Trust and adaptive learning in implicit contracts

Abstract

Trust is a phenomenon that still is quite rarely investigated in agency theory. According to a common intuitive reasoning, trust should develop over time and it should evolve even in finite implicit-contract relationships. However, if the contracting parties are fully rational, theory cannot explain this. We therefore extend the standard model and develop a model of a finite relationship where the principal promises to pay a voluntary period-by-period bonus if the agent has worked according to the implicit agreement. The agent is boundedly rational and unable to foresee the principal’s future bonus decisions. The principal is, with some probability, honest and pays a promised bonus even in situations where ex-post cheating would be optimal. Based on the agent’s adaptive learning process, we show how trust evolves depending on the principal’s bonus-payment strategy. Depending on different levels of the agent’s bounded rationality, we derive the principal’s optimal pure strategy as part of a unique equilibrium. In an extension we show that the results are robust if the agent has bounded recall. The optimal strategy pattern mirrors a subset of trigger strategies which is exogenous in the standard model. Our findings imply that subjective incentives are more effective with increasing tenureof employees, or, that the optimal level of trust depends on how fast work environments change.