经济推荐系统
Economic Recommendation Systems
摘要：
在在线探索和利用文献中，作为机器学习的核心，中央规划器面临着一组备选方案，每个方案都会产生一些未知的回报。计划者的目标是通过实验尽快学习最优方案。在这个模型中，一个典型的假设是计划者完全控制实验的设计和实施。当实验是由一个社会的自我激励的代理人实施的计划者只能建议实验，但没有权力来执行它。Kremer等人（JPE，2014）介绍了第一个研究探索和利用计划，占代理人的激励。在他们的模型中，隐含地假设代理人彼此看不到也不通信。他们的主要结果是对最佳勘探和开发方案的表征。在这项工作中，我们扩展了Kremer等人（JPE，2014），通过添加一个社交网络层，根据该层，代理可以观察彼此。事实证明，当Kremer等人（JPE，2014）提出的方案中考虑到可观测性时，不再具有激励相容性。在我们的主要结果中，我们提供了一个严格的约束有多少其他代理可以观察到每个代理，仍然有一个激励兼容的算法和渐近最优的结果。更技术性地，对于具有N个代理的设置，其中度大于N^alpha的节点的数量由N^beta限定并且2*alpha+beta < 1，我们构造激励相容的渐近最优机制。2*alpha+beta < 1的界限是紧的。
英文摘要：
In the on-line Explore and Exploit literature, central to Machine Learning, a central planner is faced with a set of alternatives, each yielding some unknown reward. The planner's goal is to learn the optimal alternative as soon as possible, via experimentation. A typical assumption in this model is that the planner has full control over the experiment design and implementation. When experiments are implemented by a society of self-motivated agents the planner can only recommend experimentation but has no power to enforce it. Kremer et al (JPE, 2014) introduce the first study of explore and exploit schemes that account for agents' incentives. In their model it is implicitly assumed that agents do not see nor communicate with each other. Their main result is a characterization of an optimal explore and exploit scheme. In this work we extend Kremer et al (JPE, 2014) by adding a layer of a social network according to which agents can observe each other. It turns out that when observability is factored in the scheme proposed by Kremer et al (JPE, 2014) is no longer incentive compatible. In our main result we provide a tight bound on how many other agents can each agent observe and still have an incentive-compatible algorithm and asymptotically optimal outcome. More technically, for a setting with N agents where the number of nodes with degree greater than N^alpha is bounded by N^beta and 2*alpha+beta < 1 we construct incentive-compatible asymptotically optimal mechanism. The bound 2*alpha+beta < 1 is shown to be tight.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
