金融物理的若干基本问题与研究进展(Ⅱ)--基于经纪人的动力学模型的建模与分析

一门全新的交叉学科金融物理研究的第二种处理方法是构建金融市场物理模型，文章对其基本观点作了简介，并重点介绍了金融市场中基于经纪人的动力学模型的建模与分析，阐述了物理学在21世纪的金融工程研究中可发挥的作用与意义。     

Progress in physical properties of Chinese stock markets

In the past two decades, statistical physics was brought into the field of finance, applying new methods and concepts to financial time series and developing a new interdiscipline “econophysics”. In this review, we introduce several commonly used methods for stock time series in econophysics including distribution functions, correlation functions, detrended fluctuation analysis method, detrended moving average method, and multifractal analysis. Then based on these methods, we review some statistical properties of Chinese stock markets including scaling behavior, long-term correlations, cross-correlations, leverage effects, antileverage effects, and multifractality. Last, based on an agent-based model, we develop a new option pricing model — financial market model that shows a good agreement with the prices using real Shanghai Index data. This review is helpful for people to understand and research statistical physics of financial markets.     

Is econophysics a new discipline? The neopositivist argument

Econophysics is a new approach which applies various models and concepts associated with statistical physics to economic (and financial) phenomena. This field of research is a new step in the history and the evolution of Physics Sciences and the question about the disciplinary characteristics of this field must be asked. At first glance, it might appear that economics and econophysics share the same subject of research (that of analysis of economic reality). In this paper I will use neopositivism to show that econophysics is methodologically very different from economics and that it can be considered as a separate discipline. The neopositivist framework provides econophysics with some arguments for rejecting mainstream economics.     

New approaches in agent-based modeling of complex financial systems

Agent-based modeling is a powerful simulation technique to understand the collective behavior and microscopic interaction in complex financial systems. Recently, the concept for determining the key parameters of agent-based models from empirical data instead of setting them artificially was suggested. We first review several agent-based models and the new approaches to determine the key model parameters from historical market data. Based on the agents’ behaviors with heterogeneous personal preferences and interactions, these models are successful in explaining the microscopic origination of the temporal and spatial correlations of financial markets. We then present a novel paradigm combining big-data analysis with agent-based modeling. Specifically, from internet query and stock market data, we extract the information driving forces and develop an agent-based model to simulate the dynamic behaviors of complex financial systems.     

Ups and downs of economics and econophysics — Facebook forecast

What is econophysics and its relationship with economics? What is the state of economics after the global economic crisis, and is there a future for the paradigm of market equilibrium, with imaginary perfect competition and rational agents? Can the next paradigm of economics adopt important assumptions derived from econophysics models: that markets are chaotic systems, striving to extremes as bubbles and crashes show, with psychologically motivated, statistically predictable individual behaviors? Is the future of econophysics, as predicted here, to disappear and become a part of economics? A good test of the current state of econophysics and its methods is the valuation of Facebook immediately after the initial public offering — this forecast indicates that Facebook is highly overvalued, and its IPO valuation of 104 billion dollars is mostly the new financial bubble based on the expectations of unlimited growth, although it’s easy to prove that Facebook is close to the upper limit of its users.     

Special issue on Econophysics

What is “Econophysics"? Who is an “econophysicist"? The coining of a new scientific term, composed of the names of two fields, traditionally considered to be far from each other, brings new dreams to investigators, by mere virtue of a new ensemble of viewpoints. The term “econophysics" has revealed a kinship between the fields of physics and economics, which was not obvious before. The first officially recognized conference by a professional society on “econophysics", Applications of Physics to Financial Analysis (APFA, soon to become APFA1) was held in Dublin in 1999. Since then APFA and its companion meetings have begun to reveal new branches of research from the established pathways explored in applied statistical physics and thus economics (in particular finance). The analysis of fluctuations in financial data by new or modified techniques has led to new insights. Such analysis involves physicists looking for correlation between entities in financial matter in much the same way as they have done for physical systems in their laboratories. This approach leads to useful new methods and results in different outputs. The studies of phase transitions and non-equilibrium effects, including self-organisation have progressed the understanding of many physical phenomena. So why not use the same methodology in a field which is thought to be governed by sociology, psychology, politics and other so called softer science? The observations of deterministic chaos, scaling, in financial time series (tools such as recurrence, plots exploiting symmetries in pricing theory or the use of the wavelet or path integral or renormalisation group method) will still give some work ahead even though all these tools have a basic origin or are rather standard tools nowadays. Characterization of data and theory talks broke new ground in pursuit of e.g. useful strategies or political consequences. One continues to ask, how is it that fluctuations or other agents in a system conspire to give surprising anomalous properties? By broadening discussion to the category of econophysics topics, as covered in APFA2 (held in Liège, Belgium on July 13-15, 2000), we have gained new paradigms to study this question. Several reports to APFA2 are not included in the following to avoid duplicating reports in this proceedings. Very warm and profound acknowledgments are in order here. APFA2 was made possible mainly by the European Physical Society (EPS), the Fond National de la Recherche Scientifique (FNRS, Brussels), the Fonds voor Wetenschappelijk Onderzoek-Vlaanderen (FWO), and the University of Liège.     

Information Theory for Agents in Artificial Intelligence,Psychology, and Economics

This review looks at some of the central relationships between artificial intelligence, psychology, and economics through the lens of information theory, specifically focusing on formal models of decision-theory. In doing so we look at a particular approach that each field has adopted and how information theory has informed the development of the ideas of each field. A key theme is expected utility theory, its connection to information theory, the Bayesian approach to decision-making and forms of (bounded) rationality. What emerges from this review is a broadly unified formal perspective derived from three very different starting points that reflect the unique principles of each field. Each of the three approaches reviewed can, in principle at least, be implemented in a computational model in such a way that, with sufficient computational power, they could be compared with human abilities in complex tasks. However, a central critique that can be applied to all three approaches was first put forward by Savage in The Foundations of Statistics and recently brought to the fore by the economist Binmore: Bayesian approaches to decision-making work in what Savage called ‘small worlds’ but cannot work in ‘large worlds’. This point, in various different guises, is central to some of the current debates about the power of artificial intelligence and its relationship to human-like learning and decision-making. Recent work on artificial intelligence has gone some way to bridging this gap but significant questions remain to be answered in all three fields in order to make progress in producing realistic models of human decision-making in the real world in which we live in.     

A unified model for price return distributions used in econophysics

For a decade, a new theoretical movement called “econophysics” has been initiated by some physicists who began to publish articles devoted to the study of economic and financial phenomena. Since then, econophysicists have written a very prolific literature about the way of characterizing the evolution of financial prices. Today, there is an “extreme diversity” of models recently developed by econophysicists whose research is sometimes presented as an ill-defined field. The objective of this paper is precisely to provide a unified framework in order to contribute to unify econophysics and to base this new field on shared scientific standards.     

Econophysics and the Entropic Foundations of Economics

This paper examines relations between econophysics and the law of entropy as foundations of economic phenomena. Ontological entropy, where actual thermodynamic processes are involved in the flow of energy from the Sun through the biosphere and economy, is distinguished from metaphorical entropy, where similar mathematics used for modeling entropy is employed to model economic phenomena. Areas considered include general equilibrium theory, growth theory, business cycles, ecological economics, urban–regional economics, income and wealth distribution, and financial market dynamics. The power-law distributions studied by econophysicists can reflect anti-entropic forces is emphasized to show how entropic and anti-entropic forces can interact to drive economic dynamics, such as in the interaction between business cycles, financial markets, and income distributions.     

A controllable laboratory stock market for modeling real stock markets

Based on the different research approaches, econophysics can be divided into threedirections: empirical econophysics, computationaleconophysics, and experimental econophysics. Becauseempirical econophysics lacks controllability that is needed to studythe impacts of different external conditions and computational econophysicshas to adopt artificial decision-making processes that are often deviated fromthose of real humans, experimental econophysics tends to overcome theseproblems by offering controllability and using real humans in laboratory experiments.However, to our knowledge, the existing laboratory experiments have not convincinglyreappeared the stylized facts (say, scaling) that have been revealed for realeconomic/financial markets by econophysicists. A most important reason is that in theseexperiments, discrete trading time makes these laboratory markets deviated from realmarkets where trading time is naturally continuous. Here we attempt to overcome thisproblem by designing a continuous double-auction stock-trading market and conductingseveral human experiments in laboratory. As an initial work, the present artificialfinancial market can reproduce some stylized facts related to clustering and scaling.Also, it predicts some other scaling in human behavior dynamics that is hard to achieve inreal markets due to the difficulty in getting the data. Thus, it becomes possible to studyreal stock markets by conducting controlled experiments on such laboratory stock marketsproducing high frequency data.     

A mini-review on econophysics:Comparative study of Chinese and western financial markets

We present a review of our recent research in econophysics, and focus on the comparative study of Chinese and western financial markets. By virtue of concepts and methods in statistical physics, we investigate the time correlations and spatial structure of financial markets based on empirical high-frequency data. We discover that the Chinese stock market shares common basic properties with the western stock markets, such as the fat-tail probability distribution of price returns, the long-range auto-correlation of volatilities, and the persistence probability of volatilities, while it exhibits very different higher-order time correlations of price returns and volatilities, spatial correlations of individual stock prices, and large-fluctuation dynamic behaviors. Furthermore, multi-agent-based models are developed to simulate the microscopic interaction and dynamic evolution of the stock markets.     

经济研究中的物理学

当前的金融危机再次表明,传统经济学作为一门学科缺乏解释力和预测力.造成这个令人失望的状况的根本原因是由于经济学家没有按照科学的范式来发展这个学科.经济学的现状吸引了一群物理学家进入这个学科并形成了一个新的交叉学科——经济物理学,人们期望它在促进经济学科学化的进程中起决定性作用.文章首先简要介绍了经济学的主要内容,说明经济学理论是建立在理性和均衡假定基础之上的;接着论述了为什么经济学还不是一门科学,指出经济学研究不是基于逻辑实证主义原则来开展的;文章还分析了物理学家是如何研究经济问题的,介绍了经济物理学的主要研究内容和研究方法;文章最后提出经济学范式的转变必须从观察和实验出发,经济学理论必须建立在一个合理设计的量纲体系和对实际经济运行过程的正确理解基础之上.     

Modeling and simulating human teamwork behaviors using intelligent agents

Among researchers in multi-agent systems there has been growing interest in using intelligent agents to model and simulate human teamwork behaviors. Teamwork modeling is important for training humans in gaining collaborative skills, for supporting humans in making critical decisions by proactively gathering, fusing, and sharing information, and for building coherent teams with both humans and agents working effectively on intelligence-intensive problems. Teamwork modeling is also challenging because the research has spanned diverse disciplines from business management to cognitive science, human discourse, and distributed artificial intelligence. This article presents an extensive, but not exhaustive, list of work in the field, where the taxonomy is organized along two main dimensions: team social structure and social behaviors. Along the dimension of social structure, we consider agent-only teams and mixed human–agent teams. Along the dimension of social behaviors, we consider collaborative behaviors, communicative behaviors, helping behaviors, and the underpinning of effective teamwork—shared mental models. The contribution of this article is that it presents an organizational framework for analyzing a variety of teamwork simulation systems and for further studying simulated teamwork behaviors.     

Gauge invariant lattice quantum field theory: Implications for statistical properties of high frequency financial markets

We report on initial studies of a quantum field theory defined on a lattice with multi-ladder geometry and the dilation group as a local gauge symmetry. The model is relevant in the cross-disciplinary area of econophysics. A corresponding proposal by Ilinski aimed at gauge modeling in non-equilibrium pricing is implemented in a numerical simulation. We arrive at a probability distribution of relative gains which matches the high frequency historical data of the NASDAQ stock exchange index.     

Between complexity of modelling and modelling of complexity: An essay on econophysics

Econophysics is an emerging field dealing with complex systems and emergent properties. A deeper analysis of themes studied by econophysicists shows that research conducted in this field can be decomposed into two different computational approaches: “statistical econophysics” and “agent-based econophysics”. This methodological scission complicates the definition of the complexity used in econophysics. Therefore, this article aims to clarify what kind of emergences and complexities we can find in econophysics in order to better understand, on one hand, the current scientific modes of reasoning this new field provides; and on the other hand, the future methodological evolution of the field.     

Competition of Multi-Agent Systems: Analysis of a Three-Company Econophysics Model

A three-company econophysics model for competing multi-agent systems in a triangular lattice is analyzed using mean field theory for its phase diagram. Interpretations for the temperature, spin density and lattice structures are presented. Suggestions for the use of this model for econophysics in the context of multi-agent systems are made.     

Toward physics of the mind: Concepts, emotions, consciousness, and symbols

Mathematical approaches to modeling the mind since the 1950s are reviewed, including artificial intelligence, pattern recognition, and neural networks. I analyze difficulties faced by these algorithms and neural networks and relate them to the fundamental inconsistency of logic discovered by Gödel. Mathematical discussions are related to those in neurobiology, psychology, cognitive science, and philosophy. Higher cognitive functions are reviewed including concepts, emotions, instincts, understanding, imagination, intuition, consciousness. Then, I describe a mathematical formulation, unifying the mind mechanisms in a psychologically and neuro-biologically plausible system. A mechanism of the knowledge instinct drives our understanding of the world and serves as a foundation for higher cognitive functions. This mechanism relates aesthetic emotions and perception of beauty to “everyday” functioning of the mind. The article reviews mechanisms of human symbolic ability. I touch on future directions: joint evolution of the mind, language, consciousness, and cultures; mechanisms of differentiation and synthesis; a manifold of aesthetic emotions in music and differentiated instinct for knowledge. I concentrate on elucidating the first principles; review aspects of the theory that have been proven in laboratory research, relationships between the mind and brain; discuss unsolved problems, and outline a number of theoretical predictions, which will have to be tested in future mathematical simulations and neuro-biological research.     

Intentional systems: Review of neurodynamics, modeling, and robotics implementation

We present an intentional neurodynamic theory for higher cognition and intelligence. This theory provides a unifying framework for integrating symbolic and subsymbolic methods as complementary aspects of human intelligence. Top-down symbolic approaches benefit from the vast experience with logical reasoning and with high-level knowledge processing in humans. Connectionist methods use bottom-up approach to generate intelligent behavior by mimicking subsymbolic aspects of the operation of brains and nervous systems. Neurophysiological correlates of intentionality and cognition include sequences of oscillatory patterns of mesoscopic neural activity. Oscillatory patterns are viewed as intermittent representations of generalized symbol systems, with which brains compute. These dynamical symbols are not rigid but flexible. They disappear soon after they emerged through spatio-temporal phase transitions. Intentional neurodynamics provides a solution to the notoriously difficult symbol grounding problem. Some examples of implementations of the corresponding dynamic principles are described in this review.     

Statistical physics of human beings in games:Controlled experiments

It is important to know whether the laws or phenomena in statistical physics for natural systems with non-adaptive agents still hold for social human systems with adaptive agents, because this implies whether it is possible to study or understand social human systems by using statistical physics originating from natural systems. For this purpose, we review the role of human adaptability in four kinds of specific human behaviors, namely, normal behavior, herd behavior, contrarian behavior, and hedge behavior. The approach is based on controlled experiments in the framework of market-directed resource-allocation games. The role of the controlled experiments could be at least two-fold: adopting the real human decision-making process so that the system under consideration could reflect the performance of genuine human beings;making it possible to obtain macroscopic physical properties of a human system by tuning a particular factor of the system,thus directly revealing cause and effect. As a result, both computer simulations and theoretical analyses help to show a few counterparts of some laws or phenomena in statistical physics for social human systems: two-phase phenomena or phase transitions, entropy-related phenomena, and a non-equilibrium steady state. This review highlights the role of human adaptability in these counterparts, and makes it possible to study or understand some particular social human systems by means of statistical physics coming from natural systems.