Motivating Effort with Information about Future Rewards
This paper studies the optimal mechanism to motivate effort in a dynamic principal-agent model without transfers. An agent is engaged in a task with uncertain future rewards and can shirk irreversibly at any time. The principal knows the reward of the task and provides information to the agent over time in order to motivate effort. We derive the optimal information policy in closed form and thus identify two conditions, each of which guarantees that delayed disclosure is valuable. First, if the principal is impatient compared to the agent, she prefers the front-loaded effort schedule induced by delayed disclosure. In a stationary environment, delayed disclosure is beneficial if and only if the principal is less patient than the agent. Second, if the environment makes the agent become pessimistic over time in absence of any information disclosure, then providing delayed news can counteract this downward trend in the agent's belief and encourage the agent to work longer. Notably, the level of patience remains a crucial determinant of the optimal policy structure.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Evidence gathering under competitive and noncompetitive rewards
Reward schemes may affect not only agents' effort, but also their incentives to gather information to reduce the riskiness of the productive activity. In a laboratory experiment using a novel task, we find that the relat…
Counterfactual Credit Assignment in Model-Free Reinforcement Learning
Credit assignment in reinforcement learning is the problem of measuring an action's influence on future rewards. In particular, this requires separating skill from luck, i.e. disentangling the effect of an action on rewa…
counterfactualmodelreinforcement-learningReinforcement Learning+1Model-Free Counterfactual Credit Assignment
Credit assignment in reinforcement learning is the problem of measuring an action’s influence on future rewards. In particular, this requires separating \emph{skill} from \emph{luck}, ie.\ disentangling the effect of an…
counterfactualmodelvalidCutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccination
The COVID-19 pandemic exposed significant weaknesses in the healthcare information system. The overwhelming volume of misinformation on social media and other socioeconomic factors created extraordinary challenges to mot…
MisinformationStance DetectionText ClassificationTopic ModelsLinguistic communication as (inverse) reward design
Natural language is an intuitive and expressive way to communicate reward information to autonomous agents. It encompasses everything from concrete instructions to abstract descriptions of the world. Despite this, natura…