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Kiyoshi Izumi
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2020 – today
- 2024
- [j29]Takehiro Takayanagi
, Kiyoshi Izumi:
Incorporating Domain-Specific Traits into Personality-Aware Recommendations for Financial Applications. New Gener. Comput. 42(4): 635-649 (2024) - [j28]Long Cheng
Improvement and Analysis of Peak Shift Demand Response Scenarios of Industrial Consumers Using an Electricity Market Model. New Gener. Comput. 42(5): 1089-1113 (2024) - [c95]Meiyun Wang, Kiyoshi Izumi, Hiroki Sakaji:
LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction. ACL (Findings) 2024: 3120-3131 - [i12]Takehiro Takayanagi, Masahiro Suzuki, Ryotaro Kobayashi, Hiroki Sakaji, Kiyoshi Izumi:
Is ChatGPT the Future of Causal Text Mining? A Comprehensive Evaluation and Analysis. CoRR abs/2402.14484 (2024) - [i11]Meiyun Wang, Kiyoshi Izumi, Hiroki Sakaji:
LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction. CoRR abs/2406.10811 (2024) - [i10]Hiroki Sakaji, Ryotaro Kobayashi, Kiyoshi Izumi, Hiroyuki Mitsugi, Wataru Kuramoto:
Summarization of Investment Reports Using Pre-trained Model. CoRR abs/2408.01744 (2024) - [i9]Hiroki Sakaji, Jason Bennett, Risa Murono, Kiyoshi Izumi, Hiroyuki Sakai:
Discovery of Rare Causal Knowledge from Financial Statement Summaries. CoRR abs/2408.01748 (2024) - [i8]Naoto Minakawa, Kiyoshi Izumi, Hiroki Sakaji:
Bilateral Trade Flow Prediction by Gravity-informed Graph Auto-encoder. CoRR abs/2408.01938 (2024) - [i7]Takehiro Takayanagi, Hiroki Sakaji, Kiyoshi Izumi:
SETN: Stock Embedding Enhanced with Textual and Network Information. CoRR abs/2408.02899 (2024) - [i6]Rei Taguchi, Hiroki Sakaji, Kiyoshi Izumi:
SSAAM: Sentiment Signal-based Asset Allocation Method with Causality Information. CoRR abs/2408.06585 (2024) - [i5]Meiyun Wang, Masahiro Suzuki, Hiroki Sakaji, Kiyoshi Izumi:
Interactive DualChecker for Mitigating Hallucinations in Distilling Large Language Models. CoRR abs/2408.12326 (2024) - [i4]Takehiro Takayanagi, Hiroya Takamura, Kiyoshi Izumi, Chung-Chi Chen:
Beyond Turing Test: Can GPT-4 Sway Experts' Decisions? CoRR abs/2409.16710 (2024) - 2023
- [j27]Takehiro Takayanagi, Kiyoshi Izumi:
Context-Aware Stock Recommendations with Stocks' Characteristics and Investors' Traits. IEICE Trans. Inf. Syst. 106(10): 1732-1741 (2023) - [j26]Masahiro Suzuki
Constructing and analyzing domain-specific language model for financial text mining. Inf. Process. Manag. 60(2): 103194 (2023) - [j25]Rei Taguchi
Constructing Sentiment Signal-Based Asset Allocation Method with Causality Information. New Gener. Comput. 41(4): 777-794 (2023) - [j24]Hiroki Sakaji
Financial Causality Extraction Based on Universal Dependencies and Clue Expressions. New Gener. Comput. 41(4): 839-857 (2023) - [j23]Masanori Hirano
Neural-network-based parameter tuning for multi-agent simulation using deep reinforcement learning. World Wide Web (WWW) 26(5): 3535-3559 (2023) - [c94]Ryotaro Kobayashi, Yuri Murayama, Kiyoshi Izumi:
Impact Analysis of Social Events on Industries through Narrative Causal Search. IEEE Big Data 2023: 2845-2854 - [c93]Takehiro Takayanagi, Kiyoshi Izumi:
Harnessing Behavioral Traits to Enhance Financial Stock Recommender Systems: Tackling the User Cold Start Problem. IEEE Big Data 2023: 5694-5703 - [c92]Masanori Hirano
Policy Gradient Stock GAN for Realistic Discrete Order Data Generation in Financial Markets. IIAI-AAI 2023: 361-368 - [c91]Takanobu Mizuta, Kiyoshi Izumi:
Frequent Batch Auctions investigated by Agent-Based Model. IIAI-AAI 2023: 446-451 - [c90]Tsubasa Ueda, Takehide Hirose, Kiyoshi Izumi:
Export Nowcasting with AIS and Foot Traffic Data. IIAI-AAI 2023: 486-489 - [c89]Hiroki Sakaji, Ryotaro Kobayashi, Kiyoshi Izumi, Hiroyuki Mitsugi, Wataru Kuramoto:
Summarization of Investment Reports Using Pre-trained Model. IIAI-AAI 2023: 554-559 - [c88]Yoshiyuki Suimon, Hiroto Tanabe, Kiyoshi Izumi:
Using weather-based machine learning approach to estimate retail sales and interpret weather factors. IIAI-AAI 2023: 725-727 - [c87]Shingo Sashida, Kiyoshi Izumi, Hiroki Sakaji:
Extraction SDGs-related sentences from Sustainability Reports using BERT and ChatGPT. IIAI-AAI 2023: 742-745 - [c86]Takehiro Takayanagi
Personalized Dynamic Recommender System for Investors. SIGIR 2023: 2246-2250 - [c85]Takehiro Takayanagi
Personalized Stock Recommendation with Investors' Attention and Contextual Information. SIGIR 2023: 3339-3343 - [i3]Masanori Hirano, Ryosuke Takata, Kiyoshi Izumi:
PAMS: Platform for Artificial Market Simulations. CoRR abs/2309.10729 (2023) - 2022
- [j22]Rei Taguchi, Hikaru Watanabe, Hiroki Sakaji, Kiyoshi Izumi, Kenji Hiramatsu:
Constructing Equity Investment Strategies Using Analyst Reports and Regime Switching Models. Frontiers Artif. Intell. 5: 865950 (2022) - [j21]Masahiro Suzuki
Forecasting Stock Price Trends by Analyzing Economic Reports With Analyst Profiles. Frontiers Artif. Intell. 5: 866723 (2022) - [j20]Masanori Hirano
STBM+: Advanced Stochastic Trading Behavior Model for Financial Markets using Residual Blocks or Transformers. New Gener. Comput. 40(1): 7-24 (2022) - [c84]Masanori Hirano, Kiyoshi Izumi, Hiroki Sakaji:
Implementation of Actual Data for Artificial Market Simulation. AAMAS 2022: 1624-1626 - [c83]Masanori Hirano
Parameter Tuning Method for Multi-agent Simulation using Reinforcement Learning. BESC 2022: 1-7 - [c82]Yoshito Noritake, Takanobu Mizuta
Investigation on effect of excess buy orders using agent-based model. BESC 2022: 1-5 - [c81]Yuya Takada
Implementation of Biased Big Data to the Japanese Official Labor Statistics Using Supervised Learning under Covariate Shift. IEEE Big Data 2022: 2062-2071 - [c80]Naoto Minakawa, Kiyoshi Izumi, Hiroki Sakaji:
Bilateral Trade Flow Prediction by Gravity-informed Graph Auto-encoder. IEEE Big Data 2022: 2327-2332 - [c79]Hiroki Sakaji, Masahiro Suzuki
Gradual Further Pre-training Architecture for Economics/Finance Domain Adaptation of Language Model. IEEE Big Data 2022: 2352-2355 - [c78]Yoshiyuki Suimon, Hiroto Tanabe, Kiyoshi Izumi:
The relationship between Twitter sentiment and mobility during the COVID-19 pandemic. IEEE Big Data 2022: 2370-2372 - [c77]Rei Taguchi, Hiroki Sakaji, Kiyoshi Izumi:
SSAAM: Sentiment Signal-based Asset Allocation Method with Causality Information. IEEE Big Data 2022: 2373-2376 - [c76]Takehiro Takayanagi, Hiroki Sakaji, Kiyoshi Izumi:
SETN: Stock Embedding Enhanced with Textual and Network Information. IEEE Big Data 2022: 2377-2382 - [c75]Masanori Hirano
Concept and Practice of Artificial Market Data Mining Platform. CIFEr 2022: 1-10 - [c74]Masanori Hirano
Quantitative Tuning of Artificial Market Simulation using Generative Adversarial Network. ICA 2022: 12-17 - [c73]Masanori Hirano
Efficient Parameter Tuning for Multi-agent Simulation Using Deep Reinforcement Learning. IIAI-AAI-Winter 2022: 130-137 - [c72]Masanori Hirano
Analysis of Demand Response Scenarios by Industrial Consumers Using Artificial Electric Power Market Simulations. IIAI-AAI 2022: 547-554 - [c71]Rei Taguchi, Hikaru Watanabe, Hiroki Sakaji, Kiyoshi Izumi, Kenji Hiramatsu:
Proposal for Turning Point Detection Method Using Financial Text and Transformer. JSAI-isAI Workshops 2022: 171-181 - [c70]Naoto Minakawa, Kiyoshi Izumi, Hiroki Sakaji, Hitomi Sano:
Transaction Prediction by Using Graph Neural Network and Textual Industry Information. JSAI-isAI Workshops 2022: 251-266 - [c69]Masanori Hirano
Analysis of Carbon Neutrality Scenarios of Industrial Consumers Using Electric Power Market Simulations. PRIMA 2022: 90-105 - [c68]Masanori Hirano
Does Order Simultaneity Affect the Data Mining Task in Financial Markets? - Effect Analysis of Order Simultaneity Using Artificial Market. PRIMA 2022: 297-313 - [c67]Naoto Minakawa, Kiyoshi Izumi, Hiroki Sakaji, Hitomi Sano:
Graph Representation Learning of Banking Transaction Network with Edge Weight-Enhanced Attention and Textual Information. WWW (Companion Volume) 2022: 630-637 - [i2]Masanori Hirano
Policy Gradient Stock GAN for Realistic Discrete Order Data Generation in Financial Markets. CoRR abs/2204.13338 (2022) - 2021
- [c66]Hiroki Sakaji, Teruaki Hayashi, Yoshiaki Fukami, Takumi Shimizu, Hiroyasu Matsushima
Retrieving of Data Similarity using Metadata on a Data Analysis Competition Platform. IEEE BigData 2021: 3480-3485 - [c65]Rei Taguchi, Hikaru Watanabe, Masanori Hirano
Market Trend Analysis Using Polarity Index Generated from Analyst Reports. IEEE BigData 2021: 3486-3494 - 2020
- [j19]Tomoki Ito
Contextual Sentiment Neural Network for Document Sentiment Analysis. Data Sci. Eng. 5(2): 180-192 (2020) - [j18]Tomoki Ito
GINN: gradient interpretable neural networks for visualizing financial texts. Int. J. Data Sci. Anal. 9(4): 431-445 (2020) - [j17]Masahiro Suzuki
Forecasting Net Income Estimate and Stock Price Using Text Mining from Economic Reports. Inf. 11(6): 292 (2020) - [j16]Masanori Hirano
Comparing Actual and Simulated HFT Traders' Behavior for Agent Design. J. Artif. Soc. Soc. Simul. 23(3) (2020) - [c64]Tomoki Ito, Kota Tsubouchi, Hiroki Sakaji, Tatsuo Yamashita, Kiyoshi Izumi:
Word-Level Contextual Sentiment Analysis with Interpretability. AAAI 2020: 4231-4238 - [c63]Hiroki Sakaji, Teruaki Hayashi, Kiyoshi Izumi
Verification of Data Similarity using Metadata on a Data Exchange Platform. IEEE BigData 2020: 4467-4474 - [c62]Yoshiyuki Suimon, Kiyoshi Izumi
Estimating Manufacturing Activity via Machine Learning Analysis of High-frequency Electricity Demand Patterns. IIAI-AAI 2020: 562-565 - [c61]Masanori Hirano
STBM: Stochastic Trading Behavior Model for Financial Markets. JSAI 2020: 157-165 - [c60]Yasutaka Nishimura, Taichi Shimura, Kiyoshi Izumi
Design and Evaluations of Multi-agent Simulation Model for Electric Power Sharing Among Households. MABS 2020: 41-53 - [c59]Masanori Hirano
Implementation of Real Data for Financial Market Simulation Using Clustering, Deep Learning, and Artificial Financial Market. PRIMA 2020: 3-18 - [c58]Masanori Hirano
Simulation of Unintentional Collusion Caused by Auto Pricing in Supply Chain Markets. PRIMA 2020: 352-359 - [c57]Tomoki Ito, Kota Tsubouchi
SSNN: Sentiment Shift Neural Network. SDM 2020: 262-270
2010 – 2019
- 2019
- [j15]Masanori Hirano
Related Stocks Selection with Data Collaboration Using Text Mining. Inf. 10(3): 102 (2019) - [j14]Ryo Hamawaki, Kiyoshi Izumi, Hiroki Sakaji, Takashi Shimada, Hiroyasu Matsushima
Chain bankruptcy size in inter-bank networks: the effects of asset price volatility and the network structure. J. Comput. Soc. Sci. 2(1): 53-66 (2019) - [c56]Hiroki Sakaji, Akio Kobayashi, Masaki Kohana, Yasunao Takano, Kiyoshi Izumi
Estimation of Tags Using Various Data for Online Videos. AINA 2019: 301-312 - [c55]Hirofumi Yamamoto, Hiroki Sakaji, Hiroyasu Matsushima
Forecasting Crypto-Asset Price Using Influencer Tweets. AINA 2019: 940-951 - [c54]Tomoki Ito, Kota Tsubouchi
Word-level Sentiment Visualizer for Financial Documents. CIFEr 2019: 1-7 - [c53]Atsuki Nakayama, Kiyoshi Izumi
Short-term Stock Price Prediction by Analysis of Order Pattern Images. CIFEr 2019: 1-5 - [c52]Yoshiyuki Suimon, Hiroki Sakaji, Takashi Shimada, Kiyoshi Izumi
Japanese long-term interest rate forecast considering the connection between the Japanese and US yield curve. CIFEr 2019: 1-7 - [c51]Kyoto Yono, Kiyoshi Izumi
Extraction of Focused Topic and Sentiment of Financial Market by using Supervised Topic Model for Price Movement Prediction. CIFEr 2019: 1-7 - [c50]Yuta Niki, Hiroki Sakaji, Kiyoshi Izumi
Causality Existence Classification from Multilingual Texts Using End-to-End LSTM Models. ICDM Workshops 2019: 17-23 - [c49]Hiroki Sakaji, Yasutomo Kimura, Kiyoshi Izumi
Extraction of Volitional Utterances from Japanese Local Political Corpus. ICDM Workshops 2019: 24-29 - [c48]Masahiro Suzuki
Stock Price Analysis Using Combination of Analyst Reports and Several Documents. ICDM Workshops 2019: 30-36 - [c47]Tomoki Ito, Kota Tsubouchi
CSNN: Contextual Sentiment Neural Network. ICDM 2019: 1126-1131 - [c46]Yoshiyuki Suimon, Hiroki Sakaji, Takashi Shimada, Kiyoshi Izumi
Extraction of Relationship between Japanese and US Interest Rates using Machine Learning Methods. IIAI-AAI 2019: 649-654 - [c45]Kei Nakagawa
Economic Causal Chain and Predictable Stock Returns. IIAI-AAI 2019: 655-660 - [c44]Kyoto Yono, Kiyoshi Izumi
Analysis of the Macroeconomic Uncertainty Based on the News-based Textual Data with Financial Market. IIAI-AAI 2019: 661-666 - [c43]Iwao Maeda, Hiroyasu Matsushima
Effectiveness of Uncertainty Consideration in Neural-Network-Based Financial Forecasting. IIAI-AAI 2019: 673-678 - [c42]Yoshinori Tanaka, Syunya Kodera, Fumihito Sato, Hiroki Sakaji, Kiyoshi Izumi
The Extraction of the Future-Oriented Sentences from Annual Reports. IIAI-AAI 2019: 679-684 - [c41]Kiyoshi Izumi
Economic Causal-Chain Search Using Text Mining Technology. IJCAI 2019: 23-35 - [c40]Tomoki Ito, Hiroki Sakaji, Kiyoshi Izumi
Segment Information Extraction from Financial Annual Reports Using Neural Network. JSAI 2019: 215-226 - [c39]Hiroki Sakaji, Akio Kobayashi, Masaki Kohana, Yasunao Takano, Kiyoshi Izumi
Card Price Prediction of Trading Cards Using Machine Learning Methods. NBiS 2019: 705-714 - [c38]Ryo Hamawaki, Jun'ichi Ozaki, Kiyoshi Izumi
Chain Bankruptcy Simulation Considering Investment from Banks to Companies. SMC 2019: 3784-3790 - [i1]Kei Nakagawa, Tomoki Ito, Masaya Abe, Kiyoshi Izumi:
Deep Recurrent Factor Model: Interpretable Non-Linear and Time-Varying Multi-Factor Model. CoRR abs/1901.11493 (2019) - 2018
- [j13]Itsuki Noda, Nobuyasu Ito, Kiyoshi Izumi, Hideki Mizuta, Tomio Kamada
Roadmap and research issues of multiagent social simulation using high-performance computing. J. Comput. Soc. Sci. 1(1): 155-166 (2018) - [c37]Masanori Hirano
Selection of Related Stocks using Financial Text Mining. ICDM Workshops 2018: 191-198 - [c36]Tomoki Ito, Hiroki Sakaji, Kota Tsubouchi
Text-Visualizing Neural Network Model: Understanding Online Financial Textual Data. PAKDD (3) 2018: 247-259 - 2017
- [j12]Takuma Torii, Tomio Kamada
Platform design for large-scale artificial market simulation and preliminary evaluation on the K computer. Artif. Life Robotics 22(3): 301-307 (2017) - [c35]Tomoki Ito, Hiroki Sakaji, Kiyoshi Izumi
Development of an Interpretable Neural Network Model for Creation of Polarity Concept Dictionaries. ICDM Workshops 2017: 1122-1131 - [c34]Hiroki Sakaji, Atsuya Miyazaki, Hiroyuki Sakai, Kiyoshi Izumi
Extracting Laboratory Front Pages from University Websites. NBiS 2017: 1117-1125 - [c33]Tomoki Ito, Hiroki Sakaji, Kiyoshi Izumi
Development of sentiment indicators using both unlabeled and labeled posts. SSCI 2017: 1-8 - [c32]Hiroki Sakaji, Risa Murono, Hiroyuki Sakai, Jason Bennett, Kiyoshi Izumi
Discovery of rare causal knowledge from financial statement summaries. SSCI 2017: 1-7 - 2016
- [j11]Takanobu Mizuta
Effects of Price Regulations and Dark Pools on Financial Market Stability: An Investigation by Multiagent Simulations. Intell. Syst. Account. Finance Manag. 23(1-2): 97-120 (2016) - [c31]Tomoki Ito, Kiyoshi Izumi
Polarity propagation of financial terms for market trend analyses using news articles. CEC 2016: 3477-3482 - [c30]Takanobu Mizuta
Affecting market efficiency by increasing speed of order matching systems on financial exchanges - investigation using agent based model. SSCI 2016: 1-8 - 2015
- [c29]Itsuki Noda, Nobuyasu Ito, Kiyoshi Izumi
Roadmap for Multiagent Social Simulation on HPC. WI-IAT (3) 2015: 22-25 - 2014
- [j10]Saki Kawakubo, Kiyoshi Izumi, Shinobu Yoshimura:
Analysis of an Option Market Dynamics Based on a Heterogeneous Agent Model. Intell. Syst. Account. Finance Manag. 21(2): 105-128 (2014) - [j9]Bungo Miyazaki, Kiyoshi Izumi, Fujio Toriumi
Change Detection of Orders in Stock Markets using a Gaussian Mixture Model. Intell. Syst. Account. Finance Manag. 21(3): 169-191 (2014) - [j8]Saki Kawakubo, Kiyoshi Izumi
How Does High Frequency Risk Hedge Activity Have an Affect on Underlying Market?: Analysis by Artificial Market Model. J. Adv. Comput. Intell. Intell. Informatics 18(4): 558-566 (2014) - [c28]Takanobu Mizuta
Do dark pools stabilize markets and reduce market impacts? Investigations using multi-agent simulations. CIFEr 2014: 71-76 - [c27]Takanobu Mizuta
Regulations' effectiveness for market turbulence by large erroneous orders using multi agent simulation. CIFEr 2014: 138-143 - 2013
- [c26]Takanobu Mizuta
Price variation limits and financial market bubbles: Artificial market simulations with agents' learning process. CIFEr 2013: 1-7 - 2012
- [c25]Isao Yagi
A study on the reversal mechanism for large stock price declines using artificial markets. CIFEr 2012: 1-7 - 2011
- [j7]Kiyoshi Izumi
Social and Group Simulation Based on Real Data Analysis. J. Adv. Comput. Intell. Intell. Informatics 15(2): 166-172 (2011) - [j6]Kiyoshi Izumi
Risk Evaluation by Human Trajectory Simulation Based on Real Data. J. Adv. Comput. Intell. Intell. Informatics 15(2): 220-225 (2011) - [c24]Tohgoroh Matsui
Compound Reinforcement Learning: Theory and an Application to Finance. EWRL 2011: 321-332 - 2010
- [c23]Isao Yagi
A Study on the Effectiveness of Short-Selling Regulation in View of Regulation Period Using Artificial Markets. ACIS-ICIS 2010: 169-174 - [c22]Kiyoshi Izumi
Trading Tests of Long-Term Market Forecast by Text Mining. ICDM Workshops 2010: 935-942 - [c21]Atsushi Nara
Surgical Workflow Monitoring Based on Trajectory Data Mining. JSAI-isAI Workshops 2010: 283-291
2000 – 2009
- 2009
- [j5]Kiyoshi Izumi
Evaluation of automated-trading strategies using an artificial market. Neurocomputing 72(16-18): 3469-3476 (2009) - [j4]Tohgoroh Matsui, Takashi Goto, Kiyoshi Izumi:
Acquiring a Government Bond Trading Strategy Using Reinforcement Learning. J. Adv. Comput. Intell. Intell. Informatics 13(6): 691-696 (2009) - [c20]Fujio Toriumi
Market Participant Estimation by Using Artificial Market. PRIMA Workshops 2009: 201-215 - [c19]Isao Yagi
A Study on the Market Impact of Short-Selling Regulation Using Artificial Markets. PRIMA Workshops 2009: 217-231 - 2007
- [j3]Kiyoshi Izumi
Integration of artificial market simulation and text mining for market analysis. Soft Comput. 11(12): 1199-1205 (2007) - [c18]Kiyoshi Izumi
Socially embedded multi agent based simulation of financial market. AAMAS 2007: 175 - [c17]Kiyoshi Izumi
A Self-Impact Analysis by Artificial Market Simulation. CIDM 2007: 726-733 - [c16]Yutaka Matsuo, Naoaki Okazaki, Kiyoshi Izumi, Yoshiyuki Nakamura, Takuichi Nishimura, Kôiti Hasida, Hideyuki Nakashima:
Inferring Long-term User Properties Based on Users' Location History. IJCAI 2007: 2159-2165 - 2006
- [c15]Kiyoshi Izumi
Integration of Artificial Market Simulation and Text Mining for Market Analysis. ICHIT 2006: 404-413 - 2005
- [j2]Kiyoshi Izumi
Development of an artificial market model based on a field study. Inf. Sci. 170(1): 35-63 (2005) - [c14]Tomohisa Yamashita, Kiyoshi Izumi, Koichi Kurumatani, Hideyuki Nakashima:
Smooth traffic flow with a cooperative car navigation system. AAMAS 2005: 478-485 - [c13]Satoshi Kurihara, Kiyoshi Izumi
Agent Network Dynamics and Intelligence (ANDI 2005). JSAI Workshops 2005: 237-238 - [c12]Tomohisa Yamashita, Kiyoshi Izumi
Information Sharing for Smooth Traffic in Road Networks. JSAI Workshops 2005: 330-339 - [p1]Tomohisa Yamashita, Kiyoshi Izumi, Koichi Kurumatani:
Analysis of the Effect of Route Information Sharing on Reduction of Traffic Congestion. Applications of Agent Technology in Traffic and Transportation 2005: 99-111 - 2004
- [c11]Kiyoshi Izumi, Tomohisa Yamashita, Koichi Kurumatani:
Analysis of Efficiency and Accuracy of Learning in Minoriy Games. AAMAS 2004: 1410-1411 - [c10]Tomohisa Yamashita, Kiyoshi Izumi, Koichi Kurumatani:
Investigation of Reduction of Traffic Congestion with Route Information Sharing. AAMAS 2004: 1446-1447 - [c9]Kiyoshi Izumi
Analysis of Efficiency and Accuracy of Learning in Minority Games. JSAI Workshops 2004: 103-113 - [c8]Kiyoshi Izumi
Analysis of Learning Types in an Artificial Market. MABS 2004: 145-158 - [c7]Tomohisa Yamashita, Kiyoshi Izumi
An Investigation into the Use of Group Dynamics for Solving Social Dilemmas. MABS 2004: 185-194 - 2003
- [c6]Tomohisa Yamashita, Kiyoshi Izumi
Effect of Using Route Information Sharing to Reduce Traffic Congestion. MAMUS 2003: 86-104 - 2002
- [c5]Kiyoshi Izumi, Shigeo Nakamura, Kazuhiro Ueda:
Identification of Agents' Strategy Making Process by an Experimental Market. JCIS 2002: 1081-1084 - 2001
- [j1]Kiyoshi Izumi
Phase transition in a foreign exchange market-analysis based on an artificial market approach. IEEE Trans. Evol. Comput. 5(5): 456-470 (2001) - [c4]Kiyoshi Izumi
Complexity of Agents and Complexity of Markets. JSAI Workshops 2001: 110-120 - 2000
- [c3]Kiyoshi Izumi
Learning of Virtual Dealers in an Artificial Market: Comparison with Interview Data. IDEAL 2000: 511-516
1990 – 1999
- 1999
- [c2]Kiyoshi Izumi, Kazuhiro Ueda:
Analysis of Exchange Rate Scenarios Using an Artificial Market Approach. IC-AI 1999: 360-366 - 1996
- [c1]Kiyoshi Izumi, Takashi Okatsu:
An Artificial Market Analysis of Exchange Rate Dynamics. Evolutionary Programming 1996: 27-36
Coauthor Index
[c95] [i12] [i11] [i10] [i9] [i8] [i7] [i6] [i5] [j26] [j25] [j24] [c92] [c89] [c87] [j22] [j21] [j20] [c84] [c80] [c79] [c77] [c76] [c75] [c71] [c70] [c67] [i2] [c66] [c65] [j19] [j18] [j17] [j16] [c64] [c63] [c62] [c61] [c59] [c57] [j15] [j14] [c56] [c55] [c54] [c53] [c52] [c51] [c50] [c49] [c48] [c47] [c46] [c45] [c44] [c43] [c42] [c41] [c40] [c39] [c38] [c37] [c36] [c35] [c34] [c33] [c32]
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