Privatization and Restructuring in Concentrated Markets

This paper examines the restructuring of state assets in markets deregulated by privatizations and investment liberalizations. We show that the government has a stronger incentive to restructure than the buyer: A firm restructuring only takes into account how much its own profit will increase. The government internalizes that restructuring increases the sales price not only from the increase in the acquirer’s profit, but also from a reduced profit for the non-acquirer, whose profits decrease due to its rival’s restructuring. We also identify situations where a slow sale can significantly reduce the sales price because of strategic investment and product market effects.Privatization, Asset ownership, Restructuring

Finite Horizon Q-learning: Stability, Convergence, Simulations and an application on Smart Grids

Q-learning is a popular reinforcement learning algorithm. This algorithm has however been studied and analysed mainly in the infinite horizon setting. There are several important applications which can be modeled in the framework of finite horizon Markov decision processes. We develop a version of Q-learning algorithm for finite horizon Markov decision processes (MDP) and provide a full proof of its stability and convergence. Our analysis of stability and convergence of finite horizon Q-learning is based entirely on the ordinary differential equations (O.D.E) method. We also demonstrate the performance of our algorithm on a setting of random MDP as well as on an application on smart grids