We follow the agentization approach and transform the standard-hidden action model introduced by Holmstr\"om  into an agent-based model. Doing so allows us to relax some of the incorporated rather "heroic" assumptions  related to the (i) availability of information about the environment and the (ii) principal's and agent's cognitive capabilities (with a particular focus on their  memory). In contrast to the standard hidden-action model, the principal and the agent are modeled to learn about the environment over time with varying capabilities to process the learned pieces of information. Moreover, we consider different characteristics of the environment. Our analysis focuses on how close and how fast the incentive scheme, which endogenously emerges from the agent-based model, converges to the second-best solution proposed by the standard hidden-action model. Also, we investigate whether a stable solution can emerge from the agent-based model variant. The results show that in stable environments the emergent result can nearly reach the solution proposed by the standard hidden-action model. Surprisingly, the results indicate that turbulence in the environment leads to stability in earlier time periods.
We introduce an agent-based model of delegation relationships between a principal and an agent, which is based on the standard-hidden action model introduced by Holmstr\"om and, by doing so, provide a model which can be used to further explore theoretical topics in managerial economics, such as the efficiency of incentive mechanisms. We employ the concept of agentization, i.e., we systematically transform the standard hidden-action model into an agent-based model. Our modeling approach allows for a relaxation of some of the rather "heroic" assumptions included in the standard hidden-action model, whereby we particularly focus on assumptions related to the (i) availability of information about the environment and the (ii) principal's and agent's cognitive capabilities (with a particular focus on their learning capabilities and their memory). Our analysis focuses on how close and how fast the incentive scheme, which endogenously emerges from the agent-based model, converges to the solution proposed by the standard hidden-action model. Also, we investigate whether a stable solution can emerge from the agent-based model variant. The results show that in stable environments the emergent result can nearly reach the solution proposed by the standard hidden-action model. Surprisingly, the results indicate that turbulence in the environment leads to stability in earlier time periods.
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follow the agentization approach and transform
introduce an agent-based model of delegation relationships between a principal and an agent, which is based on
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and, by doing so, provide a model which can be used to further explore theoretical topics in managerial economics, such as the efficiency of incentive mechanisms. We employ the concept of agentization, i.e., we systematically transform the standard hidden-action model
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Doing so allows us to relax
Our modeling approach allows for a relaxation of
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incorporated
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included in the standard hidden-action model, whereby we particularly focus on assumptions
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learning capabilities and their
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In contrast to the standard hidden-action model, the principal and the agent are modeled to learn about the environment over time with varying capabilities to process the learned pieces of information. Moreover, we consider different characteristics of the environment.
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second-best