Steering economies toward stability in the wake of Covid-19 crisis: insights from mathematical model

This paper sought to examine the impact of policy-maker decisions on economic convergence in a period characterized by turbulence, uncertainty, and complexity, like the COVID-19 pandemic. Recognizing the critical need for structure in times, we used a Markov decision process. This framework has proven invaluable modeling for modeling making decision scenarios where outcomes are determined by actions of decision-makers. First, our study is deeply rooted in Bayesian approach which analyze growth dynamics. Then we used k-means clustering to categorize EU-states into three categories which allows us to analyze the performance of each group. By utilizing MDP model, we examine three political scenarios for decision-makers then basis on the assumptions of our model, we determined the optimal policy for the scenario which maximizes the reward function. We found that effective decision-making is crucial for achieving economic convergence in a post-crisis world. Furthermore, our research indicated that a global and coordinated approach might offer several benefits. The COVID-19 crisis could paradoxically provide an opportunity for poorer states to implement optimal policies they might not have been able to afford under normal circumstances.