开放环境下的群智决策：概念、挑战及引领性技术

doi:10.11959/j.issn.2096-6652.202219

Abstract

Abstract:

Research on collective decision-making is based on new decision-making theories and their methods are driven by group participation, human-computer interaction, and big data, to realize complex problem solving and intelligent decision-making in an open environment.However, the open environment is of high openness, complex interaction, and emerging behaviors, making collective decision-making face the challenges of difficult incentive mechanism design, uncontrollable decision-making individuals, diverse decision-making environments, and highly complex decosion-making information.Based on the era background of collective decision-making in an open environment, a new decision-making paradigm was proposed, its conceptual connotation, main challenges, and leading technologies for effective implementation of collective decision-making were sorted out.The successful cases of collective decision-making were analyzed, in order to support the application of new decision-making paradigms in the fields of economy, medical care, and people’s livelihood, and to promote progress and changes in the corresponding fields.

Key words: open environment, collective decision-making, incentive, emerge, credibility, complexity, uncertainty

CLC Number:

TP18

Xueqi CHENG, Bingbing XU, Qi CAO, et al. Collective decision-making in open environment: concepts, challenges, and leading technologies[J]. Chinese Journal of Intelligent Science and Technology, 2022, 4(1): 45-54.

Figures/Tables 2

References 39

[1]	HOWE J . The rise of crowdsourcing[J]. Wired, 2006,14(6): 176-183.
[2]	MALONE T , BERNSTEIN M S . Handbook of collective intelligence[M]. Cambridge: MIT Press, 2015.
[3]	LI W , WU W J , WANG H M ,et al. Crowd intelligence in AI 2.0 era[J]. Frontiers of Information Technology ＆ Electronic Engineering, 2017,18(1): 15-43.
[4]	SHEN H W , BARABáSI A L , . Collective credit allocation in science[J]. PNAS, 2014,111(34): 12325-12330.
[5]	Wikipedia Contributors. Wikipedia[Z]. 2022.
[6]	SUN X Q , SHEN H W , CHENG X Q . Trading network predicts stock price[J]. Scientific Reports, 2014,4:3711.
[7]	LYU S S , OUYANG W T , WANG Y Q ,et al. What we vote for? Answer selection from user expertise view in community question answering[C]// Proceedings of World Wide Web Conference. New York:ACM Press, 2019: 1198-1209.
[8]	KITTUR A , CHI E H , SUH B . Crowdsourcing user studies with Mechanical Turk[C]// Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.[S.l.:s.n.], 2008: 453-456.
[9]	QUINN A J , BEDERSON B B . Human computation:a survey and taxonomy of a growing field[C]// Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.[S.l.:s.n.], 2011: 1403-1412.
[10]	LASECKI W S , SONG Y C , KAUTZ H ,et al. Real-time crowd labeling for deployable activity recognition[C]// Proceedings of the 2013 Conference on Computer Supported Cooperative Work.[S.l.:s.n.], 2013: 1203-1212.
[11]	Wikipedia Contributors. Wikipedia:Netflix prize[Z]. 2022.
[12]	VON AHN L , MAURER B , MCMILLEN C ,et al. reCAPTCHA:human-based character recognition via web security measures[J]. Science, 2008,321(5895): 1465-1468.
[13]	Wikipedia Contributors. Wikipedia:Amazon Mechanical Turk[Z]. 2022.
[14]	Wikipedia Contributors. Wikipedia:figure eight inc[Z]. 2022.
[15]	YU H , SHEN Z Q , MIAO C Y ,et al. A reputation-aware decision-making approach for improving the efficiency of crowdsourcing systems[C]// Proceedings of 2013 International Conference on Autonomous Agents and Multi-agent Systems.[S.l.:s.n.], 2013: 1315-1316.
[16]	ZHANG Y , VAN DER SCHAAR M . Reputation-based incentive protocols in crowdsourcing applications[C]// Proceedings of the IEEE INFOCOM. Piscataway:IEEE Press, 2012: 2140-2148.
[17]	SUNAHASE T , BABA Y , KASHIMA H . Pairwise HITS:quality estimation from pairwise comparisons in creator-evaluator crowdsourcing process[C]// Proceedings of the 31st AAAI Conference on Artificial Intelligence.[S.l.:s.n.], 2017: 977-983.
[18]	LYU S S , OUYANG W T , SHEN H W ,et al. Finding high-quality unstructured submissions in general crowdsourcing tasks[C]// Proceedings of the Information Retrieval.[S.l.:s.n.], 2018: 198-210.
[19]	JEON J , CROFT W B , LEE J H . Finding semantically similar questions based on their answers[C]// Proceedings of the 28th Annual international ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM Press, 2005: 617-618.
[20]	DUAN H , CAO Y , LIN C Y ,et al. Searching questions by identifying question topic and question focus[C]// Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics:Human Language Tchnologies.[S.l.:s.n.], 2008: 156-164.
[21]	CAO X , CONG G , CUI B ,et al. The use of categorization information in language models for question retrieval[C]// Proceedings of the 18th ACM Conference on Information and Knowledge Management. New York:ACM Press, 2009: 265-274.
[22]	CAO X , CONG G , CUI B ,et al. A generalized framework of exploring category information for question retrieval in community question answer archives[C]// Proceedings of the 19th International Conference on World Wide Web. New York:ACM Press, 2010: 201-210.
[23]	JEON J , CROFT W B , LEE J H . Finding similar questions in large question and answer archives[C]// Proceedings of the 14th ACM International Conference on Information and Knowledge Management. New York:ACM Press, 2005: 84-90.
[24]	ZHAO Z , LU H Q , ZHENG V W ,et al. Community-based question answering via asymmetric multi-faceted ranking network learning[C]// Proceedings of the 31st AAAI Conference on Artificial Intelligence.[S.l.:s.n.], 2017: 3532-3538.
[25]	ZHANG X , LI S , SHA L ,et al. Attentive interactive neural networks for answer selection in community question answering[C]// Proceedings of the 31st AAAI Conference on Artificial Intelligence.[S.l.:s.n.], 2017: 3525-3531.
[26]	QIU X , HUANG X . Convolutional neural tensor network architecture for community-based question answering[C]// Proceedings of the 24th International Joint Conference on Artificial Intelligence.[S.l.:s.n.], 2015: 1305-1311.
[27]	GJOKA M , KURANT M , BUTTS C T ,et al. Walking in Facebook:a case study of unbiased sampling of OSNs[C]// Proceedings of the IEEE INFOCOM. Piscataway:IEEE Press, 2010: 1-9.
[28]	WANG T Y , CHEN Y , ZHANG Z B ,et al. Unbiased sampling in directed social graph[C]// Proceedings of the ACM SIGCOMM 2010 Conference on SIGCOMM. New York:ACM Press, 2010: 401-402.
[29]	SALEHI M , RABIEE H R , NABAVI N ,et al. Characterizing twitter with respondent-driven sampling[C]// Proceedings of 2011 IEEE 9th International Conference on Dependable,Autonomic and Secure Computing. Piscataway:IEEE Press, 2011: 1211-1217.
[30]	PARISI G I , KEMKER R , PART J L ,et al. Continual lifelong learning with neural networks:a review[J]. Neural Networks, 2019,113: 54-71.
[31]	LI Z Z , HOIEM D . Learning without forgetting[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018,40(12): 2935-2947.
[32]	REBUFFI S A , KOLESNIKOV A , SPERL G ,et al. iCaRL:incremental classifier and representation learning[C]// Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2017: 5533-5542.
[33]	MIYATO T , DAI A M , GOODFELLOW I . Adversarial training methods for semi-supervised text classification[J]. arXiv preprint, 2016,arXiv:1605.07725.
[34]	GOODFELLOW I J , SHLENS J , SZEGEDY C . Explaining and harnessing adversarial examples[J]. arXiv preprint, 2015,arXiv:1412.6572.
[35]	XIE C H , WANG J Y , ZHANG Z S ,et al. Mitigating adversarial effects through randomization[J]. arXiv preprint, 2018,arXiv:1711.01991.
[36]	LIU X Q , CHENG M H , ZHANG H ,et al. Towards robust neural networks via random self-ensemble[C]// Proceedings of 2018 European Conference on Computer Vision.[S.l.:s.n.], 2018: 369-385.
[37]	VINCENT P , LAROCHELLE H , BENGIO Y ,et al. Extracting and composing robust features with denoising autoencoders[C]// Proceedings of the 25th International Conference on Machine Learning.[S.l.:s.n.], 2008: 1096-1103.
[38]	程学旗, 梅宏, 赵伟 ,等. 数据科学与计算智能:内涵、范式与机遇[J]. 中国科学院院刊, 2020,35(12): 1470-1481.
	CHENG X Q , MEI H , ZHAO W ,et al. Data science and computing intelligence:concept,paradigm,and opportunities[J]. Bulletin of Chinese Academy of Sciences, 2020,35(12): 1470-1481.
[39]	李国杰, 程学旗 . 大数据研究:未来科技及经济社会发展的重大战略领域:大数据的研究现状与科学思考[J]. 中国科学院院刊, 2012,27(6): 647-657.
	LI G J , CHENG X Q . Research status and scientific thinking of big data[J]. Bulletin of Chinese Academy of Sciences, 2012,27(6): 647-657.

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