Trust Modeling (Introduction) Ing. Arnoštka Netrvalová September 2008

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

3 September 2008 WHY? WHERE? Phenomenon of everyday life Internet e-banking – credibility e-commerce – trustworthiness of partners e-service – quality, promptness PC and computing /25 Trust modeling

4 September 2008 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 Trust modeling

5 September 2008 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. /25 Trust modeling Gambetta's definition was derived as a summary of the contributions to the symposium on trust in Cambridge, England, 1988.

6 September 2008 “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? Behaviour and trust /25 Trust modeling

7 September 2008     Basic trust levels Blind trust Ignorance Absolute distrust   /25 Trust modeling

8 September 2008 Representation of trust value /25 Trust modeling  Blind trust High trust Low trust Absolute distrust Ignorance Low distrust High distrust

9 September 2008 Hysteretic trust loop Absolute distrust Blind trust Trust value /25 Trust modeling Interval

10 September 2008 Trust visualization „Trust square“: two relation for couple and one value per relationship /25 (1, 0)trust distrust Subject A distrusttrust (1, 1) (0, 1) (0, 0) Subject B (0.5, 0.5) Trust modeling

11 September 2008 Trust visualization BASIC: 1 couple of 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 /25 Example: Trust in community Trust modeling

12 September 2008 Trust types personal personal – trust between entity - unilateral - reciprocal phenomenal phenomenal – trust to phenomenon (product) A B C Example: Representation of personal trust in group /25 Trust modeling

13 September 2008 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 Trust modeling

14 September 2008 /25 Phenomenal trust forming - 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<  <  1 - trust difference (trust acquisition, trust loss) Trust modeling

15 September 2008 Trust model concept Basic idea - intervention trust model /25      Producers    World  Dominator     Consumers      Application support ---- control ….. data  communication Trust modeling 

16 September 2008 Environment Agent Trust, agents and MAS /25 Trust modeling PerceptionRepresentation Knowledge base Decision making PlanningAction Learning Agents Agent Communication Knowledge base Reputations Recommendations Trust Evaluation Context

17 September 2008 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 - development of MAS(FIPA standards), middleware Runtime environment Libraries for development of agent Graphical tool package for administration and monitoring of agents /25 Trust modeling

18 September 2008 Cooperation – selection of partners Application Graph theory Game theory Risk - “caution index” Reciprocal trust Trust matrix /25 Trust modeling

19 September 2008 Cooperation – caution index Payoff matrix Payoff matrix r = (y -z) x = g = (x -y) w = (1- ) t = (w -x) z = (1- ) y = (1- ) (1- ) /25 Caution matrix Caution index Trust modeling

20 September 2008 Cooperation - criteria of couple selection Trust modeling Criteria of couple selection Minimum: 1. means both of caution index 2. maximum of caution index of evaluated couples Reduced caution matrix (pre-selected pairs)

21 September 2008 Results – personal trust (Trustor) /25 Trust modeling

22 September 2008 Results - cooperation Example (n=15,  =10°, t ij - 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 Group size n (α=15°)Number of identical couples/1000 runs Trust modeling

23 September 2008 Can it be trusted? Trust in Math The classic proof that 2 = 1 runs thus. 1. First, let x = y = 1. Then: x = y 2. x 2 = xy 3. x 2 - y 2 = xy - y 2 4. (x + y)(x - y) = y (x - y) 5. x + y = y 6. 2 = 1 Now, you could look at that, and shrug, and say … /25 Trust modeling

24 September 2008 Důvěra, práce a výsledky Trust modeling „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

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