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Exploring the Complex Systems as a Base of Artificial Intelligence

探索复杂系统作为人工智能的基础

基本信息

批准号：
10680361
负责人：
TANAKA Mieko
金额：
$ 2.05万
依托单位：
Miyazaki University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 2001
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-10680361/
关键词：
Econophysics Artificial Markets Positive Feedback Multi-agent System High Frequency Data in Finance (HFDF)Turbulence Arbitrage Chance Relaxation Time 複雑系経済学高頻度金融データスケール不変 Time Series Analysis Econophysics Scaling Positive Fe

项目摘要

We have investigated economic systems and the brain as complex systems from a viewpoint of artificial intelligence, by using our experience on applied mathematics and computation. Our major topics are Econophysics, Human Random Generation, Application of Neural Programming to Noninvasive Diagnosis, and Cooperation of Agents, out of which we devoted most of our effort to Econophysics.We focused on studying the statistical properties of high frequency data in finance (HFDF) an also modeling of artificial markets, both being emerging fields in frontier of science. In the course of study, we have discovered that the critical region in the parameter space of our model corresponds to the real high-frequency financial data, by showing that both have the Levy distribution of the same size of indices. This result was published in INFORMATION journal in 2001 and in Empirical Science of Financial Fluctuations (Springer, 2002) which is the proceedings of the first conference in Econophysics held in 2000. We have also studied HFDF by fast computer in detail to clarify the controversial analogy between well-developed turbulence and foreign currency exchange, and also found short-term predictability based on stable patterns observed, as well as the triangle arbitrage chances and its relaxation time. We have made various presentations in domestic meetings and international conferences and made two oversea travels including Joint Conference on Information Sciences in 2000 and an informal seminar at Santa Fe Institute in 2002.In the study of Human Random Generation, we have shown, by analyzing the time series of newly taken data from our six students, that it is possible to incorporate characteristics of individuals to model parameters that distinguish the differences of those individuals.Neural Network Application to Noninvasive diagnosis is a joint work with Dr. Satoshi Yoshida, on which we submitted an application for patent in 2001.

我们利用我们在应用数学和计算方面的经验，从人工智能的角度研究了经济系统和大脑作为复杂系统。我们的主要主题是经济物理学、人类随机生成、神经编程在无创诊断中的应用以及智能体的合作，其中我们将大部分精力投入到经济物理学上。我们重点研究金融高频数据（HFDF）的统计特性以及人工市场的建模，这两个领域都是科学前沿的新兴领域。在研究过程中，我们发现模型参数空间中的关键区域与真实的高频金融数据相对应，表明两者都具有相同指数大小的 Levy 分布。这一结果发表在 2001 年的 INFORMATION 杂志和 2000 年第一届经济物理学会议论文集《Empirical Science of Financial Fluctuations》（Springer，2002）上。我们还通过快速计算机详细研究了 HFDF，以澄清充分发展的动荡与外汇兑换之间有争议的类比，并发现基于观察到的稳定模式的短期可预测性，以及三角套利机会及其放松时间。我们在国内会议和国际会议上做了各种演讲，并两次出国旅行，包括2000年信息科学联合会议和2002年在圣达菲研究所举行的非正式研讨会。在人类随机生成的研究中，我们通过分析六名学生新采集的数据的时间序列，表明可以将个体特征纳入模型参数以区分这些个体的差异。神经网络在无创诊断中的应用是与吉田聪博士合作的成果，我们于 2001 年提交了专利申请。