Finding the effects of policy interventions is often a difficult matter. For instance, what would be the effect on the employers' productivity if the management knowledge level were improved?
Quantified models are seldom applicable to this type of questions, as so often the required information just is not there. Yet there is a need to get at least a first idea on the effects to expect from a policy intervention, both on size and timing of the effects to expect. The Method to Analyse Relations between Variables using Enriched Loops (MARVEL) may be a proper approach to reach this goal. MARVEL is based on causal loop diagrams to which specific information on the speed and strength of the causal effects is added. It includes several options to model policy interventions and analyse their effects, including the non-expected ones.
The limited information need as compared to a fully quantified model, makes the method relatively easy to use. The payoff of this advantage is a lesser outcome accuracy that must be acceptable for the policy problem at hand. Details on MARVEL can be found in the adjacent paper. MARVELtool is the standard tool to enable this method. It allows defining the model for your problem graphically, to identify the policy options, and to analyse the impact to expect from each policy option. MARVELtool also allows you to find the 'optimal' policy. It can be used for policy development, policy analysis and policy evaluation problems, while the limited accuracy of the MARVEL approach should always be taken into account.