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2021ǯ
2020ǯ
2019ǯ
2018ǯ
2017ǯ
2016ǯ
2015ǯ
2014ǯ
2013ǯ
2012ǯ
2011ǯ
2010ǯ
2009ǯ
2008ǯ
2007ǯ
2005ǯ
  • ÏÂÀô ·é, ¼Ò²ñ¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤ò¿Í¹©ÃÎǽ¤Î»î¸³Âæ¤Ë, ¿Í¹©ÃÎǽ³Ø²ñ»ï, 20´¬3¹æ p. 264, 2005.
  • ÏÂÀô ·é, ¿Í¹©»Ô¾ì¤Ë¤è¤Ã¤Æ»Ô¾ì¤òÍý²ò¤·¥Ç¥¶¥¤¥ó¤¹¤ë, ECOFORUM, Åý·×¸¦µæ²ñ, 23´¬ 2¹æ, 20ÊÇ¡Á27ÊÇ,2005.
  • ¼Öë ¹À°ì¡¦»³²¼ Îѱû¡¦ÌîÅÄ ¸Þ½½¼ù¡¦ÏÂÀô ·é¡¦¾¾Èø Ë­, ƻϩ¸òḀ̈ͥåȥ¥¯¤Î¥À¥¤¥Ê¥ß¥¯¥¹¤È·²¥æ¡¼¥¶»Ù±ç, ¿Í¹©ÃÎǽ³Ø²ñ»ï, 20´¬3¹æ p. 296-304, 2005.
  • K. Izumi, S. Nakamura, and K. Ueda: Development of an artificial market model based on a field study, INFORMATION SCIENCES, vol. 170, no. 1, pp. 35-63, 2005.
2004ǯ
  • ÏÂÀô ·é: ¿Í¹©»Ô¾ì¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¤Ë¤è¤ë·ÐºÑÍýÏÀ¤Î¸¡¾Ú, ¥·¥ß¥å¥ì¡¼¥·¥ç¥ó, 23´¬pp. 183-190, 2004.
  • ÏÂÀô ·é, ¿Í¹©»Ô¾ì: »Ô¾ì¸½¾Ý¤Î¥¨¡¼¥¸¥§¥ó¥È¥Ù¡¼¥¹¥â¥Ç¥ë, ·×¬¤ÈÀ©¸æ, ·×¬¼«Æ°À©¸æ³Ø²ñ, Vol.43, No.12, pp.950-955, 2004.
2002ǯ
  • ÏÂÀô ·é, ¿Í¹©»Ô¾ì¤Îºî¤êÊý¡§ ¥ä¥Ã¥³¡¼¤Ë¤Ê¤é¤Ê¤¤¤¿¤á¤Ë, ¥·¥¹¥Æ¥à/À©¸æ/¾ðÊó, 46 ´¬ 9 ¹æ 547 ÊÇ¡Á 555 ÊÇ, 2002.
2001ǯ
  • ÃæÅç ½¨Ç·¡¢¶¶ÅĹÀ°ì¡¢ÏÂÀô ·é¡¢ÌîÅÄ ¸Þ½½¼ù ¾¡¤¾ðÊ󥤥ó¥Õ¥é¤Ë´ð¤Å¤¯¥°¥é¥¦¥ó¥Ç¥£¥ó¥°¤È¤½¤Î±þÍÑ¡¤¥³¥ó¥Ô¥å¡¼¥¿¥½¥Õ¥È¥¦¥§¥¢¡¤18-4¡¤pp.48-56¡¢2001.
  • K. Izumi and K. Ueda, Phase Transition in a Foreign Exchange Market: Analysis Based on an Artificial Market Approach, IEEE Transactions on Evolutionary Computation, Vol.5, No. 5, pp 456-470, 2001.
2000ǯ
  • ÏÂÀô ·é¡¦¿¢ÅÄ °ìÇî, ¿Í¹©»Ô¾ì¥¢¥×¥í¡¼¥Á¤Ë¤è¤ë°ÙÂØ¥·¥Ê¥ê¥ª¤ÎʬÀÏ, ¥³¥ó¥Ô¥å¡¼¥¿¥½¥Õ¥È¥¦¥§¥¢ Vol.17, No. 5, pp 47-54, 2000.
  • ½Ð¸ý ¹°¡¦ÏÂÀô ·é¡¦±öÂô µÈŵ¡¦¹â°Â ½¨¼ù¡¦»ûÌî δͺ¡¦º´Æ£ ¹À¡¦´î¿ °ì, ¿Í¹©»Ô¾ì¤ò¸¦µæ¤¹¤ë¼Ò²ñŪ¤ª¤è¤Ó³ØÌäŪ°ÕµÁ, ¿Í¹©ÃÎǽ³Ø²ñ»ï, Vol.15, No. 6, pp 982-989, 2000.
  • ÏÂÀô ·é¡¦¿¢ÅÄ °ìÇî, ¿Í¹©»Ô¾ìÆþÌç, ¿Í¹©ÃÎǽ³Ø²ñ»ï, Vol.15, No.6, pp 941-950, 2000.
1999ǯ
  • K. Izumi and K. Ueda, ANALYSIS OF DEALERS' PROCESSING FINANCIAL NEWS BASED ON AN ARTIFICIAL MARKET APPROACH, Journal of Computational Intelligence in Finance, Vol. 7, pp. 23-33, 1999.
  • ÏÂÀô ·é¡¦¿¢ÅÄ °ìÇî, ¥³¥ó¥Ô¥å¡¼¥¿¤ÎÃæ¤Î»Ô¾ì: ǧÃε¡¹½¤ò¤â¤Ä¥¨¡¼¥¸¥§¥ó¥È¤«¤é¤Ê¤ë¿Í¹©»Ô¾ì¤Î¹½ÃۤȤ½¤Îɾ²Á, ǧÃβʳØ, 6´¬1¹æ31-43ÊÇ, ¶¦Î©½ÐÈÇ, 1999.

Ãøºî

2021ǯ
2020ǯ
2019ǯ
  • [½ñÀÒ] ¿·Ã«¸µ»Ì¡¦°Ë¿À Ëþ¡¦ËÌÀî Æ©¡¦ÏÂÀô ·é¡¦µÜÀîÂç²ð, Âè6¾Ï ¥Ó¥Ã¥°¥Ç¡¼¥¿¡¦µ¡³£³Ø½¬¤È·ÐºÑʬÀÏ, ±§°æ µ®»Ö, ²ÃǼ δ, ¸¶ À齩, ÅÏÉô ÉÒÌÀ (ÊÔ½¸), ¸½Âå·ÐºÑ³Ø¤ÎĬή 2019, ÅìÍηкѿ·Êó¼Ò, 2019
  • [½ñÀÒ] ÏÂÀô ·é,¿Ûˬ Çîɧ,ÂçÀ¾ Ω¸², ºäÃÏÂÙµª, ¾¾Åç͵¹¯, ÉÔÆ°»º¥Ç¡¼¥¿¤Ø¤Î¥Ç¡¼¥¿²òÀϤȿ͹©ÃÎǽµ»½Ñ¤Î±þÍÑ, ÉÔÆ°»º¥Æ¥Ã¥¯¤ò¹Í¤¨¤ë, pp. 75-90, ¥×¥í¥°¥ì¥¹, 2019.
2018ǯ
2017ǯ
2013ǯ
  • Àîµ×ÊÝ º´µ­, ÏÂÀô ·é, ¡ÖÂè10¾Ï¡¡¶âÍ»»Ô¾ìʬÌî¤Ë¤ª¤±¤ë´íµ¡´ÉÍý¡×in ÈÄÀ¸ À¶ ´Æ½¤, ¡Ö´íµ¡´ÉÍýÊýË¡ÏÀ¤È¤½¤Î±þÍÑ¡×, ¥·¡¼¥¨¥à¥·¡¼½ÐÈÇ, pp. 197-214, 2013.
  • Åŵ¤³Ø²ñ ÊÔ, Åŵ¤³Ø²ñ125ǯ»Ë, ¡Ö¥¨¡¼¥¸¥§¥ó¥È¥·¥ß¥å¥ì¡¼¥·¥ç¥ó¡×, pp. 525-526, Åŵ¤³Ø²ñ, 2013.
2012ǯ
2009ǯ
  • ²¼Â¼À¯»Ì¡¡»³¸ýÃÒɧ, ¼«¸ÊÁÈ¿¥²½¥Ï¥ó¥É¥Ö¥Ã¥¯, ¥¨¥Ì¡¦¥Æ¥£¡Ý¡¦¥¨¥¹, 2009ǯ.
2007ǯ
  • K. Izumi: Analysis of Efficiency and Accuracy of Learning in Minority Games, New Frontiers in Artificial Intelligence, Lecture Note in Computer Science, Vol. 3609, pp. 103-113, 2007.
2006ǯ
  • S. Kurihara and K. Izumi: Agent network dynamics and intelligence, New Frontiers in Artificial Intelligence, Lecture Note in Computer Science, Vol. 4384, pp.237-238, 2006.
  • T. Yamashita, K. Izumi, K. Kurumatani: Information sharing for smooth traffic in road networks, LECTURE NOTES IN ARTIFICIAL INTELLIGENCE, Vol. 4012, pp. 330-339, 2006.
2005ǯ
  • »º¶Èµ»½ÑÁí¹ç¸¦µæ½êÊÔ, ¡ØÊ£»¨¸½¾Ý¹©³Ø - Ê£»¨·Ï¥Ñ¥é¥À¥¤¥à¤Î¹©³Ø±þÍÑ -¡Ù, ¥×¥ì¥¢¥Ç¥¹½ÐÈÇ, 2005ǯ
  • K. Izumi, Analysis of Learning Types in an Artificial Market, in P. Davidsson et al. (eds.), Multi-Agent and Multi-Agent-Based Simulation, Springer, pp. 145-158, 2005.
2003ǯ
  • ÏÂÀô ·é, ¡Ø¿Í¹©»Ô¾ì »Ô¾ìʬÀϤÎÊ£»¨·Ï¥¢¥×¥í¡¼¥Á¡Ù, ¿¹Ë̽ÐÈÇ, 2003.
2002ǯ
  • °ÂÉÙ Êâ, ÃÓ¾å ¹â»Ö, ÏÂÀô ·é, ¡ØÊ£»¨·Ï¤È¤¤¤¦²Ê³Ø,Ê£»¨·Ï·ÐºÑ³Ø¤Ø¤Î¥¢¥×¥í¡¼¥Á¡Ù, ÅìÍηкѿ·Êó¼Ò, 2002.
  • K. Izumi & K. Ueda, Using an Artificial Market Approach to Analyze Exchange Rate Scenario, in S.-H. Chen (eds.), Evolutionary Computation in Economics and Finance, MIT Press, pp.135-157, 2002.
2001ǯ
  • K. Izumi, An Artificial Market Analysis of Development of Market Complexity, Agent-Based Approaches in Economic and Social Complex Systems, IOS Press, pp. 47-58, 2001
  • K. Izumi, Complexity of Agents and Complexity of Markets, New Frontiers in Artificial Intelligence, Springer Verlag, pp110-120, 2001.
2000ǯ
  • ÏÂÀô ·é¡¦¿¢ÅÄ °ìÇî, ¶âÍ»»Ô¾ì¤Ë¤ª¤±¤ë°Õ¿Þ¤»¤¶¤ë¶¨Ä´¸½¾Ý: ¿Í¹©»Ô¾ì¥¢¥×¥í¡¼¥Á¤Ë¤è¤ëʬÀÏ, ¿¢ÅÄ°ìÇ²¬ÅÄÌÔÊÔ,¡Ø¶¨Æ±¤ÎÃΤòõ¤ë: ÁϤŪ¥³¥é¥Ü¥ì¡¼¥·¥ç¥ó¤ÎǧÃβʳءٶ¦Î©½ÐÈÇ, pp 199-227, 2000.
  • K. Izumi and K. Ueda, Learning of Virtual Dealers in an Artificial Market: Comparison with Interview Data, in K.S. Leung, L.-W. Chan., and H. Meng (eds.), Intelligent Data Engineering and Automated Learning - IDEAL2000, Springer Verlag, pp 511-516, 2000.

¸ýƬȯɽ

2019ǯ
2018ǯ
2017ǯ
2016ǯ
2015ǯ
2014ǯ
2013ǯ
2012ǯ
2011ǯ
2010ǯ
2009ǯ
  • ÏÂÀô·é, ¸åÆ£Âî, ¾¾°æÆ£¸ÞϺ, ¥Æ¥­¥¹¥È¾ðÊó¤Ë¤è¤ë¶âÍ»»Ô¾ìÊÑÆ°¤ÎÍ×°øʬÀÏ, 2009ǯÅٿ͹©ÃÎǽ³Ø²ñÁ´¹ñÂç²ñ¡ÊÂè23²ó¡Ë, 3G3-OS12-3 (2009).
  • ÏÂÀô·é, ¸åÆ£Âî, ¾¾°æÆ£¸ÞϺ, ¥Æ¥­¥¹¥È¾ðÊó¤Ë¤è¤ëÊ£¿ô¶âÍ»»Ô¾ì¤ÎʬÀÏ, Âè2²ó¥Õ¥¡¥¤¥Ê¥ó¥¹¤Ë¤ª¤±¤ë¿Í¹©ÃÎǽ±þÍѸ¦µæ²ñ, 2009ǯ1·î25Æü
  • ȬÌÚ ·®, ¿åÅÄ ¹§¿®, ÏÂÀô ·é: ¡È³ô¼°»Ô¾ì¤Ë¤ª¤±¤ë¶õÇä¤êµ¬À©¤Ë¤è¤ë±Æ¶ÁʬÀÏ,¡É ¥·¥¹¥Æ¥àÁÏÀ®³ØÂèÆó²ó³Ø½Ñ¹Ö±é²ñ, 2009.
  • ȬÌÚ ·®, ¿åÅÄ ¹§¿®, ÏÂÀô ·é: ¡È¶õÇä¤êµ¬À©¤¬Í¿¤¨¤ë³ô¼°»Ô¾ì¤Ø¤Î±Æ¶ÁʬÀÏ,¡É ¹çƱ¥¨¡¼¥¸¥§¥ó¥È¥ï¡¼¥¯¥·¥ç¥Ã¥×¡õ¥·¥ó¥Ý¥¸¥¦¥à£²£°£°£¹¡ÊJAWS2009¡Ë, 2009.
  • ȬÌÚ ·®, ¿åÅÄ ¹§¿®, ÏÂÀô ·é: ¿Í¹©»Ô¾ì¤òÍѤ¤¤¿³ô¼°»Ô¾ì¤Ë¤ª¤±¤ë¶õÇä¤êµ¬À©¤ÎÍ­¸úÀ­Ê¬ÀÏ,Âè3²ó¥Õ¥¡¥¤¥Ê¥ó¥¹¤Ë¤ª¤±¤ë¿Í¹©ÃÎǽ±þÍѸ¦µæ²ñ, SIG-FIN-003, 2009ǯ9·î12Æü.
  • ÏÂÀô·é, ¸åÆ£Âî, ¾¾°æÆ£¸ÞϺ: ¥Æ¥­¥¹¥È¾ðÊó¤Ë¤è¤ë¶âÍ»»Ô¾ì¤ÎÃ༡³°ÁÞͽ¬,Âè3²ó¥Õ¥¡¥¤¥Ê¥ó¥¹¤Ë¤ª¤±¤ë¿Í¹©ÃÎǽ±þÍѸ¦µæ²ñ, SIG-FIN-003, 2009ǯ9·î12Æü.
  • ȬÌÚ ·®, ¿åÅÄ ¹§¿®, ÏÂÀô ·é: ¡È¿Í¹©»Ô¾ì¤òÍѤ¤¤¿³ô¼°»Ô¾ì°ÂÄê²½ºö¤È¤·¤Æ¤Î¶õÇä¤êµ¬À©¤ÎÍ­¸úÀ­¸¡¾Ú,¡É Âè31²óÆüËܶâÍ»¡¦¾Ú·ô·×ÎÌ¡¦¹©³Ø³Ø²ñ (JAFEE) ²Æµ¨Âç²ñ, 2009.
2008ǯ
2004ǯ
  • ÏÂÀô ·é¡¤»³²¼ Îѱû¡¤¼Öë ¹À°ì, ¿Ê²½Åª¥¢¥ë¥´¥ê¥º¥à¤Ï¿Í¹©»Ô¾ì¤Ë¤Õ¤µ¤ï¤·¤¤¤«?, ¹çƱ¥¨¡¼¥¸¥§¥ó¥È¥ï¡¼¥¯¥·¥ç¥Ã¥×¡õ¥·¥ó¥Ý¥¸¥¦¥à2004 (JAWS 2004) , pp 339-345, 2004.
  • ÏÂÀô ·é¡¤»³²¼ Îѱû¡¤¼Öë ¹À°ì , ¥Í¥Ã¥È¥ï¡¼¥¯¹½Â¤¤È¥Ê¥Ó¥²¡¼¥·¥ç¥ó¥¢¥ë¥´¥ê¥º¥à¤ÎÍ­¸úÀ­, Âè136²ó ÃÎǽ¤ÈÊ£»¨·Ï¸¦µæ²ñ, ¾ðÊó½èÍý³Ø²ñ, pp 33-40, 2004.
2001ǯ
2000ǯ
1999ǯ
1998ǯ
1996ǯ
  • ÏÂÀô ·é¡¦Â羡 ¹§»Ê (1996), ¿Í¹©»Ô¾ì¥â¥Ç¥ë¤Ë¤è¤ë³°¹ñ°ÙÂإǥ£¡¼¥é¡¼¤Î ³Ø½¬¹ÔÆ°¤ÎʬÀÏ, ¾ðÊó½èÍý³Ø²ñ¿Í¹©ÃÎǽ¸¦µæ²ñÊó¹ð, Vol.96, No.106, pp91-98.
1995ǯ
  • ÏÂÀô ·é¡¦Â羡 ¹§»Ê (1995), ¿Í¹©»Ô¾ì¥¢¥×¥í¡¼¥Á¤Ë¤è¤ë°ÙÂإ졼¥È¤ÎʬÀÏ, ¾ðÊó½èÍý³Ø²ñ¿Í¹©ÃÎǽ¸¦µæ²ñÊó¹ð, Vol.95, No.102, pp7-12.
  • ÏÂÀô ·é¡¦Â羡 ¹§»Ê (1995), °äÅÁŪ¥¢¥ë¥´¥ê¥º¥à¤òÍѤ¤¤¿³°¹ñ°ÙÂػԾì¤Î¥·¥ß¥å¥ì¡¼¥·¥ç¥ó, Âè9²ó¿Í¹©ÃÎǽ³Ø²ñÁ´¹ñÂç²ñ, pp 347-350.

²ñµÄÏÀʸ

2021ǯ
2020ǯ
  • [¹ñºÝ²ñµÄ] Masanori Hirano, Hiroyasu Matsushima, Kiyoshi Izumi and Hiroki Sakaji, Implementation of Real Data for Financial Market Simulation using Clustering, Deep Learning, and Artificial Financial Market, the 23rd International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2020), Nov 18-20, 2020
  • [¹ñºÝ²ñµÄ] Masanori Hirano, Hiroyasu Matsushima, Kiyoshi Izumi and Taisei Mukai, Simulation of Unintentional Collusion Caused by Auto Pricing in Supply Chain Markets, the 23rd International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2020), Nov 18-20, 2020
  • [¹ñºÝ²ñµÄ] Tomoki Ito, Kota Tsubouchi, Hiroki Sakaji, Tatsuo Yamashita and Kiyoshi Izumi, SSNN: Sentiment Shift Neural Network, SIAM International Conference on Data Mining (SDM), Ohio, USA, May, 2020. (to appear) (acceptance rate = 24.0% (75/312))
  • [¹ñºÝ²ñµÄ] Kiyoshi Izumi, Shintaro Suda and Hiroki Sakaji, Economic News Impact Analysis Using Causal-Chain Search from Textural Data, The AAAI-20 Workshop on Knowledge Discovery from Unstructured Data in Financial Services, New York, USA, February, 2020.
  • [¹ñºÝ²ñµÄ] Tomoki Ito, Kota Tsubouchi, Hiroki Sakaji, Tatsuo Yamashita and Kiyoshi Izumi, Word-level Contextual Sentiment Analysis with Interpretability, the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), February 7-12, 2020, New York, US. (to appear) (20.6% acceptance rate)
2019ǯ
2018ǯ
2017ǯ
2016ǯ
2015ǯ
  • Takuma Torii, Kiyoshi Izumi, Kenta Yamada, Shock transfer by arbitrage trading and the role of circuit breakers, The 21st International Conference on Computing in Economics and Finance 2015 (CEF2015), Taipei, Taiwan, June 20--22, 2015.
2014ǯ
  • Kiyoshi Izumi, Whole Tokyo Stock Exchange Market Simulation Project: Design of Financial Market Regulations using Agent-based Simulation, JST/CREST International Symposium on Post Petascale System Software,2014ǯ12·î3Æü.
  • Takuma Torii, Kiyoshi Izumi, Kenta Yamada. ``Whole Tokyo Stock Exchange Market Simulation Project: Design of Financial Market Regulations using Agent-based Simulation''. JST/CREST International Symposium on Post Petascale System Software (ISP2S2), IRC of Kobe University and RIKEN AICS, Kobe, Japan, December 2-4, 2014.
  • Naoki Matsumura, Kiyoshi Izumi, Kenta Yamada, A Marketing Simulation of a Retail Store with the Consumer Reactions to Stock-outs, Social Modeling and Simulations + Econophysics Colloquium 2014, Kobe,2014ǯ11·î5Æü.
  • Yuki Kusada, Tkanohbu Mizuta, S. Hakawa,Kiyoshi Izumi, Impacts of Position-Based Market Makers on Markets' Shares of Trading Volumes - An Artificial Market Approach, Social Modeling and Simulations + Econophysics Colloquium 2014,Kobe,2014ǯ11·î5Æü.
  • Takuma Torii, Kiyoshi Izumi, Kenta Yamada, Multi-Asset Artificial Market Simulation:Test of Market-wide Circuit Breaker Regulation, Social Modeling and Simulations + Econophysics Colloquium 2014,Kobe,2014ǯ11·î5Æü.
  • Takanobu Mizuta, Kiyoshi Izumi, Isao Yagi and Shinobu Yoshimura¡¤¡É Regulations' Effectiveness for Market Turbulence by Large Mistaken Orders using Multi-Agent Simulation¡É, IEEE Computational Intelligence for Financial Engineering and Economics 2014, London, 2014ǯ3·î27Æü.
  • 2014ǯ3·î27Æü, Takanobu Mizuta, Shintaro Kosugi, Takuya Kusumoto, Wataru Matsumoto and Kiyoshi Izumi, Do Dark Pools Stabilize Markets and Reduce Market Impacts? -- Investigations using Multi-Agent Simulations --, IEEE Computational Intelligence for Financial Engineering and Economics (CIFEr2014), March 27-18, 2014, London,UK.
    • 3rd Best Paper Award
2013ǯ
  • 2013ǯ9·î13Æü Takanobu Mizuta, Satoshi Hayakawa, Kiyoshi Izumi and Shinobu Yoshimura:Simulation Study on Effects of Tick Size Difference in Stock Markets Competition, Proceedings of The 8th International Workshop on Agent-based Approach in Economic and Social Complex Systems (AESCS2013), pp.235-246, Shibaura Institute of Technology, Tokyo, JAPAN, (2013.9.11-13).
  • Takanobu MIZUTA, Kiyoshi IZUMI, Shinobu YOSHIMURA, Price Variation Limits and Financial Market Bubbles: Artificial Market Simulations with Agents¡Ç Learning Process, 2013 IEEE Symposium Series on Computational Intelligence (SSCI), CD-ROM, Singapore, (2013.4.16-19)
2012ǯ
  • Chi WANG, Kiyoshi IZUMI, Takanobu MIZUTA, and Shinobu YOSHIMURA, Investigating the Impact of Trading Frequencies of Market Makers: a Multi-agent Simulation Approach, 4th World Congress on Social Simulation, Taiwan, Sep. 4-7, 2012.
2011ǯ
  • Kiyoshi Izumi, Space Design by Agent Simulation based on Real Trajectory Data, SICE2011, Tokyo, Sep. 14, 2011.
  • Tohgoroh Matsui, Takashi Goto, Kiyoshi Izumi, Yu Chen, Compound Reinforcement Learning: Theory and An Application to Finance, The 9th European Workshop on Reinforcement Learning,, Greece, Sept 2011.
  • F. Toriumi, I. Okada, H. Yamamoto, H. Suwa, K. Izumi and Y. Hashimoto, Classification of Social Network Sites based on Network Indexes and Communication Patterns, International Workshop on Social Web Mining, 18 July 2011, Spain.
2010ǯ
  • Kiyoshi Izumi, Takashi Goto, Tohgoroh Matsui, Trading Tests of Long-Term Market Forecast by Text Mining, Proceedings of 2010 IEEE International Conference on Data Mining Workshops, pp. 935-942, 2010.
  • Atsushi Nara, Kiyoshi Izumi, Hiroshi Iseki, Takashi Suzuki, Kyojiro Nambu, Yasuo Sakurai, Surgical Workflow Monitoring based on Trajectory Data Mining, Proceedings of International Workshop on Innovating Service Systems (ISS2010), 2010.
2009ǯ
  • F. Toriumi, K. Izumi, and H. Matsui, Market Participant Estimation by using Artificial Market, The 1st Workshop on Multi-Agent Simulation in Finance and Economics, 2009.
  • I. Yagi, T. Mizuta, and K. Izumi, A study on the market impact of short-selling regulation using artificial markets, The 1st Workshop on Multi-Agent Simulation in Finance and Economics, 2009.
  • A. Nara, K. Izumi, H. Iseki, et.al., Trajectory Data Mining for Surgical Workflow Analysis, Geocomputing, 2009.
  • A. Nara, K. Izumi, H. Iseki, et.al., Surgical Workflow Analysis based on Staff¡Çs Trajectory Patterns, The 1st Workshop on Modeling and Monitoring of Computer Assisted Interventions, 2009.
  • I. Yagi, T. Mizuta and K. Izumi, A study on the effectiveness of short-selling regulation using artificial markets, The 9th Asia-Pacific Complex Systems Conference, 2009.
2008ǯ
  • K. Izumi, T. Goto, T. Matsui: Long-Term Market Analysis using Text Mining, The 7th International Conference on Computational Intelligence in Economics and Finance, 2008.
2007ǯ
  • K. Izumi, H. Matsui, and Y. Matsuo: Market Impact Analysis by Socially Embedded Multi Agent Simulation, Eleventh International Conference on the Enhancement and Promotion of Computational Methods in Engineering and Science, 2007.
  • K. Izumi, F. Toriumi, and H. Matsui, Artificial Market 2.0: From Simulation to Service, The Fifth International Workshop on Agent-based Approaches in Economic and Social Complex Systems, 2007.
  • K. Izumi, H. Matsui, and Y. Matsuo: A Self-Impact Analysis by Artificial Market Simulation, Computational Intelligence and Data Mining, 2007.
  • K. Izumi, H. Matsui, and Y. Matsuo: Socially Embedded Multi Agent Based Simulation of Financial Market, Autonomous Agents and Multi-Agent Systems, 2007.
  • K. Izumi, H. Matsui, and Y. Matsuo: Integration of Artificial Market Simulation and Text Mining for Market Analysis, Hybrid Information Technology, 2007
2005ǯ
  • H. Matsui, K. Izumi, and S. Tojo: Learning Foreign Exchange Intervention Policies with an Artificial Market, Proceedings of 8th Joint Conference on Information Science, pp. 841-844, 2005.
2004ǯ
  • K. Izumi, T. Yamashita, and K. Kurumatani: Reward Mechanism of Agent Types in Minority Games, Proceedings of Second Conference of the European Social Simulation Association, ESSA 04, 2004.
  • K. Izumi, T. Yamashita, and K. Kurumatani: Analysis of Learning Types in an Artificial Market, Proceeding of Joint Workshop on Multi-Agent and Multi-Agent-Based Simulation, 2004.
  • K. Izumi, T. Yamashita, and K. Kurumatani: Control of Learning Types for Efficient Resource Allocation, Proceedings of North American Association for Computational Social and Organizational Science, 2004.
  • K. Izumi, T. Yamashita, and K. Kurumatani: Analysis of Efficiency and Accuracy of Learning in Minority Games, Proceedings of THE THIRD INTERNATIONAL JOINT CONFERENCE ON AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS, 2004.
  • K. Izumi, T. Yamashita, and K. Kurumatani, Analysis of Complexity and Time Restriction in Resouces Alllocation Problems, Proceedings of the 9th Workshop on Economics and Heterogeneous Interacting Agents (WEHIA2004), 2004.
2002ǯ
  • K. Izumi, Does Learnig by Market Participants Make Financial Markets Complicated?, AAAI Workshop Multi-Agent Modeling and Simulation of Economic Systems, 2002.
  • K. Izumi, Micro-macro relation in artificial market models, Proceedings of the 6th International Conference on Complex Systems 2002, pp. 36-43, 2002.
  • K. Izumi, S. Nakamura, K. Ueda, Identification of Agents¡Ç Strategy Making Process by an Experimental Market, Proceedings of the 6th Joint Conference on Artificial Intelligence, pp. 1081-1084, 2002.
  • K. Izumi, Analysis of the Relation between Complexity of Agents and Complexity of Markets in an Artificial Market, Proceedings of Fourth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2001), pp. 29-33, 2002.
2001ǯ
2000ǯ
  • K. Izumi and K. Ueda, Phase Transition in a Foreign Exchange Market: Analysis Based on an Artificial Market Approach, in Proceedings of Symposium on Empirical Science of Financial Fluctuations, pp 33-34, 2000.
  • K. Izumi and K. Ueda, Analysis of Thought Processes of Virtual Dealers in an Artificial Market, in P.P. Wang (eds.), Proceedings of the Fifth Joint Conference on Artificial Intelligence, vol. 2, pp 891-894, 2000.
1999ǯ
1998ǯ
1996ǯ