Diffusion and social networks: revisiting medical innovation with agents

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.