Peer prediction incentive mechanisms for crowdsourcing are generally limited to eliciting samples from categorical distributions. Prior work on extending peer prediction to arbitrary distributions has largely relied on assumptions on the structures of the distributions or known properties of the data providers. We introduce a novel class of incentive mechanisms that extend peer prediction mechanisms to arbitrary distributions by replacing the notion of an exact match with a concept of neighborhood matching. We present conditions on the belief updates of the data providers that guarantee incentive-compatibility for rational data providers, and admit a broad class of possible reasonable updates.

翻译：众包中的同伴预测激励机制通常仅限于从分类分布中获取样本。先前将同伴预测推广至任意分布的研究主要依赖于对分布结构或数据提供者已知属性的假设。我们提出了一类新颖的激励机制，通过将精确匹配的概念替换为邻域匹配概念，将同伴预测机制扩展到任意分布。我们给出了数据提供者信念更新的条件，这些条件可保证理性数据提供者的激励兼容性，并支持一类广泛的合理更新。