Name
Abstract | ||
|---|---|---|
In principal-agent models, a principal offers a contract to an agent to perform a certain task. The agent exerts a level of effort that maximizes her utility. The principal is oblivious to the agentu0027s chosen level of effort, and conditions her wage only on possible outcomes. In this work, we consider a model in which the principal is unaware of the agentu0027s utility and action space. She sequentially offers contracts to identical agents, and observes the resulting outcomes. We present an algorithm for learning the optimal contract under mild assumptions. We bound the number of samples needed for the principal obtain a contract that is within $epsilon$ of her optimal net profit for every $epsilonu003e0$. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Computer Science and Game Theory | Journal |
Volume | Citations | PageRank |
abs/1811.06736 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alon Cohen | 1 | 11 | 5.28 |
| Moran Koren | 2 | 0 | 1.01 |
| Argyrios Deligkas | 3 | 19 | 7.43 |