Hybrid SD and AB Simulation Experimentation for Policy Decision Making

The novel RAM presented in the previous chapter is applied as a procedure for hybrid quantitative simulation. This chapter provides a practical example of this hybridization procedure for the exploration and evaluation of various pharmaceutical policies, related to medicines prescribing and pricing regulations. The procedure includes the transfer of the qualitative RAM to main SD and ABM components’ functions, that is, stocks and flows of resources and agents, their attributes and their behaviour, including main parameters and behavioural assumptions. The analysis of the pharmaceutical regulation in this chapter presents a number of what if scenarios conceptualized through the RAM approach and performed through the use of a public policy scenario simulator. They have the task to elicit the effects of the regulation tool box and its elements in combination with important contextual regulatory and market competition factors. It extends further the practice of public policy evaluation through simulation, bringing a comprehensive approach and providing interactive learning environments, where decision makers can design and test policies through experimentation. Using simulation helps to illustrate why intendedly rational policies can lead to policy resistance and can support the design and testing of robust interventions, accounting for the counterintuitive nature of policy problems.