{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T04:14:45Z","timestamp":1742962485863,"version":"3.40.3"},"publisher-location":"Cham","reference-count":46,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031212024"},{"type":"electronic","value":"9783031212031"}],"license":[{"start":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T00:00:00Z","timestamp":1668211200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T00:00:00Z","timestamp":1668211200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-21203-1_18","type":"book-chapter","created":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T07:35:25Z","timestamp":1668152125000},"page":"297-313","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Does Order Simultaneity Affect the\u00a0Data Mining Task in\u00a0Financial Markets? \u2013 Effect Analysis of\u00a0Order Simultaneity Using Artificial Market"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5883-8250","authenticated-orcid":false,"given":"Masanori","family":"Hirano","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0870-7310","authenticated-orcid":false,"given":"Kiyoshi","family":"Izumi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,12]]},"reference":[{"key":"18_CR1","doi-asserted-by":"publisher","unstructured":"Arjovsky, M., Bottou, L.: Towards principled methods for training generative adversarial networks (2017). https:\/\/doi.org\/10.48550\/arXiv.1701.04862","DOI":"10.48550\/arXiv.1701.04862"},{"key":"18_CR2","doi-asserted-by":"publisher","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN (2017). https:\/\/doi.org\/10.48550\/arXiv.1701.07875","DOI":"10.48550\/arXiv.1701.07875"},{"key":"18_CR3","doi-asserted-by":"publisher","unstructured":"Arthur, W.B., Holland, J.H., LeBaron, B., Palmer, R., Tayler, P.: Asset pricing under endogenous expectations in an artificial stock market. In: The Economy as an Evolving Complex System II, pp. 15\u201344 (1997). https:\/\/doi.org\/10.1201\/9780429496639-2","DOI":"10.1201\/9780429496639-2"},{"issue":"6275","key":"18_CR4","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1126\/science.aad0299","volume":"351","author":"S Battiston","year":"2016","unstructured":"Battiston, S., et al.: Complexity theory and financial regulation: economic policy needs interdisciplinary network analysis and behavioral modeling. Science 351(6275), 818\u2013819 (2016). https:\/\/doi.org\/10.1126\/science.aad0299","journal-title":"Science"},{"key":"18_CR5","doi-asserted-by":"publisher","unstructured":"Byrd, D., Hybinette, M., Hybinette Balch, T., Morgan, J.: ABIDES: towards high-fidelity multi-agent market simulation. In: Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, vol. 12 (2020). https:\/\/doi.org\/10.1145\/3384441.3395986","DOI":"10.1145\/3384441.3395986"},{"issue":"5","key":"18_CR6","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1088\/1469-7688\/2\/5\/303","volume":"2","author":"C Chiarella","year":"2002","unstructured":"Chiarella, C., Iori, G.: A simulation analysis of the microstructure of double auction markets. Quant. Finance 2(5), 346\u2013353 (2002). https:\/\/doi.org\/10.1088\/1469-7688\/2\/5\/303","journal-title":"Quant. Finance"},{"key":"18_CR7","doi-asserted-by":"publisher","unstructured":"Chu, C., Zhmoginov, A., Sandler, M.: CycleGAN, a Master of Steganography (2017). https:\/\/doi.org\/10.48550\/arXiv.1712.02950","DOI":"10.48550\/arXiv.1712.02950"},{"key":"18_CR8","doi-asserted-by":"publisher","unstructured":"Cui, W., Brabazon, A.: An agent-based modeling approach to study price impact. In: Proceedings of 2012 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2012, pp. 241\u2013248 (2012). https:\/\/doi.org\/10.1109\/CIFEr.2012.6327798","DOI":"10.1109\/CIFEr.2012.6327798"},{"key":"18_CR9","doi-asserted-by":"publisher","first-page":"1486","DOI":"10.5555\/2969239.2969405","volume":"28","author":"E Denton","year":"2015","unstructured":"Denton, E., Chintala, S., Szlam, A., Fergus, R.: Deep generative image models using a Laplacian pyramid of adversarial networks. Adv. Neural. Inf. Process. Syst. 28, 1486\u20131494 (2015). https:\/\/doi.org\/10.5555\/2969239.2969405","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"18_CR10","doi-asserted-by":"publisher","unstructured":"Donahue, J., Kr\u00e4henb\u00fchl, P., Darrell, T.: Adversarial Feature Learning (2016). https:\/\/doi.org\/10.48550\/arXiv.1605.09782","DOI":"10.48550\/arXiv.1605.09782"},{"key":"18_CR11","unstructured":"Edmonds, S.M., Bruce: towards good social science. J. Artif. Soc. Soc. Simul. 8(4) (2005). https:\/\/www.jasss.org\/8\/4\/13.html"},{"issue":"7256","key":"18_CR12","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1038\/460685a","volume":"460","author":"JD Farmer","year":"2009","unstructured":"Farmer, J.D., Foley, D.: The economy needs agent-based modelling. Nature 460(7256), 685\u2013686 (2009). https:\/\/doi.org\/10.1038\/460685a","journal-title":"Nature"},{"key":"18_CR13","doi-asserted-by":"publisher","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014). https:\/\/doi.org\/10.1145\/3422622","DOI":"10.1145\/3422622"},{"key":"18_CR14","doi-asserted-by":"publisher","first-page":"5767","DOI":"10.5555\/3295222.3295327","volume":"30","author":"I Gulrajani","year":"2017","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of Wasserstein GANs montreal institute for learning algorithms. Adv. Neural. Inf. Process. Syst. 30, 5767\u20135777 (2017). https:\/\/doi.org\/10.5555\/3295222.3295327","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"18_CR15","doi-asserted-by":"publisher","unstructured":"Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: GANs trained by a two time-scale update rule converge to a local NASH equilibrium. In: Advances in Neural Information Processing Systems, vol. 30 (2017). https:\/\/doi.org\/10.5555\/3295222.3295408","DOI":"10.5555\/3295222.3295408"},{"issue":"4","key":"18_CR16","doi-asserted-by":"publisher","first-page":"75","DOI":"10.3390\/jrfm13040075","volume":"13","author":"M Hirano","year":"2020","unstructured":"Hirano, M., Izumi, K., Shimada, T., Matsushima, H., Sakaji, H.: Impact Analysis of Financial Regulation on Multi-Asset Markets Using Artificial Market Simulations. J. Risk Fin. Manage. 13(4), 75 (2020). https:\/\/doi.org\/10.3390\/jrfm13040075","journal-title":"J. Risk Fin. Manage."},{"key":"18_CR17","doi-asserted-by":"publisher","unstructured":"Hirano, M., Sakaji, H., Izumi, K.: Concept and practice of artificial market data mining platform. In: 2022 IEEE Conference on Computational Intelligence for Financial Engineering Economics (CIFEr), pp. 1\u201310 (2022). https:\/\/doi.org\/10.1109\/CIFEr52523.2022.9776095","DOI":"10.1109\/CIFEr52523.2022.9776095"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Hirano, M., Sakaji, H., Izumi, K.: Policy gradient stock GAN for realistic discrete order data generation in financial markets (2022). https:\/\/doi.org\/10.48550\/arXiv.2204.13338","DOI":"10.2139\/ssrn.4095304"},{"key":"18_CR19","doi-asserted-by":"publisher","unstructured":"Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017). https:\/\/doi.org\/10.1109\/CVPR.2017.632","DOI":"10.1109\/CVPR.2017.632"},{"key":"18_CR20","unstructured":"Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: 6th International Conference on Learning Representations (2018)"},{"key":"18_CR21","doi-asserted-by":"publisher","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401\u20134410 (2019). https:\/\/doi.org\/10.1109\/TPAMI.2020.2970919","DOI":"10.1109\/TPAMI.2020.2970919"},{"key":"18_CR22","unstructured":"Kohda, S., Yoshida, K.: Analysis of high-frequency trading from the viewpoint of tick distance and Execution [in Japanese]. In: Proceedings of the 22nd Meeting of Special Interest Group on Financial Informatics of Japanese Society for Artificial Intelligence (2019). https:\/\/sigfin.org\/?022-02"},{"key":"18_CR23","doi-asserted-by":"publisher","unstructured":"Larsen, A.B.L., S\u00f8nderby, S.K., Larochelle, H., Winther, O.: Autoencoding beyond pixels using a learned similarity metric. In: International Conference on Machine Learning, pp. 1558\u20131566 (2016). https:\/\/doi.org\/10.5555\/3045390.3045555","DOI":"10.5555\/3045390.3045555"},{"key":"18_CR24","doi-asserted-by":"publisher","unstructured":"Leal, S.J., Napoletano, M.: Market stability vs. market resilience: regulatory policies experiments in an agent-based model with low- and high-frequency trading. J. Econ. Behav. Organ. 157, 15\u201341 (2019). https:\/\/doi.org\/10.1016\/j.jebo.2017.04.013","DOI":"10.1016\/j.jebo.2017.04.013"},{"key":"18_CR25","doi-asserted-by":"publisher","unstructured":"Li, D., Chen, D., Goh, J., Ng, S.K.: Anomaly detection with generative adversarial networks for multivariate time series (2018). https:\/\/doi.org\/10.48550\/arXiv.1809.04758","DOI":"10.48550\/arXiv.1809.04758"},{"issue":"01","key":"18_CR26","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1609\/aaai.v34i01.5415","volume":"34","author":"J Li","year":"2020","unstructured":"Li, J., Wang, X., Lin, Y., Sinha, A., Wellman, M.: Generating realistic stock market order streams. AAAI Conf. Artif. Intell. 34(01), 727\u2013734 (2020). https:\/\/doi.org\/10.1609\/aaai.v34i01.5415","journal-title":"AAAI Conf. Artif. Intell."},{"issue":"6719","key":"18_CR27","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1038\/17290","volume":"397","author":"T Lux","year":"1999","unstructured":"Lux, T., Marchesi, M.: Scaling and criticality in a stochastic multi-agent model of a financial market. Nature 397(6719), 498\u2013500 (1999). https:\/\/doi.org\/10.1038\/17290","journal-title":"Nature"},{"key":"18_CR28","doi-asserted-by":"publisher","unstructured":"Mao, X., Li, Q., Xie, H., Lau, R.Y., Wang, Z., Paul Smolley, S.: Least squares generative adversarial networks. In: IEEE International Conference on Computer Vision, pp. 2794\u20132802 (2017). https:\/\/doi.org\/10.1109\/ICCV.2017.304","DOI":"10.1109\/ICCV.2017.304"},{"key":"18_CR29","doi-asserted-by":"publisher","unstructured":"Mirza, M., Osindero, S.: Conditional Generative Adversarial Nets (1784), 1\u20137 (2014). https:\/\/doi.org\/10.48550\/arXiv.1411.1784","DOI":"10.48550\/arXiv.1411.1784"},{"key":"18_CR30","doi-asserted-by":"publisher","unstructured":"Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. In: 6th International Conference on Learning Representations (2018). https:\/\/doi.org\/10.48550\/arXiv.1802.05957","DOI":"10.48550\/arXiv.1802.05957"},{"key":"18_CR31","doi-asserted-by":"publisher","unstructured":"Mizuta, T.: An agent-based model for designing a financial market that works well (2019). https:\/\/doi.org\/10.1109\/SSCI47803.2020.9308376","DOI":"10.1109\/SSCI47803.2020.9308376"},{"issue":"1\u20132","key":"18_CR32","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1002\/isaf.1374","volume":"23","author":"T Mizuta","year":"2016","unstructured":"Mizuta, T., et al.: Effects of price regulations and dark pools on financial market stability: an investigation by multiagent simulations. Intell. Syst. Account. Finance Manage. 23(1\u20132), 97\u2013120 (2016). https:\/\/doi.org\/10.1002\/isaf.1374","journal-title":"Intell. Syst. Account. Finance Manage."},{"key":"18_CR33","doi-asserted-by":"publisher","unstructured":"Naritomi, Y., Adachi, T.: Data augmentation of high frequency financial data using generative adversarial network. In: 2020 IEEE\/WIC\/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), pp. 641\u2013648. IEEE (2020). https:\/\/doi.org\/10.1109\/WIIAT50758.2020.00097","DOI":"10.1109\/WIIAT50758.2020.00097"},{"key":"18_CR34","doi-asserted-by":"publisher","unstructured":"Nowozin, S., Cseke, B., Tomioka, R.: f-GAN: training generative neural samplers using variational divergence minimization. In: 30th International Conference on Neural Information Processing Systems, pp. 271\u2013279 (2016). https:\/\/doi.org\/10.5555\/3157096.3157127","DOI":"10.5555\/3157096.3157127"},{"key":"18_CR35","doi-asserted-by":"publisher","unstructured":"Paddrik, M., Hayes, R., Todd, A., Yang, S., Beling, P., Scherer, W.: An agent based model of the E-Mini S &P 500 applied to flash crash analysis. In: Proceedings of 2012 IEEE Conference on Computational Intelligence for Financial Engineering and Economics, CIFEr 2012, pp. 257\u2013264 (2012). https:\/\/doi.org\/10.1109\/CIFEr.2012.6327800","DOI":"10.1109\/CIFEr.2012.6327800"},{"key":"18_CR36","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks (2015). https:\/\/doi.org\/10.48550\/arXiv.1511.06434"},{"key":"18_CR37","doi-asserted-by":"publisher","unstructured":"Sato, H., Koyama, Y., Kurumatani, K., Shiozawa, Y., Deguchi, H.: U-mart: a test bed for interdisciplinary research into agent-based artificial markets. In: Aruka, Y. (eds) Evolutionary Controversies in Economics, pp. 179\u2013190. Springer, Tokyo (2001). https:\/\/doi.org\/10.1007\/978-4-431-67903-5_13","DOI":"10.1007\/978-4-431-67903-5_13"},{"key":"18_CR38","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1007\/978-3-319-59050-9_12","volume-title":"Information Processing in Medical Imaging","author":"T Schlegl","year":"2017","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 146\u2013157. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_12"},{"key":"18_CR39","unstructured":"Torii, T., et al.: Plham: platform for large-scale and high-frequency artificial market (2016). https:\/\/github.com\/plham\/plham"},{"key":"18_CR40","unstructured":"Torii, T., et al.: PlhamJ (2019). https:\/\/github.com\/plham\/plhamJ"},{"issue":"2","key":"18_CR41","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1007\/s40844-015-0024-z","volume":"12","author":"T Torii","year":"2016","unstructured":"Torii, T., Izumi, K., Yamada, K.: Shock transfer by arbitrage trading: analysis using multi-asset artificial market. Evol. Inst. Econ. Rev. 12(2), 395\u2013412 (2016). https:\/\/doi.org\/10.1007\/s40844-015-0024-z","journal-title":"Evol. Inst. Econ. Rev."},{"issue":"3","key":"18_CR42","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1007\/s10015-017-0368-z","volume":"22","author":"T Torii","year":"2017","unstructured":"Torii, T., Kamada, T., Izumi, K., Yamada, K.: Platform design for large-scale artificial market simulation and preliminary evaluation on the K computer. Artif. Life Robot. 22(3), 301\u2013307 (2017). https:\/\/doi.org\/10.1007\/s10015-017-0368-z","journal-title":"Artif. Life Robot."},{"issue":"3","key":"18_CR43","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/BF00992696","volume":"8","author":"RJ Williams","year":"1992","unstructured":"Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8(3), 229\u2013256 (1992). https:\/\/doi.org\/10.1007\/BF00992696","journal-title":"Mach. Learn."},{"key":"18_CR44","unstructured":"Zenati, H., Foo, C.S., Lecouat, B., Manek, G., Chandrasekhar, V.R.: Efficient GAN-Based Anomaly Detection (2018). https:\/\/doi.org\/10.48550\/arXiv.1802.06222"},{"key":"18_CR45","doi-asserted-by":"publisher","first-page":"400","DOI":"10.1016\/j.procs.2019.01.256","volume":"147","author":"K Zhang","year":"2019","unstructured":"Zhang, K., Zhong, G., Dong, J., Wang, S., Wang, Y.: Stock market prediction based on generative adversarial. Network 147, 400\u2013406 (2019). https:\/\/doi.org\/10.1016\/j.procs.2019.01.256","journal-title":"Network"},{"key":"18_CR46","doi-asserted-by":"publisher","unstructured":"Zhou, X., Pan, Z., Hu, G., Tang, S., Zhao, C.: Stock market prediction on high-frequency data using generative adversarial nets. Math. Prob. Eng. 2018 (2018). https:\/\/doi.org\/10.1155\/2018\/4907423","DOI":"10.1155\/2018\/4907423"}],"container-title":["Lecture Notes in Computer Science","PRIMA 2022: Principles and Practice of Multi-Agent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21203-1_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T07:37:32Z","timestamp":1668152252000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21203-1_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,12]]},"ISBN":["9783031212024","9783031212031"],"references-count":46,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21203-1_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022,11,12]]},"assertion":[{"value":"12 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRIMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Principles and Practice of Multi-Agent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Valencia","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"prima2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prima2022.webs.upv.es\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"100","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"15","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"31% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1 (demo paper)","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}