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Diffusion and social networks: revisiting medical innovation with agents

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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.

Authors


  •   Perez, Pascal
  •   Ratna, Nazmun N. (external author)
  •   Dray, Anne (external author)
  •   Grafton, R. Quentin (external author)
  •   Newth, David (external author)
  •   Kompas, Tom (external author)

Publication Date


  • 2008

Citation


  • Perez, P., Ratna, N. N., Dray, A., grafton, Q., Newth, D. & Kompas, T. (2008). Diffusion and social networks: revisiting medical innovation with agents. In H. Qudrat-Ullah, J. Spector & P. I. Davidsen (Eds.), Complex Decision Making: Theory and Practice (pp. 247-268). Berlin: Springer.

Scopus Eid


  • 2-s2.0-80053116703

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1030&context=smartpapers

Ro Metadata Url


  • http://ro.uow.edu.au/smartpapers/25

Book Title


  • Complex Decision Making: Theory and Practice

Start Page


  • 247

End Page


  • 268

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.

Authors


  •   Perez, Pascal
  •   Ratna, Nazmun N. (external author)
  •   Dray, Anne (external author)
  •   Grafton, R. Quentin (external author)
  •   Newth, David (external author)
  •   Kompas, Tom (external author)

Publication Date


  • 2008

Citation


  • Perez, P., Ratna, N. N., Dray, A., grafton, Q., Newth, D. & Kompas, T. (2008). Diffusion and social networks: revisiting medical innovation with agents. In H. Qudrat-Ullah, J. Spector & P. I. Davidsen (Eds.), Complex Decision Making: Theory and Practice (pp. 247-268). Berlin: Springer.

Scopus Eid


  • 2-s2.0-80053116703

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=1030&context=smartpapers

Ro Metadata Url


  • http://ro.uow.edu.au/smartpapers/25

Book Title


  • Complex Decision Making: Theory and Practice

Start Page


  • 247

End Page


  • 268