Bribery Dynamics: An agent-based approach with evolutionary learning

This paper studies the dynamics of bribery that occurs between citizens and bureaucrats when the formers go to the laters in order to aquire a public service that requires an administrative procedure. The study is based in an agent-based model. Citizens adapt their bribes levels through social learning. Bureaucrats develop their decisions through both individual and social learning mechanisms. The act of bribing can be caught and punish with certain probability, provoking the agents to inccur in different types of costs. Unlike most agent-based models of corruption, the penalization for bureacrats consists on a loss of utlility rathaer than jail time, or suspension from the game. This is intended to be a representative condition of the incentives structure of many countries in which this type of bribery is common. Bribing also generates a social costs among the population of citizens. This work is focused in two objectives: replicate the notion of bribery stable levels as a “social convention” through evolutionary social learning, and to compare two scenarios for hypothesis testing. The experiment testes the idea that societies with disparities in the social cost of corruption across the population, bribery remains higher than in those with flat social cost distribution. Furthermore, the former will be better off than the later. The model accomplishes both objectives, and provides useful insights about the direction and implementation of anti-bribery public policies.