Abstract
-
the classic study on diffusion of Tetracycline by
Coleman, Katz and Menzel (1966). Medical
Innovation articulates how different patterns of
interpersonal communications can influence the
diffusion process at different stages of adoption.
In their pioneering study, individual network
(discussion, friendship or advice) was perceived
as a set of disjointed pairs, and the extent of
influences were therefore, evaluated for pairs of
individuals. Given the existence of overlapping
networks and consequent influences on doctors’
adoption decisions, the complexity of actual
events was not captured by pair analysis.
Subsequent reanalyses (Burt 1987, Strang and
Tuma 1993, Valente 1995, Van den Bulte and
Lilien 2001) failed to capture the complexity
involved in the diffusion process and had a static
exposure of the network structure. In this paper,
for the first time, we address these limitations by
combining Agent-Based Modeling (ABM) and
network analysis.
Based on the findings of Coleman et. al. (1966)
study, we develop a diffusion model, Gammanym.
Using SMALLTALK programming language,
Gammanym is developed with CORMAS
platform under Visual Works environment. The
medical community is portrayed in an 8 X 8
spatial grid. The unit cell captures three different
locations for professional interactions: practices,
hospitals, and conference centers, randomly
located over the spatial grid. Two social agents-
Doctor and Laboratory are depicted in the model.
Doctors are the principal agents in the diffusion
process and are initially located at their respective
practices. A doctor’s adoption decision is
influenced by a random friendship network, and a
professional network created through discussions
with office colleagues, or hospital visits or
conference attendance. A communicating agent,
Laboratory, on the other hand, influences doctors’
adoption decisions by sending information through
multiple channels: medical representatives or
detailman visiting practices, journals sent to
doctors’ practices and commercial flyers available
during conferences. Doctors’ decisions to adopt a
new drug involve interdependent local interactions
among different entities in Gammanym.
The cumulative adoption curves (Figure 1) are
derived for three sets of initial conditions, based on
which network topology and evolution of uptake
are analyzed. The three scenarios are specified to
evaluate the degree of influences by different
factors in the diffusion process: baseline scenario
with one seed (initial adopter), one detailman and
one journal; heavy media scenario with one seed
but increasing degrees of external influence, with
five detailman and four journals; and integration
scenario with one seed, without any external
influence from the laboratory.