A Multi-theoretical Multi-level Multi-agent Computational
Model
of the Co-evolution of Communication and Knowledge Networks
Noshir Contractor
nosh@uiuc.edu
The agent-based approach to the study of complex systems
is especially well suited to understand knowledge networks among agents where
some of the agents may be humans, while others may be non-human, such as
knowledge repositories. This paper addresses a general, but increasingly
relevant, question: Under what conditions, are individuals more likely to
seek information they need from (or provide information they posses to) other
individuals as opposed to knowledge repositories? The theories of Transactive
Memory and Public Goods both seek to describe the conditions under which
agents share (retrieve or allocate) information in order to accomplish a
collective task. The Theory of Transactive Memory offers a set of peer-to-peer
mechanisms to explain these processes in terms of an agent's perception of
others' knowledge (directory updating and expertise recognition). As such
it offers an explanation primarily at the dyadic level of analysis. Public
Goods theory describes, in terms of agents‚ individual costs and benefits,
the conditions under which as a collective they are more likely to share
information with others by publishing to, and retrieving from, communal knowledge
repositories. Public Goods theory therefore offers an explanation that is
based at the dyadic and global levels of analyses. Implementing and „docking‰
computational models based on these two theories, which operate at multiple
levels, offer emergent and empirically verifiable hypotheses in response
to the question posed above.