Computational implementation

Following a theoretical analysis of the scope Nash implementation for given mechanism, we study formal framework computational identification implementability. We provide tools in finite environments. In particular, supply Python codes that identify (i) domain preferences allows by (ii) maximal mechanism implements Pareto efficiency, (iii) all consistent collections sets social choice correspondence (SCC), existence which is necessary condition this SCC, and (iv) check whether some well-known sufficient conditions hold SCC. Our results exhibit with an SCC enables planner to design appealing mechanisms.