具有内生熟练度的众包判断诱导
Crowdsourced Judgement Elicitation with Endogenous Proficiency
摘要：
众包现在被广泛用于用一些非专家的综合评价取代专家权威的判断，应用范围从对在线内容进行评级和分类到通过同行评分评估大规模开放在线课程中的学生作业。在直接监测不可行的情况下，一个关键问题是激励“人群”中的代理人努力做出良好的评价，并如实报告他们的评价。这导致了一个新的家庭的信息引出问题与不可观察的地面真相，代理的熟练程度-概率，她正确地评估潜在的地面真相-是内生地决定了她的战略选择多少努力投入到任务中。我们的主要贡献是一个简单的，新的，二进制信息引出多个任务的机制时，代理有内生profitness，具有以下属性：（i）最大努力，其次是真实的报告观察是一个纳什均衡。(ii)这是所有代理人都有最大收益的均衡，即使代理人有不同的最大收益，可以使用混合策略，并且可以为每个任务选择不同的策略。我们的信息启发机制只需要最小的界限先验，要求代理只报告自己的评价，并不需要任何条件上的分歧数量的代理报告每个任务，以实现其激励属性。我们的机制背后的主要思想是使用存在的多个任务和评级，以确定和惩罚低努力协议：该机制奖励代理同意一个“参考”评分员的任务，但也惩罚盲目的协议减去一个统计术语设计，使代理获得奖励，只有当他们把努力投入到他们的意见。
英文摘要：
Crowdsourcing is now widely used to replace judgement by an expert authority with an aggregate evaluation from a number of non-experts, in applications ranging from rating and categorizing online content to evaluation of student assignments in massively open online courses via peer grading. A key issue in these settings, where direct monitoring is infeasible, is incentivizing agents in the `crowd' to put in effort to make good evaluations, as well as to truthfully report their evaluations. This leads to a new family of information elicitation problems with unobservable ground truth, where an agent's proficiency- the probability with which she correctly evaluates the underlying ground truth- is endogenously determined by her strategic choice of how much effort to put into the task. Our main contribution is a simple, new, mechanism for binary information elicitation for multiple tasks when agents have endogenous proficiencies, with the following properties: (i) Exerting maximum effort followed by truthful reporting of observations is a Nash equilibrium. (ii) This is the equilibrium with maximum payoff to all agents, even when agents have different maximum proficiencies, can use mixed strategies, and can choose a different strategy for each of their tasks. Our information elicitation mechanism requires only minimal bounds on the priors, asks agents to only report their own evaluations, and does not require any conditions on a diverging number of agent reports per task to achieve its incentive properties. The main idea behind our mechanism is to use the presence of multiple tasks and ratings to identify and penalize low-effort agreement: the mechanism rewards agents for agreeing with a `reference' rater on a task but also penalizes for blind agreement by subtracting out a statistic term designed so that agents obtain reward only when they put effort into their observations.
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。
