Akbar Abdollahiasl, Shekoufeh Nikfar, Abbas Kebriaeezadeh, Rasoul Dinarvand, Mohammad Abdollahi defined. Changes in flows and stocks levels can simulate the events. These models can adjust many social and managerial situations easily. Usually mathematical equations define relations between stocks [14, 15]. Here, to develop a decision support system to simulate NDP indicators, we propose 3 main groups of input variables and 4 main groups for output, as shown in Figure 1. The three main groups of input variables are: 1. Common variables such as population and its structure, economics and health situation. 2. Disease variables including prevalence, incidence, burden, duration and treatments. 3. Pharmaceutical market-related variables such as product specification, price, supply chain, promotion and people (4 Ps in marketing: Product, Price, Place, and Promotion). Output variables are NDP indicators including availability, affordability, quality, and rationality indices. The model is supposed to simulate the interactions between input variables and predict changes in output variables that should have some specification: 1) It should be multi-variable to multi-variable, having the capability to connect many inputs to some output variables. 2) It doesn’t need so many data for training. 3) The model has to be as simplest as possible. Based on the specifications and limitations of mentioned models, a combination of the system dynamics models and econometrics can simulate the situations of the decision making process in NDAs in the environment of the pharmaceutical market and its variables. This simulation model creates an opportunity to evaluate the consequences of each decision in NDAs based on NDP indicators. The model can be implemented in software to help as a decision support system in reduction of the risk of decision making in uncertain situations. R e f e r e n c e s 1. Nikfar S, Kebriaeezadeh A, Majdzadeh R, Abdollahi M. Monitoring of national drug policy (NDP) and its standardized indicators; conformity to decisions of the national drug selecting committee in Iran. BMC Int Health Hum Rights 2005; 5: 5. 2. Abdollahiasl A, Nikfar S, Abdollahi M. Pharmaceutical market and health system in the Middle Eastern and Central Asian countries: time for innovations and changes in policies and actions. Arch Med Sci 2011; 7: 365-7. 3. Nikfar S, Khatibi M, Abdollahi-Asl A, Abdollahi M. Cost and utilization study of antidotes: an Iranian experience. Int J Pharmacol 2011; 7: 46-9. 4. Abdollahiasl A, Kebriaeezadeh A, Nikfar S, Farshchi A, Ghiasi G, Abdollahi M. Patterns of antibiotic consumption in Iran during 2000-2009. Int J Antimicrob Agents 2011; 37: 489-90. 5. Kebriaeezadeh A, Abdollahi M. Iran seeks to boost domestic pharma production. Scrip 2002; 2695: 16. 6. Using indicators to measure country pharmaceutical situations, Fact Book on WHO Level I and Level II monitoring indicators, World Health Organization, 2006. 7. The world medicines situation, World Health Organization, 2004. 8. White F, Nanan D. Community health case studies selected from developing and developed countries- common principles for moving from evidence to action. Arch Med Sci 2008; 4: 358-63. 9. Weinstein MC, Toy EL, Sandberg EA, et al. Modeling for health care and other policy decisions: uses, roles, and validity. Value Health 2001; 4: 348-61. 10. Davis P, Lay-Yee R, Pearson J. Using micro-simulation to create a synthesised data set and test policy options: the case of health service effects under demographic ageing. Health Policy 2010; 97: 267-74. 11. Schlessinger L, Eddy DM. Archimedes: a new model for simulating health care systems – the mathematical formulation. Biomed Inform 2002; 35: 37-50. 12. Mullahy J. Econometric modeling of health care costs and expenditures a survey of analytical issues and related policy considerations. Med Care 2009; 47 (7 Suppl 1): S104-8. 13. DiRusso SM, Sullivan T, Holly C, Cuff SN, Savino J. An artificial neural network as a model for prediction of survival in trauma patients: validation for a regional trauma area. J Trauma 2000; 49: 212-20. 14. Galea S, Hall C, Kaplan GA. Social epidemiology and complex system dynamic modelling as applied to health behaviour and drug use research. Int J Drug Policy 2009; 20: 209-16. 15. McDonnell G, Heffernan M, Faulkner A. Using system dynamics to analyse health system performance within the WHO framework in international system dynamics Pty Ltd, 2005. 746 Arch Med Sci 5, October / 2011