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? Where? What?
 Behaviour and trust
 Trust representation
 Trust visualization
 Trust forming
 Trust, agents and MAS
 Cooperation
 Results
 Can it be trusted?

[ChangingMinds.org]
September 2008
2 / 25
Trust modeling
WHY? WHERE?
Phenomenon of everyday life
 Internet

 e-banking
– credibility
 e-commerce
 e-service
 PC
September 2008
– trustworthiness of partners
– quality, promptness
and computing
3 /25
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)
September 2008
4 /25
Trust modeling
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.
Gambetta's definition was derived as a summary of the contributions to the symposium
on trust in Cambridge, England, 1988.
September 2008
5 /25
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?

September 2008
6 /25
Trust modeling
Basic trust levels

Blind
trust



Ignorance
Absolute
distrust
September 2008





7 /25
Trust modeling
Representation of trust value
1
0
0.95
Blind
trust
0.7
0.5
0.3
 0.025
High
trust
0.05
High
distrust
Low trust
Absolute
distrust
Low distrust
Ignorance
September 2008
8 /25
Trust modeling
Hysteretic trust loop
Trust value
Absolute
distrust
Ignorance
Th
ThTh+
Interval
Blind trust
1
September 2008
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,1
0
9 /25
Trust modeling
Trust visualization
„Trust square“: two relation for couple and one value per relationship
trust
(1, 0)
(1, 1)
Subject A
(0.5, 0.5)
distrust
(0, 1)
(0, 0)
distrust
trust
Subject B
September 2008
10 /25
Trust modeling
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
1
2
3
4
Example:
5
September 2008
6
7
8
9
Trust in community
11 /25
Trust modeling
Trust types
A


personal
– trust between entity
- unilateral
- reciprocal
phenomenal
– trust to phenomenon
(product)
0.9
0.8
0.6
B
C
0.5
Example:
Representation of personal trust
in group
September 2008
12 /25
Trust modeling
Personal trust forming

Tij  f tij , t ji , cij , rCij , rPij , zij , G , 

Tij  tij  tij
t ij - personal trust i-th entity to j-th entity
t ji - personal trust j-th entity to i-th entity
cij - number of reciprocal contacts i-th and j-th entities
rCij - number of recommendations of j-th entity to i-th from others
zij - knowledge (learning, testing set)
rPij - reputation of j-th entity at i-th entity
G  ,   - randomness, where 0<<1
tij - trust difference (trust acquisition, trust loss)
September 2008
13 /25
Trust modeling
Phenomenal trust forming

Ti k  f tik , rCki , rPki , G , 

Ti k  t ik  t ik
tik
- trust i-th entity in k-th product
k
- number of recommendation of k-th product to i-th entity
rCi
rPik - reputation of k-th product at i-th entity
G  ,   - randomness, where 0<<1
t ik
September 2008
- trust difference (trust acquisition, trust loss)
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Trust modeling
Trust model concept
Basic idea - intervention trust model
Application
support
World
 




Consumers

 


Producers


 




 

Dominator
----
control
….. data
 communication
September 2008
15 /25
Trust modeling
Trust, agents and MAS
Environment
Agent
Learning
Perception
Representation
Knowledge base
Decision
making
Planning
Action
Context
Agents
Agent
Knowledge
base
Reputations
September 2008
Trust
Evaluation
Communication
Recommendations
16 /25
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 - development of MAS(FIPA standards), middleware



Runtime environment
Libraries for development of agent
Graphical tool package for administration and monitoring of agents
September 2008
17 /25
Trust modeling
Cooperation – selection of partners
Application




Graph theory
Game theory
Risk - “caution index”
Reciprocal trust
Trust matrix
 t11 t12

 t 21 t 22
t
t 32
31

T
 t 41 t 42
 ... ...

t
 n1 t n 2
September 2008
t13
t14
t 23
t 24
t 33
t 34
t 43
t 44
...
...
t n3
t n4
... t1n 

... t 2 n 
... t 3n 

... t 4 n 
... ... 
... t nn 
18 /25
Trust modeling
Cooperation – caution index
Payoff matrix
P
O
P
x, x
w, z
O
z, w
y, y
x = tij t ji
w = t ij (1- t ji)
z = (1- t ij ) t ji
y = (1- t ij ) (1- t ji )
r = (y -z)
g = (x -y)
t = (w -x)
Caution matrix
Caution index
rt
c
g
September 2008
 c11

 c21
c
C   31
 c41
 ...

c
 n1
c12
c13
c14
c22
c23
c24
c32
c33
c34
c42
c43
c44
...
...
...
cn 2
cn 3
cn 4
... c1n 

... c2 n 
... c3n 

... c4 n 
... ... 
... cnn 
19 /25
Trust modeling
Cooperation - criteria of couple selection
Reduced caution matrix
(pre-selected pairs)
 c p11 c p12 c p13 c p14 ... c p1n 

C p  
 c p 21 c p 22 c p 23 c p 24 ... c p 2n 
Criteria of couple selection
Minimum:
1. means both of caution index
2. maximum of caution index of evaluated couples
September 2008
20
Trust modeling
Results – personal trust (Trustor)
trust
1
Trustee's reputation
0,9
0,8
t12
t14
t25
reputation
1
0,7
t32
t34
t54
0,84
0,9
0,6
0,74
0,8
0,5
0,79
0,7
0,4
0,6
0,3
0,5
0,2
0,4
0,1
0,3
0
0,2
0
1
2
3
4
step
3
5
0,34
0,27
0,14
0,1
0
2
1
S[i,j]
1
0
4
0
1
2
3
4
5
3
2
1
s12 (r21)
s14 (r41)
s25 (r52)
s32 (r23)
s34 (r43)
s54 (r45)
0
0
September 2008
1
2
3
4
5
21 /25
Trust modeling
Results - cooperation
Example
(n=15, =10°, tij - random):
t
BA
1
[0;6]
[4;9]
[4;13]
[5;9]
[5;10]
[9;12]
[12;14]
c[0.45;0.15]
c[0.52;0.35]
c[0.19;0.51]
c[0.40;0.49]
c[0.36;0.50]
c[0.56;0.24]
c[0.40;0.36]
t[0.96;0.82]
t[0.79;0.72]
t[0.78;0.94]
t[0.71;0.74]
t[0.72;0,79]
t[0.88;0.72]
t[0.83;0.81]
0,78; 0,94
0,9
0,96; 0,82
0,83; 0,81
Group size n (α=15°)
Number of identical
couples/1000 runs
15
669
50
659
100
663
500
672
1 000
662
0,8
0,72; 0,79
0,71; 0,74
September 2008
0,79; 0,72
0,88; 0,72
0,7
0,7
0,8
0,9
1
t
AB
22 /25
Trust modeling
Can it be trusted?
Trust in Math
The classic proof that 2 = 1 runs thus.
1.
2.
3.
4.
5.
6.
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 …
September 2008
23 /25
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]
September 2008
24 /25
Thank you for your attention.