Risk Aversion and the Pareto Frontier of a Dynamic Principal-Agent Model: An Evolutionary Approximation

In this paper we formulate an infinitely repeated Principal-Agent relationship as a Multi-Objective Optimization problem. We numerically approximate the solution of this model using a Multi-Objective Optimization Evolutionary Algorithm, named RankMOEA, for different values of the Agent's coefficient of relative risk aversion. Our numerical results indicate that as the Agent becomes more risk averse, the Pareto Frontier becomes less concave, the Principal-Agent relationship generates more value for the Agent, and the Principal appears to assume more of the risk sharing regardless of the contract we analyze along the Pareto Frontier.