Analysis of the purchase and consumer behaviour towards direct purchase of food

The paper presented the methodic as well as selected results of own empiric research (face-to-face questionings in seven different questioning regions, n = 1488) to the analysis of the shopping behaviour and the attitudes towards direct purchase. To the analysis of the buying patterns a Kaufverhaltensindex (KVI) was introduced. In the regional comparison will be clear that the questioning regions of Baden-Württemberg and North Rhine-Westphalia show KVI by far highest, while the eastern questioning-regions (particularly Thuringia) are marked by the lowest KVI. Within the attitude measurement (factor analysis) six attitude-dimensions could be extracted. In particular are these: confidence in the emotional product quality, price- consciousness, socio-political motivation, confidence in the conventional food-offer, the orientation to convenience food and health consciousness. The analysis of the buying patterns-relevance (regression analysis) showed the distinct dominance of the attitude dimension to price consciousness. From the segmentation of the whole random check (cluster analysis) result four clusters (DV-sympathizers, price-conscious not-DV- buyers, direct buyers oriented to organic farming and a conventionally oriented DV-cluster). The analytic interpretation of buying patterns and soziodemographic variables as well as the regional affiliation allows a continuing characteristic and differentiation of the found clusters

Predictability of extreme events in social media

It is part of our daily social-media experience that seemingly ordinary items (videos, news, publications, etc.) unexpectedly gain an enormous amount of attention. Here we investigate how unexpected these events are. We propose a method that, given some information on the items, quantifies the predictability of events, i.e., the potential of identifying in advance the most successful items defined as the upper bound for the quality of any prediction based on the same information. Applying this method to different data, ranging from views in YouTube videos to posts in Usenet discussion groups, we invariantly find that the predictability increases for the most extreme events. This indicates that, despite the inherently stochastic collective dynamics of users, efficient prediction is possible for the most extreme events.Comment: 13 pages, 3 figure

Stochastic dynamics and the predictability of big hits in online videos

The competition for the attention of users is a central element of the Internet. Crucial issues are the origin and predictability of big hits, the few items that capture a big portion of the total attention. We address these issues analyzing 10 million time series of videos' views from YouTube. We find that the average gain of views is linearly proportional to the number of views a video already has, in agreement with usual rich-get-richer mechanisms and Gibrat's law, but this fails to explain the prevalence of big hits. The reason is that the fluctuations around the average views are themselves heavy tailed. Based on these empirical observations, we propose a stochastic differential equation with L\'evy noise as a model of the dynamics of videos. We show how this model is substantially better in estimating the probability of an ordinary item becoming a big hit, which is considerably underestimated in the traditional proportional-growth models.Comment: Manuscript (8 pages and 5 figures

Re-Examination of Possible Bimodality of GALLEX Solar Neutrino Data

The histogram formed from published capture-rate measurements for the GALLEX solar neutrino experiment is bimodal, showing two distinct peaks. On the other hand, the histogram formed from published measurements derived from the similar GNO experiment is unimodal, showing only one peak. However, the two experiments differ in run durations: GALLEX runs are either three weeks or four weeks (approximately) in duration, whereas GNO runs are all about four weeks in duration. When we form 3-week and 4-week subsets of the GALLEX data, we find that the relevant histograms are unimodal. The upper peak arises mainly from the 3-week runs, and the lower peak from the 4-week runs. The 4-week subset of the GALLEX dataset is found to be similar to the GNO dataset. A recent re-analysis of GALLEX data leads to a unimodal histogram.Comment: 14 pages, 8 figure

Characterizing Weak Chaos using Time Series of Lyapunov Exponents

We investigate chaos in mixed-phase-space Hamiltonian systems using time series of the finite- time Lyapunov exponents. The methodology we propose uses the number of Lyapunov exponents close to zero to define regimes of ordered (stickiness), semi-ordered (or semi-chaotic), and strongly chaotic motion. The dynamics is then investigated looking at the consecutive time spent in each regime, the transition between different regimes, and the regions in the phase-space associated to them. Applying our methodology to a chain of coupled standard maps we obtain: (i) that it allows for an improved numerical characterization of stickiness in high-dimensional Hamiltonian systems, when compared to the previous analyses based on the distribution of recurrence times; (ii) that the transition probabilities between different regimes are determined by the phase-space volume associated to the corresponding regions; (iii) the dependence of the Lyapunov exponents with the coupling strength.Comment: 8 pages, 6 figure

Extracting information from S-curves of language change

It is well accepted that adoption of innovations are described by S-curves (slow start, accelerating period, and slow end). In this paper, we analyze how much information on the dynamics of innovation spreading can be obtained from a quantitative description of S-curves. We focus on the adoption of linguistic innovations for which detailed databases of written texts from the last 200 years allow for an unprecedented statistical precision. Combining data analysis with simulations of simple models (e.g., the Bass dynamics on complex networks) we identify signatures of endogenous and exogenous factors in the S-curves of adoption. We propose a measure to quantify the strength of these factors and three different methods to estimate it from S-curves. We obtain cases in which the exogenous factors are dominant (in the adoption of German orthographic reforms and of one irregular verb) and cases in which endogenous factors are dominant (in the adoption of conventions for romanization of Russian names and in the regularization of most studied verbs). These results show that the shape of S-curve is not universal and contains information on the adoption mechanism. (published at "J. R. Soc. Interface, vol. 11, no. 101, (2014) 1044"; DOI: http://dx.doi.org/10.1098/rsif.2014.1044)Comment: 9 pages, 5 figures, Supplementary Material is available at http://dx.doi.org/10.6084/m9.figshare.122178