Policy Optimisation - System Dynamics Models with GAs

System dynamics models provide a detailed mathematical description of the complex interactions between numerous variables. Software packages, such as iThink and Vensim, allow policy makers to easily adapt, implement and explore these models. By testing various scenarios through an analysis of the model with different parameter values it is possible to investigate and visualise the long-term effects of changes that could be implemented over the short-term. Genetic algorithms (GA) are shown to provide an efficient and accurate method for identifying optimal scenarios from among the vast number of possible scenarios that are available. The combination of the GA parameter search and human intuition can be utilised to arrive at better strategies for government policy. This approach to optimisation is demonstrated using a model of the malaria-control program in Bolivia [1-2].

Malaria Optimisation The annual parasitic index (API) provides a measure of the prevalence of malaria for each year. API is defined as 1,000 times the number of confirmed cases as a fraction of the population at moderate and high risk. A comparison of the 1998-2001 policy, the human-optimised model and the GA-optimised model demonstrated that the GA approach can outperform the others in terms of providing more accurate results and being more efficient (see figure to the left). An analysis of a collection of solutions in the form of investment strategies was used to determine the robustness of the optimal solution. This showed that the GA optimal solution is robust to small changes in the investment allocations.

Files available for download:

ithink2matlab.m	Matlab file for converting from i-think equations to Matlab code
gabudget.m	Matlab file containing the Genetic Algorithm with built-in budget constraint
gamanual.pdf	Operations manual

Further information is available from Patrick McSharry. Suggestions and comments are welcome.

[1] P.E. McSharry (2004), Optimisation of system dynamics models using genetic algorithms. World Bank Report, May 2004.

[2] P.E. McSharry (2004), Phase II: Optimisation of system dynamics models. World Bank Report, June 2004.