Should the government reward cooperation? Insights from an agent-based model of wealth redistribution

TL;DR

This paper uses an agent-based model to explore how government incentives and information scenarios influence cooperation levels in wealth redistribution, revealing conditions for achieving high cooperation with lower taxes.

Contribution

It introduces an analytical framework for understanding how government bonuses and information accuracy affect cooperation in wealth redistribution models.

Findings

Global scenario enables full cooperation with lower taxes.

Critical bonus needed increases with cooperation in local scenario.

High cooperation achievable when government correctly identifies cooperators.

Abstract

In our multi-agent model agents generate wealth from repeated interactions for which a prisoner's dilemma payoff matrix is assumed. Their gains are taxed by a government at a rate $\alpha$. The resulting budget is spent to cover administrative costs and to pay a bonus to cooperative agents, which can be identified correctly only with a probability $p$. Agents decide at each time step to choose either cooperation or defection based on different information. In the local scenario, they compare their potential gains from both strategies. In the global scenario, they compare the gains of the cooperative and defective subpopulations. We derive analytical expressions for the critical bonus needed to make cooperation as attractive as defection. We show that for the local scenario the government can establish only a medium level of cooperation, because the critical bonus increases with the level of cooperation. In the global scenario instead full cooperation can be achieved once the cold-start problem is solved, because the critical bonus decreases with the level of cooperation. This allows to lower the tax rate, while maintaining high cooperation.