Policy Incentives for Innovation Diffusion: An Agent-Based Simulation

1. Bankes, S.C.: Agent-based modeling: A revolution? Proceedings of the National Academy of Sciences of the United States of America 99, 7199–7200 (2002)

   Article  Google Scholar 

2. Rogers, E.M.: Diffusion of innovations, 5th edn. Free Press, New York (2003)

   Google Scholar 

3. Bass, F.M.: A New Product Growth for Model Consumer Durables. Management Science 15(5), 215–227 (1969)

   Article  Google Scholar 

4. Abaye, A.R., Babbitt, J., Best, B., Hu, R.Q., Maveddat, P.: Forecasting Methodology and Traffic Estimation for Satellite Multimedia Services. In: Proceedings of IEEE ICC’99, Vancouver, Canada, pp. 1084–1088 (1999)

   Google Scholar 

5. Guidolin, M., Mortarino, C.: Cross-country diffusion of photovoltaic systems: Modelling choices and forecasts for national adoption patterns. Technological Forecasting and Social Change 77(2), 279–296 (2010)

   Article  Google Scholar 

6. Guseo, R., Guidolin, M.: Modelling a dynamic market potential: A class of automata networks for diffusion of innovations. Technological Forecasting and Social Change 76(6), 806–820 (2009)

   Article  Google Scholar 

7. Liu, M., Xiao, Y.: Simulation on the consumer innovation diffusion behavior in the environment of electronic commerce. In: IEEE International Conference on Management of Innovation and Technology (2006)

   Google Scholar 

8. Park, Y.T.: Technology diffusion policy: a review and classification of policy practices. Technology in Society 21(3), 275–286 (1999)

   Article  Google Scholar 

9. Volkery, A., Ribeiro, T.: Scenario planning in public policy: Understanding use, impacts and the role of institutional context factors. Technological Forecasting and Social Change 76(9), 1198–1207 (2009)

   Article  Google Scholar 

10. Georghiou, L., Roessner, D.: Evaluating technology programs: tools and methods. Research Policy 29(4-5), 657–678 (2000)

    Article  Google Scholar 

11. Kim, Y.: Enriching Policy Analysis: The Role of Agent Based Models. In: The 9th Public Management Research Conference, Tucson, Arizona (2007)

    Google Scholar 

12. Zagonel, A.A., Rohrbaugh, J., Richardson, G.P., Andersen, D.F.: Using simulation models to address “what if” questions about welfare reform. Journal of Policy Analysis and Management 23(4), 890–901 (2004)

    Article  Google Scholar 

13. Anderson, J., Chaturvedi, A., Cibulskis, M.: Simulation tools for developing policies for complex systems: Modeling the health and safety of refugee communities. Health Care Management Science 10(4), 331–339 (2007)

    Article  Google Scholar 

14. Berger, T., Schreinemachers, P., Woelcke, J.: Multi-agent simulation for the targeting of development policies in less-favored areas. Agricultural Systems 88(1), 28–43 (2006)

    Article  Google Scholar 

15. Bankes, S.C.: Tools and techniques for developing policies for complex and uncertain systems. Proceedings of the National Academy of Sciences of the United States of America 99(suppl. 3), 7263–7266 (2002)

    Article  Google Scholar 

16. Coad, A., de Haan, P., Woersdorfer, J.S.: Consumer support for environmental policies: An application to purchases of green cars. Ecological Economics 68(7), 2078–2086 (2009)

    Article  Google Scholar 

17. Diamond, D.: The impact of government incentives for hybrid-electric vehicles: Evidence from US states. Energy Policy 37(3), 972–983 (2009)

    Article  Google Scholar 

18. de Haan, P., Mueller, M.G., Scholz, R.W.: How much do incentives affect car purchase? Agent-based microsimulation of consumer choice of new cars - Part II: Forecasting effects of feebates based on energy-efficiency. Energy Policy 37(3), 1083–1094 (2009)

    Article  Google Scholar 

19. Sultan, F., Farley, J.U., Lehmann, D.R.: A meta-analysis of applications of diffusion models. Journal of marketing research 27(1), 70–77 (1990)

    Article  Google Scholar 

Download references