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 convention. 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 it 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 government in the economy, as the agent that determines the fiscal policy, and whose objective is to maximize economy growth. We present the implementation of a simulator of the proposed model based on SWARM, that employs the SARSA algorithm combined with a multilayer perceptron as the function approximation for the action value function.