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Innovation diffusion among heterogeneous agents: exploring complexity with Agent-Based Modelling (ABM)

Chapter


Abstract


  • In this chapter we apply agent-based modelling (ABM) to capture the complexity of the diffusion process depicted in Medical Innovation, the classic study on diffusion of a new drug tetracycline by Coleman, Katz, and Menzel (1966). Based on our previous model with homogenous social agents, Gammanym (Raina et al., 2007), in this chapter we further our analysis with heterogeneous social agents who vary in terms of their degree of predisposition to knowledge. We also explore the impact of stage-dependent degrees of external influence from the change agent, pharmaceutical company in this case. Cumulative diffusion curves suggest that the pharmaceutical company plays a much weaker role in accelerating the speed of diffusion when a diffusion dynamics is explored with complex agents, defined as heterogeneous agents under stage-dependent degrees of external influence. Although our exploration with groups of doctors with different combination of social and professional integration signifies the importance of interpersonal ties, our analysis also reveals that degree of adoption threshold or individual predisposition to knowledge is crucial for adoption decisions. Overall, our approach brings in fresh insights to the burgeoning policy literature exploring complexity, by providing necessary framework for research translation to policy and practice.

Authors


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

Publication Date


  • 2008

Citation


  • Ratna, N. N., Dray, A., Perez, P., Grafton, R. Quentin. & Kompas, T. (2008). Innovation diffusion among heterogeneous agents: exploring complexity with Agent-Based Modelling (ABM). In Y. Shan & A. Yang (Eds.), Applications of Complex Adaptive Systems (pp. 113-141). Hershey, USA: IGI Global.

Book Title


  • Applications of Complex Adaptive Systems

Start Page


  • 113

End Page


  • 141

Abstract


  • In this chapter we apply agent-based modelling (ABM) to capture the complexity of the diffusion process depicted in Medical Innovation, the classic study on diffusion of a new drug tetracycline by Coleman, Katz, and Menzel (1966). Based on our previous model with homogenous social agents, Gammanym (Raina et al., 2007), in this chapter we further our analysis with heterogeneous social agents who vary in terms of their degree of predisposition to knowledge. We also explore the impact of stage-dependent degrees of external influence from the change agent, pharmaceutical company in this case. Cumulative diffusion curves suggest that the pharmaceutical company plays a much weaker role in accelerating the speed of diffusion when a diffusion dynamics is explored with complex agents, defined as heterogeneous agents under stage-dependent degrees of external influence. Although our exploration with groups of doctors with different combination of social and professional integration signifies the importance of interpersonal ties, our analysis also reveals that degree of adoption threshold or individual predisposition to knowledge is crucial for adoption decisions. Overall, our approach brings in fresh insights to the burgeoning policy literature exploring complexity, by providing necessary framework for research translation to policy and practice.

Authors


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

Publication Date


  • 2008

Citation


  • Ratna, N. N., Dray, A., Perez, P., Grafton, R. Quentin. & Kompas, T. (2008). Innovation diffusion among heterogeneous agents: exploring complexity with Agent-Based Modelling (ABM). In Y. Shan & A. Yang (Eds.), Applications of Complex Adaptive Systems (pp. 113-141). Hershey, USA: IGI Global.

Book Title


  • Applications of Complex Adaptive Systems

Start Page


  • 113

End Page


  • 141