Network Monitoring and Punishment in Public Goods Games

Abstract

We report experimental findings on the impact of network structure on decentralized monitoring and punishment in public goods games. In the environments we study, individuals can only directly monitor and punish their immediate neighbors in an exogenously determined network. We examine contributions and punishment decisions in three network environments - a Complete network, a Circle network, and an Asymmetric network. We find that average contributions are lower in the Asymmetric network, but that this result is driven entirely by the player who faces only one potential punisher. We then examine the hypothesis that asymmetry in the network leads to discriminatory punishment across potential targets on the basis of their location. After controlling for targets' contribution decisions, we find instead that punishment is assigned indiscriminately to targets in different network locations. However, the data indicate that some potential targets retaliate against previous punishment more often than others, which may deter the punisher from assigning them harsher (discriminatory) punishment. Thus, we identify an additional complication of asymmetry in the network - that it may lead to more anti-social counter-punishments.