Eliciting Expertise without Verification
Eliciting Expertise without Verification
复制标题
未经验证而获取专业知识
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
G. Schoenebeck
中科院分区：
文献类型：
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作者：
Yuqing Kong;G. Schoenebeck
众包的一个核心问题是如何从代理人那里获得专业知识。当答案无法直接验证时，这就更加困难了。一个关键的挑战是，当复杂的代理人认为他们的回报将基于与其他代理人的比较时，他们可能会在战略上隐瞒努力或信息，而其他代理人的报告可能会由于缺乏努力或专业知识而忽略这些信息。我们的工作为这种设置定义了一个自然模型，该模型基于更复杂的代理知道不那么复杂的代理的信念的假设。然后我们为这个设置提供一个机制设计框架。从这个框架中，我们设计了几个新的机制，用于单个和多个任务设置，(1)鼓励智能体投入努力并诚实地提供信息；(2)在agent是理性的情况下，输出正确的信息“层次结构”。
A central question of crowdsourcing is how to elicit expertise from agents. This is even more difficult when answers cannot be directly verified. A key challenge is that sophisticated agents may strategically withhold effort or information when they believe their payoff will be based upon comparison with other agents whose reports will likely omit this information due to lack of effort or expertise. Our work defines a natural model for this setting based on the assumption that more sophisticated agents know the beliefs of less sophisticated agents. We then provide a mechanism design framework for this setting. From this framework, we design several novel mechanisms, for both the single and multiple tasks settings, that (1) encourage agents to invest effort and provide their information honestly; (2) output a correct "hierarchy" of the information when agents are rational.