This paper explores reward mechanisms for a query incentive network in which agents seek information from social networks. In a query tree issued by the task owner, each agent is rewarded by the owner for contributing to the solution, for instance, solving the task or inviting others to solve it. The reward mechanism determines the reward for each agent and motivates all agents to propagate and report their information truthfully. In particular, the reward cannot exceed the budget set by the task owner. However, our impossibility results demonstrate that a reward mechanism cannot simultaneously achieve Sybil-proof (agents benefit from manipulating multiple fake identities), collusion-proof (multiple agents pretend as a single agent to improve the reward), and other essential properties. In order to address these issues, we propose two novel reward mechanisms. The first mechanism achieves Sybil-proof and collusion-proof, respectively; the second mechanism sacrifices Sybil-proof to achieve the approximate versions of Sybil-proof and collusion-proof. Additionally, we show experimentally that our second reward mechanism outperforms the existing ones.

翻译：本文探讨了在查询激励网络中，代理从社交网络寻求信息的奖励机制。在任务发起者发出的查询树中，每个代理因对解决方案做出贡献（例如，解决任务或邀请他人解决）而获得任务发起者的奖励。奖励机制决定了每个代理的奖励，并激励所有代理如实传播和报告信息。特别地，奖励不能超过任务发起者设定的预算。然而，我们的不可能性结果表明，奖励机制无法同时实现防女巫攻击（代理无法通过操纵多个虚假身份获利）、防共谋（多个代理无法冒充单个代理以提高奖励）以及其他关键特性。为解决这些问题，我们提出了两种新型奖励机制。第一种机制分别实现了防女巫攻击和防共谋；第二种机制牺牲了防女巫攻击，以近似实现防女巫攻击和防共谋。此外，实验表明，我们的第二种奖励机制优于现有机制。