arXiv subjects
Publications and source records attributed to Dietrich Stauffer.
The opinion dynamics of economic agents is modeled with the link structure influenced by the resulting opinions: Links between people of nearly the same opinion are more stable than those between people of vastly different opinions. A simple scaling law describes the number of surviving final opinion as a function of the numbers of agents and of possible opinions.
We propose a stochastic model of interactive formation of individual expectations regarding the business climate in an industry. Our model is motivated by a business climate survey cond ucted since 1960 in Germany by the Ifo-institute (www.ifo.de). In accordance with the data structure of this survey, in our model there is asso ciated to each economic agent (business manager) a random variable with a three-element state space representing her possible types of expectations. The evolution of individual expectations in a finite population is then modeled as a spatio-temporal stochastic process with local interaction between agents. An appropriate structure of the interaction between agents in our setting turns out to be provided by a Festinger-function (in physics called energy function or Ham iltonian) of the Blume-Capel type. Time series of the fractions of agents holding each type of expectations are obtained for the model by Monte Carlo simulations. We find that our model reproduces some generic features of the empirical time series obtained from the German business-climate data, in particu lar the occurrence of abrupt large but rare swings. In our model, such swings occur as spontaneous phase changes between macroscopic states.
Using Monte Carlo methods, we simulated the effects of bias in generation and elimination of paralogs on the size distribution of paralog groups. It was found that the function describing the decay of the number of paralog groups with their size depends on the ratio between the probability of duplications of genes and their deletions, which corresponds to different selection pressures on the genome size. Slightly different slopes of curves describing the decay of the number of paralog groups with their size were also observed when the threshold of homology between paralogous sequences was changed.
We propose a generic model of eco-systems, with a {\it hierarchical} food web structure. In our computer simulations we let the eco-system evolve continuously for so long that that we can monitor extinctions as well as speciations over geological time scales. {\it Speciation} leads not only to horizontal diversification of species at any given trophic level but also to vertical bio-diversity that accounts for the emergence of complex species from simpler forms of life. We find that five or six trophic levels appear as the eco-system evolves for sufficiently long time, starting initially from just one single level. Moreover, the time intervals between the successive collections of ecological data is so short that we could also study ``micro''-evolution of the eco-system, i.e., the birth, ageing and death of individual organisms.
A simple discrete model for magnetic structures of chromium nanoclusters, found with the help of local-spin DFT by Kohl and Bertsch, still confirms their conclusion that in most of the clusters the magnetic moments are not collinear; instead, in most of the cases they are oriented within one plane. We also simulate the destruction of the anti-ferromagnetic ordering by thermal fluctuations.
We review the possibilities and difficulties for statistical physicists if they apply their methods to biology, economics, or sociology.
The present review deals with the computer simulation of biological ageing as well as its demographic consequences for industrialized societies.
This paper proposes a percolation-based model of new-product diffusion in the spirit of Solomon et al. (2000) and Goldenberg et al. (2000). A consumer buys the new product if she has formed her individual valuation of the product (reservation price) and if this valuation is greater or equal than the price of the product announced by the firm in a given period. Our model differs from previous percolation-based models of new-product diffusion in two respects. First, we consider macroscopic feedback effects affecting the supply or the demand side of the market (or both). Second, a consumer who did not buy the product in the period in which her valuation was formed remains a potential buyer and buys in some later period if and when her individual valuation equals or exceeds the price of the product. Unlike most previous models of new-product diffusion, our framework accounts for the empirical finding of long tails characteristic for early stages of innovation diffusion.
Monte Carlo simulations of the Sznajd model with bounded confidence for varying dimensions show that the probability to reach a consensus in d-dimensional lattices depends only weakly on d. but strongly on the number Q of possible opinions: Q=3 usually leads to consensus, Q=4 does not.
In the Sznajd model of 2000, a pair of neighbouring agents on a square lattice convinces its six neighbours of the pair opinion if and only if the two agents of the pair share the same opinion. It differs from other consensus models of sociophysics (Deffuant et al., Hegselmann and Krause) by having integer opinions like $\pm 1$ instead of continuous opinions. The basic results and the progress since the last review are summarized here.
Very recently we have developed a dynamic network model for eco-systems that achieved ``unification'' of ``micro'' and ``macro''-evolution. We now propose an extension of our model so as to stabilize the eco-system and describe {\it speciation} in a more realistic manner.
We implement the cellular automata model proposed by Stauffer and Weisbuch in 1992 to describe the response of the immune system to antigens in the presence of medications. The model contains two thresholds, $θ_1$ and $θ_2$, suggested by de Boer, Segel, and Perelson to present the minimum field needed to stimulate the proliferation of the receptors and to suppress it, respectively. The influence of the drug is mimicked by increasing the second threshold, thus enhancing the immune response. If this increase is too strong, the immune response is triggered in the whole immune repertoire, causing it to attack the own body. This effect is seen in our simulations to depend both on the ratio of the thresholds and on their absolute values.
We incorporate the generic hierarchical architecture of foodwebs into a "{\it unified}" model that describes both "micro" and "macro" evolutions within a single theoretical framework. This model describes the "micro" -evolution in detail by accounting for the birth, ageing and natural death of individual organisms as well as prey-predator interactions on a hierarchical dynamic food web. It also provides a natural description of random mutations and speciation/orgination of species as well as their extinctions. The distribution of lifetimes of species follows an approximate power law only over a limited regime.
This article summarizes some physics aspects of the market model of Weisbuch and Stauffer, Physica A (2003): How do demand and quality expectation adjust to each other in a buyer dominated market like cinema visits? And how can business cycles be modelled?
Using an inverse of the standard linear congruential random number generator, large randomly occupied lattices can be visited by a random walker without having to determine the occupation status of every lattice site in advance. In seven dimensions, at the percolation threshold with L^7 sites and L < 420, we confirm the expected time-dependence of the end-to-end distance (including the corrections to the asymptotic behavior).
The potential for associative recall of diluted neuronal networks is investigated with respect to several biologically relevant configurations, more specifically the position of the cells along the input space and the spatial distribution of their connections. First we put the asymmetric Hopfield model onto a scale-free Barabasi-Albert network. Then, a geometrical diluted architecture, which maps from L-bit input patterns into $N$-neurons networks, with R=N/L<1 (we adopt R=0.1, 0.2 and 0.3), is considered. The distribution of the connections between cells along the one-dimensional input space follows a normal distribution centered at each cell, in the sense that cells that are closer to each other have increased probability to interconnect. The models also explicitly consider the placement of the neuronal cells along the input space in such a way that denser regions of that space tend to become denser, therefore implementing a special case of the Barabasi-Albert connecting scheme. The obtained results indicate that, for the case of the considered stimuli and noise, the network performance increases with the spatial uniformity of cell distribution.
Neural networks are supposed to recognise blurred images (or patterns) of $N$ pixels (bits) each. Application of the network to an initial blurred version of one of $P$ pre-assigned patterns should converge to the correct pattern. In the "standard" Hopfield model, the $N$ "neurons'' are connected to each other via $N^2$ bonds which contain the information on the stored patterns. Thus computer time and memory in general grow with $N^2$. The Hebb rule assigns synaptic coupling strengths proportional to the overlap of the stored patterns at the two coupled neurons. Here we simulate the Hopfield model on the Barabasi-Albert scale-free network, in which each newly added neuron is connected to only $m$ other neurons, and at the end the number of neurons with $q$ neighbours decays as $1/q^3$. Although the quality of retrieval decreases for small $m$, we find good associative memory for $1 \ll m \ll N$. Hence, these networks gain a factor $N/m \gg 1$ in the computer memory and time.
We develop a ``unified'' model that describes both ``micro'' and ``macro'' evolutions within a single theoretical framework. The eco-system is described as a dynamic network; the population dynamics at each node of this network describes the ``micro''-evolution over ecological time scales (i.e., birth, ageing and natural death of individual organisms) while the appearance of new nodes, the slow changes of the links and the disappearance of existing nodes accounts for the ``macro'' evolution over geological time scales (i.e., the origination, evolution and extinction of species). In contrast to several earlier claims in the literature, we observe strong deviations from power law in the regime of long life times where the statistics is, usually, poor.