Learning in a Hiring Logic and Optimal Contracts - Jose Iranildo Sales (IEEE, 2021)
This paper examines a hiring logic problem in which all players involved in this game are exposed to scenarios where they can learn from the changes and these modifications influence their preferences; consequently, their decision-making differs from the classical agency theory proposed by [1]. Therefore, how this new learning approach of the agents involved in the delegation of activities changes the methodology of the hiring logic. Concepts such as nonlinear preferences, partial understanding of performances by repetition, and economic cycles of employability are introduced into the classical model, bringing a series of significant changes in the structuring of the game according to the perception and knowledge of the agents involved in the model. As a result, the model indicates that there is a different way to understand hiring logic using the principal-agent model, in which the optimal contract is adapted for learning agents, due to the natural change of behavior by changing perception and preferences in the game.