Learning to Incentivize: Results of an Agent-Based Simulation
We study which incentive systems emerge in organizations when self-interested managers collaboratively search for higher level of organizational performance and the headquarter learns about the success of the incentive systems employed. We use an agent-based simulation based on the idea of NK fitness landscapes and, in particular, control for different levels of intra-organizational complexity. The results indicate that for different levels of intra-organizational complexity different incentive system emerge: With lower intra-organizational complexity, in tendency, the less focus is put on firm performance and vice versa. In general, results are in line with related findings of research efforts employing a closed-form analysis based on principal-agent theory - though the model introduced in this paper relies on different (and less heroic) assumptions on agents’ cognitive capabilities which may give reason to some deviations we found.