation - criteria of couple selection Reduced caution matrix (pre-selected pairs) Criteria of couple selection Minimum: 1. meansboth ofcaution index 2. maximumofcaution index of evaluatedcouples

  21. Results – personal trust (Trustor) Trust modeling /25

  22. Trust modeling t BA 1 0,78; 0,94 0,9 0,96; 0,82 0,83; 0,81 0,8 0,72; 0,79 0,71; 0,74 0,79; 0,72 0,88; 0,72 0,7 0,7 0,8 0,9 1 t AB 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] /25

  23. Trust modeling 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 … /25

  24. Trust modeling Důvěra, práce a výsledky „Malá důvěra je příčinou třenic a sporů, často vyvolaných neetickým či neprofesionálním jednáním. Jejím projevem jsou skryté agendy a politikaření skupin. Bývá zdrojem nezdravé rivality, vede k uvažování „výhra-prohra“ a ústí do defenzivní komunikace. Důsledkem je snížení rychlosti a zvýšení námahy při řešení úkolů.“ … … „Tím nejdůležitějším faktorem ovlivňujícím důvěru jsou výsledky. Avšak být důvěryhodným, neznamená jen mít výsledky, ale také docílit, aby o nich věděli i ostatní.“ Stephen M. R. Covey: Důvěra: jediná věc, která dokáže změnit vše, Management Press, 2008 [Stephen M. R. Covey: The Speed of Trust, Free Press,New York, 2006] /25

  25. Thank you for your attention.

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