一種面向眾包的基于信譽(yù)值的激勵(lì)機(jī)制
doi: 10.11999/JEIT151095 cstr: 32379.14.JEIT151095
基金項(xiàng)目:
國家自然科學(xué)基金(61302078, 61372108),國家自然科學(xué)基金創(chuàng)新研究群體科學(xué)基金(61121061),北京高等學(xué)校青年英才計(jì)劃項(xiàng)目(YETP0476)
Reputation-based Incentive Mechanisms in Crowdsourcing
Funds:
The National Natural Science Foundation of China (61302078, 61372108), The Funds for Creative Research Groups of China (61121061), Beijing Higher Education Young Elite Teacher Project (YETP0476)
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摘要: 眾包是互聯(lián)網(wǎng)帶來的一種分布式問題解決模式。然而,由于工作者和任務(wù)發(fā)布者具有自私特性并且致力于獲得自身效益的最大化,使得在眾包應(yīng)用中,存在內(nèi)部的激勵(lì)問題。該文主要完成以下工作:首先,基于重復(fù)博弈,提出一種基于信譽(yù)值的激勵(lì)模型,用于激勵(lì)理性工作者高質(zhì)量地完成任務(wù);其次,該激勵(lì)模型中同時(shí)設(shè)置了懲罰機(jī)制,將針對惡意工作者做出相應(yīng)懲罰。仿真結(jié)果表明,即使在自私工作者比例為0.2的條件下,只要合理選擇懲罰參數(shù),均可有效激勵(lì)理性工作者的盡力工作,眾包平臺的整體性能可以提升至90%以上。Abstract: Crowdsourcing is a new distributed problem solving pattern brought by the Internet. However, intrinsic incentive problems reside in crowdsourcing applications as workers and requester are selfish and aim to maximize their own benefit. In this paper, the following key contributions are made. A reputation-based incentive model is designed using repeated game theory, based on thorough analysis for current research on reputation and incentive mechanism; and a punishment mechanism is established to counter selfish workers. The experiment results show that the new established model can efficiently motivate the rational workers and counter the selfish ones. By setting punishment parameters appropriately, the overall performance of crowdsourcing system can be improved up to 90%, even if the fraction of selfish workers is 20%.
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Key words:
- Crowdsourcing system /
- Incentive mechanisms /
- Punishment mechanisms /
- Repeated game
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