Tsallis’ non-extensive free energy as a subjective value of an uncertain reward

Recent studies in neuroeconomics and econophysics revealed the importance of reward expectation in decision under uncertainty. Behavioral neuroeconomic studies have proposed that the unpredictability and the probability of an uncertain reward are distinctly encoded as entropy and a distorted probability weight, respectively, in the separate neural systems. However, previous behavioral economic and decision-theoretic models could not quantify reward-seeking and uncertainty aversion in a theoretically consistent manner. In this paper, we have: (i) proposed that generalized Helmholtz free energy in Tsallis’ non-extensive thermostatistics can be utilized to quantify a perceived value of an uncertain reward, and (ii) empirically examined the explanatory powers of the models. Future study directions in neuroeconomics and econophysics by utilizing the Tsallis’ free energy model are discussed.     

Psychophysics of time perception and valuation in temporal discounting of gain and loss

Anomalies in intertemporal choice (e.g. “hyperbolic” discounting and sign effect) have been investigated in econophysics and behavioral neuroeconomics. We experimentally examined the roles of psychophysical effects of time perception and subjective valuation of outcomes (value function) on temporal discounting of gain and loss, by utilizing a q$q$-exponential temporal discounting model developed in Tsallis’s thermostatistics. Consequently, we demonstrated that both “hyperbolic” discounting and the sign effect (i.e. gain is more steeply time-discounted than loss) are due to psychophysical effects of time perception (i.e., nonlinearity and gain–loss asymmetry). Implications of the present study for neuroeconomics and econophysics are discussed.     

A social discounting model based on Tsallis’ statistics

Social decision making (e.g. social discounting and social preferences) has been attracting attention in economics, econophysics, social physics, behavioral psychology, and neuroeconomics. This paper proposes a novel social discounting model based on the deformed algebra developed in the Tsallis’ non-extensive thermostatistics. Furthermore, it is suggested that this model can be utilized to quantify the degree of consistency in social discounting in humans and analyze the relationships between behavioral tendencies in social discounting and other-regarding economic decision making under game-theoretic conditions. Future directions in the application of the model to studies in econophysics, neuroeconomics, and social physics, as well as real-world problems such as the supply of live organ donations, are discussed.     

Economic uncertainty and econophysics

The objective of this paper is to provide a methodological link between econophysics and economics. I will study a key notion of both fields: uncertainty and the ways of thinking about it developed by the two disciplines. After having presented the main economic theories of uncertainty (provided by Knight, Keynes and Hayek), I show how this notion is paradoxically excluded from the economic field. In economics, uncertainty is totally reduced by an a priori Gaussian framework—in contrast to econophysics, which does not use a priori models because it works directly on data. Uncertainty is then not shaped by a specific model, and is partially and temporally reduced as models improve. This way of thinking about uncertainty has echoes in the economic literature. By presenting econophysics as a Knightian method, and a complementary approach to a Hayekian framework, this paper shows that econophysics can be methodologically justified from an economic point of view.     

A comparison of intertemporal choices for oneself versus someone else based on Tsallis’ statistics

Impulsivity and inconsistency in intertemporal choice (discounting) have drawn attention in econophysics and neuroeconomics. Although it is well established that most people often show irrational discounting (e.g., hyperbolic discounting), little is known regarding whether the irrationality is mitigated or not, when the choice was performed by someone else instead of subject herself. This point is important for economic policy-making. In order to compare consistency and impulsivity in choices for oneself versus someone else, we experimentally estimated the consistency parameter q in Tsallis’ statistics-based discount function for oneself and someone else, by assessing the points of subjective equality (indifference points) at 7 delays (1 week to 25 years) in humans. We observed that (i) most people are more inconsistent when the outcomes of intertemporal choice are only relevant to someone else (q=−8.89) than when relevant to oneself (q=−2.63), and (ii) impulsivity, distinguished from inconsistency by utilizing the Tsallis statistics-based q-exponential discount function, is also larger in the choice for someone else than for oneself. Our results indicate that (i) leaving decision-making processes with some others may neither reduce impulsivity nor correct inconsistency and (ii) when q-exponential discounting is utilized, the definition range of q-parameter should be extended to q<0, and smaller (q<1) and larger (q>1) values indicate more inconsistent discounting. Together, the usefulness of the q-exponential discounting for analyzing the dynamic consistency of economic policy was demonstrated in the present study.     

Effects of lesions of the nucleus accumbens core on choice between small certain rewards and large uncertain rewards in rats

Background  

Animals must frequently make choices between alternative courses of action, seeking to maximize the benefit obtained. They must therefore evaluate the magnitude and the likelihood of the available outcomes. Little is known of the neural basis of this process, or what might predispose individuals to be overly conservative or to take risks excessively (avoiding or preferring uncertainty, respectively). The nucleus accumbens core (AcbC) is known to contribute to rats' ability to choose large, delayed rewards over small, immediate rewards; AcbC lesions cause impulsive choice and an impairment in learning with delayed reinforcement. However, it is not known how the AcbC contributes to choice involving probabilistic reinforcement, such as between a large, uncertain reward and a small, certain reward. We examined the effects of excitotoxic lesions of the AcbC on probabilistic choice in rats.     

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.     

The Pietra term structures of financial assets

This paper explores an elemental connection between call options-the most commonly tradable financial derivatives, implied volatility term structures-critical “market information” emanating from call-option prices, and the Pietra index-a quantitative economic measure of societal egalitarianism. Our study: (i) unveils an intrinsic “Pietra structure” of call-option prices; (ii) introduces the notion of the “Pietra term structures” of financial assets; (iii) describes the probabilistic meaning of the Pietra term structures; (iv) establishes an explicit nonlinear one-to-one mapping between the Pietra term structures and the implied volatility term structures of financial assets. The results presented in this paper provide a deep insight into the econophysics of call options and implied volatility term structures.     

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.     

Interference of probabilities and number field structure of quantum models

We study the probabilistic consequences of the choice of the basic number field in the quantum formalism. We demonstrate that by choosing a number field for a linear space representation of quantum model it is possible to describe various interference phenomena. We analyse interference of probabilistic alternatives induced by real, complex, hyperbolic (Clifford) and p‐adic representations.     

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.     

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.     

Lapse of time effects on tax evasion in an agent-based econophysics model

We investigate an inhomogeneous Ising model in the context of tax evasion dynamics where different types of agents are parameterized via local temperatures and magnetic fields. In particular, we analyze the impact of lapse of time effects (i.e. backauditing) and endogenously determined penalty rates on tax compliance. Both features contribute to a microfoundation of agent-based econophysics models of tax evasion.     

Prediction of uncertain frequency response function bounds using polynomial chaos expansion

A probabilistic method is developed to predict the uncertainty bounds on Frequency Response Functions (FRFs) developed from Finite Element models. A non-intrusive Polynomial Chaos Expansion (PCE) method is used to predict uncertainty regression models of the various parameters that make up a curvefit of the FRF: natural frequencies, damping ratios, complex amplitudes, mass and stiffness residuals, by making use of an efficient Latin Hypercube technique. These uncertainty models are then combined to efficiently determine PDFs of the parameters and also the uncertainty bounds of the FRFs. The approach is demonstrated using two examples; a simple beam containing uncertainty in Young's Modulus, and a full-scale aircraft composite wing model containing uncertainties in both Young's modulus and the shear modulus. The results were compared with Monte Carlo Simulation (MCS) and it was found that the parameter PDFs and FRF error bounds obtained using a 2nd-order PCE model agreed very well whilst requiring significantly less computation.     

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。     

The mis-specification of the expected rescaled adjusted range

Rescaled range analysis has regained popularity in the recent econophysics literature as a means of identifying long-term dependence in time-series data. Conclusions derived from the rescaled adjusted range statistic are conditional however upon the choice of an appropriate benchmark against which calculated results can be compared. One recent paper in Physica A by Couillard and Davison [Physica A 348 (2005) 404] concludes that the Anis and Lloyd [Biometrika 63 (1976) 111] model of the expected rescaled adjusted range is more accurate than that proposed by Peters [Fractal Market Analysis, Wiley, New York, 1994]. This finding is contrary to the evidence presented by Peters. This paper reveals significant inconsistencies in the empirical results reported by Peters, which when considered, support the conclusions of Couillard and Davison and explain the apparent contradiction in their results versus those of Peters.     

Numerical approach for quantification of epistemic uncertainty

In the field of uncertainty quantification, uncertainty in the governing equations may assume two forms: aleatory uncertainty and epistemic uncertainty. Aleatory uncertainty can be characterised by known probability distributions whilst epistemic uncertainty arises from a lack of knowledge of probabilistic information. While extensive research efforts have been devoted to the numerical treatment of aleatory uncertainty, little attention has been given to the quantification of epistemic uncertainty. In this paper, we propose a numerical framework for quantification of epistemic uncertainty. The proposed methodology does not require any probabilistic information on uncertain input parameters. The method only necessitates an estimate of the range of the uncertain variables that encapsulates the true range of the input variables with overwhelming probability. To quantify the epistemic uncertainty, we solve an encapsulation problem, which is a solution to the original governing equations defined on the estimated range of the input variables. We discuss solution strategies for solving the encapsulation problem and the sufficient conditions under which the numerical solution can serve as a good estimator for capturing the effects of the epistemic uncertainty. In the case where probability distributions of the epistemic variables become known a posteriori, we can use the information to post-process the solution and evaluate solution statistics. Convergence results are also established for such cases, along with strategies for dealing with mixed aleatory and epistemic uncertainty. Several numerical examples are presented to demonstrate the procedure and properties of the proposed methodology.     

A quantum model for the stock market

Beginning with several basic hypotheses of quantum mechanics, we give a new quantum model in econophysics. In this model, we define wave functions and operators of the stock market to establish the Schrödinger equation for stock price. Based on this theoretical framework, an example of a driven infinite quantum well is considered, in which we use a cosine distribution to simulate the state of stock price in equilibrium. After adding an external field into the Hamiltonian to analytically calculate the wave function, the distribution and the average value of the rate of return are shown.     

金融物理的若干基本问题与研究进展(Ⅰ)--价格的统计分析与价格涨落的随机过程模型

金融物理学是物理学概念和方法应用于金融分析的一门新的交叉学科，近年来受到人们的广泛关注．文章简述了金融物理的研究方向和研究方法，重点讨论了价格涨落的统计分析和相关的物理模型．