@unpublished{Emond_2002_Cognitive-Modelling,
	title = {Cognitive Modeling and its Application for the Development of Socially Adept Technologies},
	author = {B. Emond and R.L. West},
	year = {2002},
	biburl = {http://www.bibsonomy.org/bibtex/2557704e0ea2c7c1ad42c1971d906da7d/wnpxrz},
	abstract = {In this paper we would like to propose that the
methodology of cognitive modeling can provide an
approach to the implementation of socially adept
technologies and the evaluation of its usability. We base
this approach on the assumption that representations play
an essential role in mediating social relations. Cognitive
representations and their causal link to the social and
physical environments have been recognized as an essential
element for understanding social relations. The challenge is
to port cognitive modeling methodology into the realm of
socially adept technologies. The first section briefly
presents the state of development of a modeling
environment aimed at supporting usability testing of sociotechnical
systems. The second section will give an
overview of an application intended to support social and
personal awareness in a web-based learning environment.
This application maps users identity, users behavior, and
shared information content into a model of human
memory.
},
	keywords = {cognition cognitive model modeling }
}

@unpublished{Stewart_2006_Popularity,
	title = {Distributed Factors in the Development of Popularity (or 'Why Doesn't Anybody Like Me?')},
	author = {Terry Stewart and Robert L. West and Robert Coplan},
	year = {2006},
	biburl = {http://www.bibsonomy.org/bibtex/2283b48fc45896ba27af6d3ad492128f8/stefano},
	abstract = {Individual factors by no means completely account for
individual popularity within a group structure. To begin to
explain the majority of the variance, we must investigate the
hypothesis that popularity is strongly influenced by the
dynamics of group interactions. Here, we present a
computational model of peer interaction that allows us to
investigate the influence of different distributed factors. In
constructing the model, we discovered that certain elements
are vital for the simulation to produce data that matches the
observed patterns in real social groups. We found that the
internal representation of how much agents like each other
must be discrete, that judgements should be made relative to
behavioural expectations, and that models do not require
variation in the initial state of the agents to produce realistic
individual differences in popularity. Our result is a set of
models with psychologically realistic attributes. When
simulated, these models result in popularity data that cannot
be reliably distinguished from real life data. Since these
models capture the essential dynamics of the social group
interaction, they can form the basis for understanding how
interaction within the group influences individuals to become
popular or rejected.
},
	keywords = {cognition networks social }
}