Şahin Nicat

Job Market Paper

"Gradualism in a Principal-Agent Interaction" (with Ayça Ebru Giritligil)

This paper theoretically and experimentally analyzes a repeated principal-agent game with varying relative stakes. The principal hires an informed agent to observe the state and take action accordingly. There is a high probability that the principal and the agent have misaligned preferences and the principal does not know the agent's type. Repeated play becomes valuable in such a setting by improving the equilibrium payoff of the principal. The agent has reputation incentives that motivate her to take action matching the true state in the initial periods rather than maximize her own period payoff. We show it is optimal for the principal to start the interaction small and increase the stakes gradually. The agent's reputation incentives are managed so that the reputation evolves slowly. We test these predictions in four treatments via online experiments. Each period receives an equal stake in the first treatment. In the other three treatments, the interaction starts small, and the stakes increase at different speeds. We show that the smaller the interaction starts, the higher the reputation incentives of the agent. More importantly, we show that the principal earns a higher payoff in starting-small (gradualism) treatments than the equal-stakes treatment. We contribute to the literature on gradualism by showing it is a valuable tool to improve equilibrium payoffs in the principal-agent framework with asymmetric information.

Other Research Papers

“Managing Reputation Incentives”

In a repeated interaction, one tries to maximize his/her current payoffs and considers the future implications of the decision. Reputation concerns arise when there is incomplete information regarding the preferences of the counterparts. We analyze the finitely repeated interaction between an agent and a principal where either the agent or the principal sets the allocation of the relative stakes. The agent prefers to start the career path with larger stakes, decreasing the stakes gradually. By starting large, the (good) agent creates an environment where the future interaction becomes less valuable. Hence the incentive to take the right action in the current period prevails reputation concerns. On the other hand, the principal allocates the stakes so that the interaction starts small. By doing so, the principal benefits from the reputation concerns of the agent. The principal updates his belief on the agent through interaction - stakes increase as the agent's reputation rises. The findings highlight the optimal design of a career path under different conditions.

Teaching