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.

Asistencia a Educación Preescolar y Desempeño Escolar en el Largo Plazo, Evidencia para Colombia

Reconociendo la importancia que tienen las inversiones durante los primeros años del ciclo de vida, en este documento se estima el efecto de la asistencia a preescolar sobre el desempeño escolar de largo plazo, medido a través del puntaje en las áreas de lenguaje y matemáticas de la prueba Saber 11 en Colombia. Para ello, se realiza la aproximación empírica a través de la metodología de variables instrumentales. Los resultados indican que la asistencia a un año adicional de educación preescolar reduce en 0.121 desviaciones estándar el puntaje obtenido en el área de lenguaje. No obstante, se identifica un efecto diferenciado a partir de variables que dan cuenta del estatus socioeconómico de los individuos.