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Personalized Collaboration 265
Trust aggregation method PathRank (Friedman, 2007) computes the reputation of
entity v as the length of the shortest path (edges have length equal to inverse trust values)
from some start node v0 V. This allows for personalized reputation functions where each
node uses itself as the start node. PathRank algorithm is robust against both dishonest
feedback and Sybil attacks since it is asymmetric and sybils cannot manipulate the length
of a shortest path between legitimate entities.
The list of above mentioned types of attack is by no means complete and reputation
systems are under constant attack by previously unknown types of attack, requiring them
to continuously tweak their ranking mechanisms.
9.3 Human-Computer Relationships
Interpersonal relationships were found to be useful in many contexts. Even in education,
relationships between students are important in peer learning situations, and collaboration
between friends was found to be more effective than collaboration between acquaintances
(Hartup, 1998), as friends engage in more extensive discourses and are more supportive
and critical at the same time.
People react to computer agents in fundamentally social ways (Reeves, 2003). The
provisions of human relationships such as emotional support, group belonging, and social
network support, can be made available by the use of intelligent computer technology.
Embodied in various physical forms (e.g. toys, jewelry), or even purely software agents,
relational agents are computational artifacts designed to build long-term social-emotional
relationships with human users (Bickmore, 2003). Language is the primary modality in
constructing human relationships, and even though many relational strategies are nonverbal,
relational agents need to implement at least simple text interfaces. Animated humanoid
agents employing speech, gestures, intonation and other nonverbal modalities that
emulate face-to-face interactions are currently being studied in human-computer interaction
community.
Typically, relationships span from the micro level of face-to-face relational conversation
to the macro level of long-term maintenance, and thus relational agents must employ
strategies for maintaining and developing relationships, just as people do.
Several interesting relational effects in non-embodied text-only human-computer interfaces
were demonstrated (Reeves, 2003), such as computers which praise rather than
criticize their users are liked more, users prefer the computer to match them in personality,
and users prefer computers that become more like them over time over those which maintain
a consistent level of similarity.
9.3.1 Relational Strategies
In social psychology, the concept of relationship is referring to the interaction between two
people whose behavior is mutually dependent in that a change in the state of one produces
a change in the other (Kelley, 2002). Furthermore, relationship is not defined by generic
patterns of stereotypical interactions (e.g. buyer–seller) but rather by the unique patterns
for a particular pair (Berschied, 1998).
In relationship, friends are expected to provide various provisions for each other
(Duck, 2007) such as the sense of belonging, anchor points for opinions and beliefs, oppor-