In this paper, we examine the sensitivity of the results of an earlier paper which presented and analyzed a dynamic game model of a monetary union with coalitions between governments (fiscal policy makers) and a common central bank (monetary policy maker). Here we examine alternative values of the parameters of the underlying model to show how the earlier results depend on the numerical parameter values chosen, which were obtained by calibration instead of econometric estimation. We demonstrate that the main results are qualitatively the same as in the original model for plausible smaller and larger values of the parameters. For the few cases where they differ, we interpret the deviations in economic terms and illustrate the policies and their macroeconomic effects resulting from the change to the parameter under consideration for one of these cases.

This research introduces the Multi-Objective Liver Cancer Algorithm (MOLCA), a novel approach inspired by the growth and proliferation patterns of liver tumors. MOLCA emulates the evolutionary tendencies of liver tumors, leveraging their expansion dynamics as a model for solving multi-objective optimization problems in engineering design. The algorithm uniquely combines genetic operators with the Random Opposition-Based Learning (ROBL) strategy, optimizing both local and global search capabilities. Further enhancement is achieved through the integration of elitist non-dominated sorting (NDS), information feedback mechanism (IFM) and Crowding Distance (CD) selection method, which collectively aim to efficiently identify the Pareto optimal front. The performance of MOLCA is rigorously assessed using a comprehensive set of standard multi-objective test benchmarks, including ZDT, DTLZ and various Constraint (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design problems like Brushless DC wheel motor, Safety isolating transformer, Helical spring, Two-bar truss and Welded beam. Its efficacy is benchmarked against prominent algorithms such as the non-dominated sorting grey wolf optimizer (NSGWO), multiobjective multi-verse optimization (MOMVO), non-dominated sorting genetic algorithm (NSGA-II), decomposition-based multiobjective evolutionary algorithm (MOEA/D) and multiobjective marine predator algorithm (MOMPA). Quantitative analysis is conducted using GD, IGD, SP, SD, HV and RT metrics to represent convergence and distribution, while qualitative aspects are presented through graphical representations of the Pareto fronts. The MOLCA source code is available at: https://github.com/kanak02/MOLCA.

In this paper we analyze dynamic interactions in a monetary union with three fiscal players (the governments of the countries concerned) and a common central bank in the presence of exogenous shocks. The model is calibrated for the euro area and includes a fiscally more solid core block denoted as country 1 as well as a fiscally less solid periphery block represented by countries 2 and 3. Introducing two periphery countries allows us to capture different attitudes of the periphery countries towards the goal of sustainable fiscal performance. Moreover, different coalition scenarios are modelled in this study including a fiscal union, a coalition of periphery countries and a coalition of fiscal-stability oriented countries. The exogenous shocks are calibrated in such a way as to describe the last major crises in the euro area, namely the financial crisis, the European sovereign debt crisis, the Covid-19 crisis, and the Ukraine war (energy price) crisis. Using the OPTGAME algorithm we calculate a cooperative Pareto and non-cooperative feedback Nash equilibrium solutions for the modelled scenarios. The fully cooperative solution yields the best results. The different non-cooperative scenarios allow insights into the underlying trade-off between economic growth, price stability and fiscal stability.