arXiv subjects

Didier Sornette

Publications and source records attributed to Didier Sornette.

At least 235 records · Page 13Linked to original sources

arXiv1999

Acoustic radiation controls friction: Evidence from a spring-block experiment

Brittle failures of materials and earthquakes generate acoustic/seismic waves which lead to radiation damping feedbacks that should be introduced in the dynamical equations of crack motion. We present direct experimental evidence of the importance of this feedback on the acoustic noise spectrum of well-controlled spring-block sliding experiments performed on a variety of smooth surfaces. The full noise spectrum is quantitatively explained by a simple noisy harmonic oscillator equation with a radiation damping force proportional to the derivative of the acceleration, added to a standard viscous term.

cond-mat↗

arXiv1999

Punctuated vortex coalescence and discrete scale invariance in two-dimensional turbulence

We present experimental evidence and theoretical arguments showing that the time-evolution of freely decaying 2-d turbulence is governed by a {\it discrete} time scale invariance rather than a continuous time scale invariance. Physically, this reflects that the time-evolution of the merging of vortices is not smooth but punctuated, leading to a prefered scale factor and as a consequence to log-periodic oscillations. From a thorough analysis of freely decaying 2-d turbulence experiments, we show that the number of vortices, their radius and separation display log-periodic oscillations as a function of time with an average log-frequency of ~ 4-5 corresponding to a prefered scaling ratio of ~ 1.2-1.3

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arXiv1999

Critical Crashes

We argue that the word ``critical'' in the title is not purely literary. Based on our and other previous work on nonlinear complex dynamical systems, we summarize present evidence, on the Oct. 1929, Oct. 1987, Oct. 1987 Hong-Kong, Aug. 1998 global market events and on the 1985 Forex event, for the hypothesis advanced four years ago that stock market crashes are caused by the slow buildup of long-range correlations between traders leading to a collapse of the stock market in one critical instant. We qualify the log-periodic oscillations using a novel non-parametric method that does not rely on any fit: the corresponding log-periodogram exhibits a strong statistically significant peak for all six crashes examined, pointing at approximately the same prefered scaling ratio around 2.

cond-mat.stat-mech↗

arXiv1998

Discrete scale invariance and complex dimensions

We discuss the concept of discrete scale invariance and how it leads to complex critical exponents (or dimensions), i.e. to the log-periodic corrections to scaling. After their initial suggestion as formal solutions of renormalization group equations in the seventies, complex exponents have been studied in the eighties in relation to various problems of physics embedded in hierarchical systems. Only recently has it been realized that discrete scale invariance and its associated complex exponents may appear ``spontaneously'' in euclidean systems, i.e. without the need for a pre-existing hierarchy. Examples are diffusion-limited-aggregation clusters, rupture in heterogeneous systems, earthquakes, animals (a generalization of percolation) among many other systems. We review the known mechanisms for the spontaneous generation of discrete scale invariance and provide an extensive list of situations where complex exponents have been found. This is done in order to provide a basis for a better fundamental understanding of discrete scale invariance. The main motivation to study discrete scale invariance and its signatures is that it provides new insights in the underlying mechanisms of scale invariance. It may also be very interesting for prediction purposes.

cond-mat.stat-mech↗

arXiv1998

Modeling the Stock Market prior to large crashes

We propose that the minimal requirements for a model of stock market price fluctuations should comprise time asymmetry, robustness with respect to connectivity between agents, ``bounded rationality'' and a probabilistic description. We also compare extensively two previously proposed models of log-periodic behavior of the stock market index prior to a large crash. We find that the model which follows the above requirements outperforms the other with a high statistical significance.

cond-mat↗

arXiv1998

Crashes as Critical Points

We study a rational expectation model of bubbles and crashes. The model has two components : (1) our key assumption is that a crash may be caused by local self-reinforcing imitation between noise traders. If the tendency for noise traders to imitate their nearest neighbors increases up to a certain point called the ``critical'' point, all noise traders may place the same order (sell) at the same time, thus causing a crash. The interplay between the progressive strengthening of imitation and the ubiquity of noise is characterized by the hazard rate, i.e. the probability per unit time that the crash will happen in the next instant if it has not happened yet. (2) Since the crash is not a certain deterministic outcome of the bubble, it remains rational for traders to remain invested provided they are compensated by a higher rate of growth of the bubble for taking the risk of a crash. Our model distinguishes between the end of the bubble and the time of the crash,: the rational expectation constraint has the specific implication that the date of the crash must be random. The theoretical death of the bubble is not the time of the crash because the crash could happen at any time before, even though this is not very likely. The death of the bubble is the most probable time for the crash. There also exists a finite probability of attaining the end of the bubble without crash. Our model has specific predictions about the presence of certain critical log-periodic patterns in pre-crash prices, associated with the deterministic components of the bubble mechanism. We provide empirical evidence showing that these patterns were indeed present before the crashes of 1929, 1962 and 1987 on Wall Street and the 1997 crash on the Hong Kong Stock Exchange. These results are compared with statistical tests on synthetic data.

cond-mat↗

arXiv1998

Economic returns of research: the Pareto law and its implications

At what level should government or companies support research? This complex multi-faceted question encompasses such qualitative bonus as satisfying natural human curiosity, the quest for knowledge and the impact on education and culture, but one of its most scrutinized component reduces to the assessment of economic performance and wealth creation derived from research. In certain areas such as biotechnology, semi-conductor physics, optical communications, the impact of basic research is direct while, in other disciplines, the path from discovery to applications is full of surprises. As a consequence, there are persistent uncertainties in the quantification of the exact economic returns of public expenditure on basic research. Here, we suggest that these uncertainties have a fundamental origin to be found in the interplay between the intrinsic ``fat tail'' power law nature of the distribution of economic returns, characterized by a mathematically diverging variance, and the stochastic character of discovery rates. In the regime where the cumulative economic wealth derived from research is expected to exhibit a long-term positive trend, we show that strong fluctuations blur out significantly the short-time scales: a few major unpredictable innovations may provide a finite fraction of the total creation of wealth. In such a scenario, any attempt to assess the economic impact of research over a finite time horizon encompassing only a small number of major discoveries is bound to be highly unreliable. New tools, developed in the theory of self-similar and complex systems to tackle similar extreme fluctuations in Nature can be adapted to measure the economic benefits of research, which is intimately associated to this large variability.

cond-mat.stat-mech↗

arXiv1998

Large deviations and portfolio optimization

Risk control and optimal diversification constitute a major focus in the finance and insurance industries as well as, more or less consciously, in our everyday life. We present a discussion of the characterization of risks and of the optimization of portfolios that starts from a simple illustrative model and ends by a general functional integral formulation. A major theme is that risk, usually thought one-dimensional in the conventional mean-variance approach, has to be addressed by the full distribution of losses. Furthermore, the time-horizon of the investment is shown to play a major role. We show the importance of accounting for large fluctuations and use the theory of Cramér for large deviations in this context. We first treat a simple model with a single risky asset that examplifies the distinction between the average return and the typical return, the role of large deviations in multiplicative processes, and the different optimal strategies for the investors depending on their size. We then analyze the case of assets whose price variations are distributed according to exponential laws, a situation that is found to describe reasonably well daily price variations. Several portfolio optimization strategies are presented that aim at controlling large risks. We end by extending the standard mean-variance portfolio optimization theory, first within the quasi-Gaussian approximation and then using a general formulation for non-Gaussian correlated assets in terms of the formalism of functional integrals developed in the field theory of critical phenomena.

cond-mat.stat-mech↗

arXiv1998

Self-organized critical random directed polymers

We uncover a nontrivial signature of the hierarchical structure of quasi-degenerate random directed polymers (RDPs) at zero temperature in 1+1 dimensional lattices. Using a cylindrical geometry with circumference $8 \leq W \leq 512$, we study the differences in configurations taken by RDPs forced to pass through points displaced successively by one unit lattice mesh. The transition between two successive configurations (interpreted as an avalanche) defines an area $S$. The distribution of moderatly sized avalanches is found to be a power-law $P(S) dS \sim S^{-(1+μ)} dS$. Using a hierarchical formulation based on the length scales $W^{2\over 3}$ (transverse excursion) and the distance $W^{{2\over 3}α}$ between quasi-degenerate ground states (with $0<α\le 1$), we determine $μ= {2\over 5}$, in excellent agreement with numerical simulations by a transfer matrix method. This power-law is valid up to a maximum size $S_{5\over 3} \sim W^{5\over 3}$. There is another population of avalanches which, for characteristic sizes beyond $S_{5\over 3}$, obeys $P(S) dS \sim \exp(-(S/S_{5\over 3})^3) dS$ also confirmed numerically. The first population corresponds to almost degenerate ground states, providing a direct evidence of ``weak replica symmetry breaking'', while the second population is associated with different optimal states separated by the typical fluctuation $W^{2\over 3}$ of a single RDP.

cond-mat.stat-mech↗

arXiv1997

Log-periodic Oscillations for Biased Diffusion in 3D Random Lattices

Random walks with a fixed bias direction on randomly diluted cubic lattices far above the percolation threshold exhibit log-periodic oscillations in the effective exponent versus time. A scaling argument accounts for the numerical results in the limit of large biases and small dilution and shows the importance of the interplay of these two ingredients in the generation of the log-periodicity. These results show that log-periodicity is the dominant effect compared to previous predictions of and reports on anomalous diffusion.

cond-mat.stat-mech↗

arXiv1997

Fine structure and complex exponents in power law distributions from random maps

Discrete scale invariance (DSI) has recently been documented in time-to-failure rupture, earthquake processes and financial crashes, in the fractal geometry of growth processes and in random systems. The main signature of DSI is the presence of log-periodic oscillations correcting the usual power laws, corresponding to complex exponents. Log-periodic structures are important because they reveal the presence of preferred scaling ratios of the underlying physical processes. Here, we present new evidence of log-periodicity overlaying the leading power law behavior of probability density distributions of affine random maps with parametric noise. The log-periodicity is due to intermittent amplifying multiplicative events. We quantify precisely the progressive smoothing of the log-periodic structures as the randomness increases and find a large robustness. Our results provide useful markers for the search of log-periodicity in numerical and experimental data.

cond-mat.stat-mech↗

arXiv1997

Self-organized criticality in stick-slip models with periodic boundaries

A spring-block model governed by threshold dynamics and driven by temporally increasing spring constants is investigated. Due to its novel multiplicative driving, criticality occurs even with periodic boundary conditions via a mechanism distinct from that of previous models. This mechanism is dictated by a coarsening process. The results show a high degree of universality. The observed behavior should be relevant to a class of systems approaching equilibrium via a punctuated threshold dynamics.

cond-mat.stat-mech↗

arXiv1997

Large financial crashes

We propose that large stock market crashes are analogous to critical points studied in statistical physics with log-periodic correction to scaling. We extend our previous renormalization group model of stock market prices prior to and after crashes [D. Sornette et al., J.Phys.I France 6, 167, 1996] by including the first non-linear correction. This predicts the existence of a log-frequency shift over time in the log-periodic oscillations prior to a crash. This is tested on the two largest historical crashes of the century, the october 1929 and october 1987 crashes, by fitting the stock market index over an interval of 8 years prior to the crashes. The good quality of the fits, as well as the consistency of the parameter values obtained from the two crashes, promote the theory that crashes have their origin in the collective ``crowd'' behavior of many interacting agents.

cond-mat↗

arXiv1996

Convergent multiplicative processes repelled from zero: power laws and truncated power laws

Random multiplicative processes $w_t =λ_1 λ_2 ... λ_t$ (with < λ_j > 0 ) lead, in the presence of a boundary constraint, to a distribution $P(w_t)$ in the form of a power law $w_t^{-(1+μ)}$. We provide a simple and physically intuitive derivation of this result based on a random walk analogy and show the following: 1) the result applies to the asymptotic ($t \to \infty$) distribution of $w_t$ and should be distinguished from the central limit theorem which is a statement on the asymptotic distribution of the reduced variable ${1 \over \sqrt{t}}(log w_t -< log w_t >)$; 2) the necessary and sufficient conditions for $P(w_t)$ to be a power law are that < 0 (corresponding to a drift $w_t \to 0$) and that $w_t$ not be allowed to become too small. We discuss several models, previously unrelated, showing the common underlying mechanism for the generation of power laws by multiplicative processes: the variable $\log w_t$ undergoes a random walk biased to the left but is bounded by a repulsive ''force''. We give an approximate treatment, which becomes exact for narrow or log-normal distributions of $λ$, in terms of the Fokker-Planck equation. 3) For all these models, the exponent $μ$ is shown exactly to be the solution of $\langle λ^μ \rangle = 1$ and is therefore non-universal and depends on the distribution of $λ$.

cond-mat↗

arXiv1996

Comment on ``Turbulent cascades in foreign exchange markets''

Recently, Ghashghaie et al. have shown that some statistical aspects of fully developed turbulence and exchange rate fluctuations exhibit striking similarities (Nature 381, 767 (1996)). The authors then suggested that the two problems might be deeply connected, and speculated on the existence of an `information cascade' which would play the role in finance of the well known Kolmogorov energy cascade in turbulence. Here we want to convince the reader that the two problems differ on a fundamental aspect, namely, correlations.

cond-mat↗

arXiv1995

Rank-Ordering Statistics of Extreme Events: Application to the Distribution of Large Earthquakes

Rank-ordering statistics provides a perspective on the rare, largest elements of a population, whereas the statistics of cumulative distributions are dominated by the more numerous small events. The exponent of a power law distribution can be determined with good accuracy by rank-ordering statistics from the observation of only a few tens of the largest events. Using analytical results and synthetic tests, we quantify the systematic and the random errors. We also study the case of a distribution defined by two branches, each having a power law distribution, one defined for the largest events and the other for smaller events, with application to the World-Wide (Harvard) and Southern California earthquake catalogs. In the case of the Harvard moment catalog, we make more precise earlier claims of the existence of a transition of the earthquake magnitude distribution between small and large earthquakes; the $b$-values are $b_2 = 2.3 \pm 0.3$ for large shallow earthquakes and $b_1 = 1.00 \pm 0.02$ for smaller shallow earthquakes. However, the cross-over magnitude between the two distributions is ill-defined. The data available at present do not provide a strong constraint on the cross-over which has a $50\%$ probability of being between magnitudes $7.1$ and $7.6$ for shallow earthquakes; this interval may be too conservatively estimated. Thus, any influence of a universal geometry of rupture on the distribution of earthquakes world-wide is ill-defined at best. We caution that there is no direct evidence to confirm the hypothesis that the large-moment branch is indeed a power law. In fact, a gamma distribution fits the entire suite of earthquake moments from the smallest to the largest satisfactorily. There is no evidence that the earthquakes of the Southern California catalog have a distribution with two

cond-mat↗

arXiv1995

Stock market crashes, Precursors and Replicas

We present an analysis of the time behavior of the $S\&P500$ (Standard and Poors) New York stock exchange index before and after the October 1987 market crash and identify precursory patterns as well as aftershock signatures and characteristic oscillations of relaxation. Combined, they all suggest a picture of a kind of dynamical critical point, with characteristic log-periodic signatures, similar to what has been found recently for earthquakes. These observations are confirmed on other smaller crashes, and strengthen the view of the stockmarket as an example of a self-organizing cooperative system.

cond-mat↗

arXiv1995

Faults Self-Organized by Repeated Earthquakes in a Quasi-Static Antiplane Crack Model

We study a 2D quasi-static discrete {\it crack} anti-plane model of a tectonic plate with long range elastic forces and quenched disorder. The plate is driven at its border and the load is transfered to all elements through elastic forces. This model can be considered as belonging to the class of self-organized models which may exhibit spontaneous criticality, with four additional ingredients compared to sandpile models, namely quenched disorder, boundary driving, long range forces and fast time crack rules. In this ''crack'' model, as in the ''dislocation'' version previously studied, we find that the occurrence of repeated earthquakes organizes the activity on well-defined fault-like structures. In contrast with the ''dislocation'' model, after a transient, the time evolution becomes periodic with run-aways ending each cycle. This stems from the ''crack'' stress transfer rule preventing criticality to organize in favor of cyclic behavior. For sufficiently large disorder and weak stress drop, these large events are preceded by a complex space-time history of foreshock activity, characterized by a Gutenberg-Richter power law distribution with universal exponent $B=1 \pm 0.05$. This is similar to a power law distribution of small nucleating droplets before the nucleation of the macroscopic phase in a first-order phase transition. For large disorder and large stress drop, and for certain specific initial disorder configurations, the stress field becomes frustrated in fast time : out-of-plane deformations (thrust and normal faulting) and/or a genuine dynamics must be introduced to resolve this frustration.

cond-mat↗