R&Dsimulab: a micro-policy simulator for an ex-ante assessment of the effect of public R&D policies

RD (ii) which share of S has the agency to provide to each firm selected for support. Thus, the agency comes up with two optimal solutions: (i) the N1 (out of N) selected companies; the optimal allocation of the subsidy S within the N1 selected companies. b. Companies’ behaviour Companies choose the level of R that maximizes the profit. Thus, the optimal R is the one equalizing the marginal rate of return and the marginal capital cost of doing RD (ii) on the other hand, different network topologies could provide different R&D performance. Therefore, running a series of simulations under different policy scenarios can provide some guidance to detect the emerging properties in the R&D effect’s pattern, especially when one considers specific model’s parameterizations. R&Dsimulab uses Monte Carlo methods to provide sound conclusions about simulation results. Are specific configurations of the network more likely to produce larger R&D effect than other types of settings? In order to answer questions like this, we run a number of R&Dsimulab simulation exercises. For example, one could be interested in identifying whether, ceteris paribus, a quasi-random network is or is not more conducive to higher levels of R&D than, for instance, networks characterized by the emergence of specific nodes playing as hubs. It may thus be interesting to assess whether the policy effect on R will show an increasing or decreasing pattern as a function of the network’s “hubness”. Other experiments could also include the assessment of policy effect when other significant network parameters are changed or when one considers different network topologies, such as “scale-free” or “small-world” networks. Moreover, once a measure of the actual companies’ network is available and an empirical calibration of the model’s parameters achieved, one may also provide an assessment of the impact of the R&D support policy on a real study context, thus using R&Dsimulab as a tool for an effective ex-ante evaluation of the R&D policy considered.