Dynamic Fracture in Single Crystal Silicon

We have measured the velocity of a running crack in brittle single crystal silicon as a function of energy flow to the crack tip. The experiments are designed to permit direct comparison with molecular dynamics simulations; therefore the experiments provide an indirect but sensitive test of interatomic potentials. Performing molecular dynamics simulations of brittle crack motion at the atomic scale we find that experiments and simulations disagree showing that interatomic potentials are not yet well understood.Comment: 4 pages, 4 figures, 19 reference

Capacity Investment Timing by Start-ups and Established Firms in New Markets

We analyze the competitive capacity investment timing decisions of both established firms and start-ups entering new markets, which have a high degree of demand uncertainty. Firms may invest in capacity early (when uncertainty is high) or late (when uncertainty has been resolved), possibly at different costs. Established firms choose an investment timing and capacity level to maximize expected profits, whereas start-ups make those choices to maximize the probability of survival. When a start-up competes against an established firm, we find that when demand uncertainty is high and costs do not decline too severely over time, the start-up takes a leadership role and invests first in capacity, whereas the established firm follows; by contrast, when two established firms compete in an otherwise identical game, both firms invest late. We conclude that the threat of firm failure significantly impacts the dynamics of competition involving start-ups

From time series to superstatistics

Complex nonequilibrium systems are often effectively described by a `statistics of a statistics', in short, a `superstatistics'. We describe how to proceed from a given experimental time series to a superstatistical description. We argue that many experimental data fall into three different universality classes: chi^2-superstatistics (Tsallis statistics), inverse chi^2-superstatistics, and log-normal superstatistics. We discuss how to extract the two relevant well separated superstatistical time scales tau and T, the probability density of the superstatistical parameter beta, and the correlation function for beta from the experimental data. We illustrate our approach by applying it to velocity time series measured in turbulent Taylor-Couette flow, which is well described by log-normal superstatistics and exhibits clear time scale separation.Comment: 7 pages, 9 figure

Bridging the ARCH model for finance and nonextensive entropy

Engle's ARCH algorithm is a generator of stochastic time series for financial returns (and similar quantities) characterized by a time-dependent variance. It involves a memory parameter $b$ ($b=0$ corresponds to {\it no memory}), and the noise is currently chosen to be Gaussian. We assume here a generalized noise, namely $q_n$-Gaussian, characterized by an index $q_{n} \in {\cal R}$ ($q_{n}=1$ recovers the Gaussian case, and $q_n>1$ corresponds to tailed distributions). We then match the second and fourth momenta of the ARCH return distribution with those associated with the $q$-Gaussian distribution obtained through optimization of the entropy S_{q}=\frac{% 1-\sum_{i} {p_i}^q}{q-1}, basis of nonextensive statistical mechanics. The outcome is an {\it analytic} distribution for the returns, where an unique $q\ge q_n$ corresponds to each pair $(b,q_n)$ ($q=q_n$ if $b=0$). This distribution is compared with numerical results and appears to be remarkably precise. This system constitutes a simple, low-dimensional, dynamical mechanism which accommodates well within the current nonextensive framework.Comment: 4 pages, 5 figures.Figure 4 fixe