Explore Truthful Incentives for Tasks with Heterogenous Levels of Difficulty in the Sharing Economy
Explore Truthful Incentives for Tasks with Heterogenous Levels of Difficulty in the Sharing Economy
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DOI：
10.24963/ijcai.2019/94
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发表时间：
2019-08
期刊：
影响因子：
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通讯作者：
Pengzhan Zhou;Xin Wei;Cong Wang;Yuanyuan Yang
中科院分区：
文献类型：
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作者：
Pengzhan Zhou;Xin Wei;Cong Wang;Yuanyuan Yang
在共享经济中探索激励措施，以激励用户更好地分配资源。以往的研究建立了一个不可行的激励机制来学习用户的成本分布。然而，他们只考虑一种特殊情况，即所有任务都被视为相同。一般的问题要求找到一个解决方案时，不同的任务的成本变化。在本文中，我们研究这个一般性的问题，考虑一个系统的k级难度。我们提出了两种激励策略，离线和在线实施，并正式推导出它们之间的效用在不同的情况下的比率。我们提出了一个遗憾最小化机制，通过动态调整预算分配和学习用户的成本分布来决定激励。我们的实验表明，效用提高约7倍，节省54%的时间，以满足效用目标相比，以前的作品。
Incentives are explored in the sharing economy to inspire users for better resource allocation. Previous works build a budget-feasible incentive mechanism to learn users' cost distribution. However, they only consider a special case that all tasks are considered as the same. The general problem asks for finding a solution when the cost for different tasks varies. In this paper, we investigate this general problem by considering a system with k levels of difficulty. We present two incentivizing strategies for offline and online implementation, and formally derive the ratio of utility between them in different scenarios. We propose a regret-minimizing mechanism to decide incentives by dynamically adjusting budget assignment and learning from users' cost distributions. Our experiment demonstrates utility improvement about 7 times and time saving of 54% to meet a utility objective compared to the previous works.