Mixed oligopoly in education

This paper studies oligopolistic competition in education markets when schools can be private and public and when the quality of education depends on ìpeer groupî e§ects. In the Örst stage of our game schools set their quality and in the second stage they Öx their tuition fees. We examine how the (subgame perfect Nash) equilibrium allocation (qualities, tuition fees and welfare) is a§ected by the presence of public schools and by their relative position in the quality range. When there are no peer group e§ects, e¢ ciency is achieved when (at least) all but one school are public. In particular in the two school case, the impact of a public school is spectacular as we go from a setting of extreme di§erentiation to an e¢ cient allocation. However, in the three school case, a single public school will lower welfare compared to the private equilibrium. We then introduce a peer group e§ect which, for any given school is determined by its student with the highest ability. These PGE do have a signiÖcant impact on the results. The mixed equilibrium is now never e¢ cient. However, welfare continues to be improved if all but one school are public. Overall, the presence of PGE reduces the e§ectiveness of public schools as regulatory tool in an otherwise private education sector.

An artificial economy based on reinforcement learning and agent based modeling

In this paper, we employ techniques from artificial intelligence such as reinforcement learning and agent based modeling as building blocks of a computational model for an economy based on conventions. First we model the interaction among firms in the private sector. These firms behave in an information environment based on conventions, meaning that a firm is likely to behave as its neighbors if it observes that their actions lead to a good pay off. On the other hand, we propose the use of reinforcement learning as a computational model for the role of the government in the economy, as the agent that determines the fiscal policy, and whose objective is to maximize the growth of the economy. We present the implementation of a simulator of the proposed model based on SWARM, that employs the SARSA(λ) algorithm combined with a multilayer perceptron as the function approximation for the action value function.