Truthful Incentives in Crowdsourcing Tasks using Regret Minimization Mechanisms 使用后悔最小化机制的众包任务中的真实激励
11页1、Truthful Incentives in Crowdsourcing Tasks using Regret Minimization Mechanisms Adish Singla ETH Zurich Zurich Switzerland adish.singlainf.ethz.ch Andreas Krause ETH Zurich Zurich Switzerland krauseaethz.ch ABSTRACT What price should be off ered to a worker for a task in an online labor market?How can one enable workers to ex- press the amount they desire to receive for the task com- pletion? Designing optimal pricing policies and determin- ing the right monetary incentives is central to maximiz
2、ing requesters utility and workers profi ts. Yet, current crowd- sourcing platforms only off er a limited capability to the re- quester in designing the pricing policies and often rules of thumb are used to price tasks. This limitation could result in ineffi cient use of the requesters budget or workers becoming disinterested in the task. In this paper, we address these questions and present mechanisms using the approach of regret minimization in online learning. We exploit a link between procur
3、ement auc- tions and multi-armed bandits to design mechanisms that are budget feasible, achieve near-optimal utility for the re- quester, are incentive compatible (truthful) for workers and make minimal assumptions about the distribution of work- ers true costs. Our main contribution is a novel, no-regret posted price mechanism, BP-UCB, for budgeted procure- ment in stochastic online settings.We prove strong the- oretical guarantees about our mechanism, and extensively evaluate it in simulations
4、 as well as on real data from the Mechanical Turk platform. Compared to the state of the art, our approach leads to a 180% increase in utility. Categories and Subject Descriptors K.4.4 Computers and Society: Electronic Commerce - Payment schemes; H.2.8 Database Management: Data- base applications - Data mining General Terms Algorithms, Economics, Experimentation, Human Factors, Theory Keywords Crowdsourcing, incentive compatible mechanisms, procure- ment auctions, posted prices, regret minimizat
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