abstract

Wordnets are semantic networks containing nouns, verbs, adjectives, and adverbs organized according to linguistic principles, by means of semantic relations. In this work, we adopt a complex network perspective to perform a comparative analysis of the English and Polish wordnets. We determine their similarities and show that the networks exhibit some of the typical characteristics observed in other real-world networks. We analyse interlingual relations between both wordnets and deliberate over the problem of mapping the Polish lexicon onto the English one.

abstract

We explore the influence of community structure on the effectiveness of social sanction in promoting cooperative behavior in populations sharing common pool resources. We found that the formation of community within a population is not necessarily associated with a higher level of cooperation. In fact, our results show that defectors tend to survive better in populations with weak community structure. Nonetheless, as we further strengthened the community structure within a social network, we uncovered the occurrence of a transition towards a regime of greater cooperation. In this respect, our results provide deeper insights into the manner in which governance structures can have important influence on the management of coupled socio-ecological systems.

abstract

A model of intelligent agents system is presented in which the agents interact with environment and change their opinions as a result of mutual contacts. The agents have defined the rules of motion \(\{M\}\), the rules of opinion formation \(\{D\}\) and environmental rules \(\{E\}\). The process of renewing of resources in a certain area, defined with \(\{E\}\), determines the standard of life of agents in this area, it has also influence on their opinion. The time evolution of the system is investigated using numerical simulations and results are discussed.

abstract

The leading tool for event based discrete simulation in Python programming language is SimPy. The SimPy supports all simulation primitives and efficiently utilizes high level Python constructs. Unfortunately, SimPy’s simulation processes have to be implemented as one coroutine i.e. in a single code unit. This solution is sufficient for simple simulations but totally inappropriate for large teams and more complex problems. Our SimPy extension ETOS makes possible separation of roles in simulation teams and simplify description of simulation. The simulation is represented as a group of XML nodes and is built from simpler Python constructs — entities. The entity is implemented as a Python class using SimPy primitives and specialized support of value context (i.e. random and time-dependant values relative to simulation, a process or to entity processing). The entities provide elementary control flow constructs, versatile simulation objects and elementary operations of simulated models. The entities are also responsible for a data collecting. The paper describes hierarchy of basic entities of ETOS model and it also covers case study of framework including annotated Python and XML source codes.

abstract

In the paper, the mathematical model and numerical program for simulations of evacuations from multi-floor buildings is presented. In the model, pedestrian motion is described using Langevin equations with the Social Force term — connected with the mental component in a pedestrian motion. In the modeling of pedestrian motion in the staircases, gravity force was added to make more realistic the description of pedestrian motion in the multi-floor buildings. The level of pedestrian’s hazard defines desired velocity \(v_{\rm D}\), which is the parameter of the model. Numerical simulations of evacuation were performed for the typical three-floor office building and different values of desired velocity. The character of the pedestrian motion during evacuation and the times of evacuations were found. Also experimental drill evacuation from the similar building was observed and registered using TV cameras. A type of pedestrians motion and time of this evacuation is discussed.

abstract

Initially, a 2D field is given that contains particles with randomly distributed spins (colors, orientations). Four different spins are assumed. The vector sum of all spins can be interpreted as a magnetic field (magnetization). The task is to align the particles by moving agents into the color which is in the majority at the beginning. First, the capabilities of the agents (actions, inputs, number of control states) were defined, because they decide on how effective the task can be solved at all. The agents’ behavior is determined by an embedded finite state machine (FSM, algorithm) with 4 states only. For a given \(16 \times 16\) field with 8 agents, an FSM was evolved by a genetic procedure based on mutation. The best evolved algorithm was successful for 91 out of 100 given initial fields. It turned out that the reached color alignments were not stable. After the whole field has been colored in one color, the agents proceeded to change the field into the next color. Two experiments showed that the amplitude of the magnetization decreased and the frequency increased when the number of agents was increased. The whole system including the agents was modeled by cellular automata. For the simulation of the system, the CA-w model (cellular automata with write access) was used in order to simplify the program and speed up the simulation.

abstract

We found a unified formula for description of the household incomes of all society classes, for instance, for the European Union in years 2005–2010. The formula is more general than the well known one of Yakovenko et al. because, it satisfactorily describes not only the household incomes of low- and medium-income society classes but also the household incomes of the high-income society class. As a striking result, we found that the high-income society class almost disappeared in year 2009, in opposite to situation in remaining years, where this class played a significant role.

abstract

We investigate weekly record sales of the world’s most popular 30 artists (2003–2013). Time series of sales have nontrivial kind of memory (anticorrelations, strong seasonality and constant autocorrelation decay within 120 weeks). Amount of artists record sales are usually the highest in the first week after premiere of their brand new records and then decrease to fluctuate around zero till next album release. We model such a behavior by discrete mean-reverting quasi-geometric jump diffusion (MRqGJD) and Markov regime switching mechanism (MRS) between the base and the promotion regimes. We can build up the evidence through such a toy model that quantifies linear and nonlinear dynamical components (with stationary and nonstationary parameters set), and measure local divergence of the system with collective behavior phenomena. We find special kind of disagreement between model and data for Christmas time due to unusual shopping behavior. We consider 2 categories of customers: typical music fans and occasional music buyers who join the market before Christmas time. Analogies to earthquakes, product life-cycles, and energy markets will also be discussed.

abstract

A set of \(N\) points is chosen randomly in a \(D\)-dimensional volume \(V=a^D\), with periodic boundary conditions. For each point \(i\), its distance \(d_i\) is found to its nearest neighbour. Then, the maximal value is found, \(d_{\max }=\max (d_i, i=1,\dots ,N)\). Our numerical calculations indicate that when the density \(N/V={\rm const.}\), \(d_{\max }\) scales with the linear system size as \(d^2_{\max }\propto a^\phi \), with \(\phi =0.24\pm 0.04\) for \(D=1,2,3,4\).

abstract

Ferromagnetic and glassy phase transitions in \(p\)-spin models on scale-free hypernetworks are investigated, with Ising spins located in the nodes and \(p\)-spin exchange interactions corresponding to hyperedges. Monte Carlo simulations show that ferromagnetic transition at non-zero temperature is possible in such models which exhibits certain characteristics of the first-order phase transition. However, the ground state is, in general, degenerate and at low temperatures, depending on the network topology, the model apart from the ferromagnetic state can stay in one of few or even infinitely many disordered states. These states are degenerate with the ferromagnetic one and have structure resembling that of a spin glass. The presence of the first-order ferromagnetic transition and the degeneracy of the ground state is confirmed by analytic calculations in the mean-field approximation. The critical temperatures for the ferromagnetic transition obtained in the mean-field approach and from numerical simulations are in reasonable agreement.

abstract

Self-organization in biological systems often emerges as widespread oscillatory dynamics of coupled units. Two types of oscillation phenomena can be investigated in networks of automata. One may observe sustained oscillations in a system of non-oscillating automata (as in, e.g. , the Greenberg–Hastings cellular automata), or one may investigate synchronization, i.e. self-organization of individual cellular oscillations to the common oscillation. Both approaches are used in modeling cardiac electrophysiology. This paper begins with a review of the capabilities and limitations of these propositions in reproducing the functionality of the human pacemaker. Then, an approach to modeling the pacemaker tissue is presented that is based on timed automata having heterogeneous topology of couplings. Timed automata combine intrinsic cellular transitions with nearest neighbor interactions. The complex topology of intercellular interactions is modeled by a stochastic network with the heterogeneous structure arising from the preferential rewiring. The resulting simulation framework exhibits significantly improved computational efficiency in modeling different aspects of the self-organization to the common wave patterns, and furthermore, in reproducing changes in the pacemaker tissue caused by biological aging.

abstract

An order–disorder phase transition is observed for Ising-like systems even for arbitrarily chosen probabilities of spins flips. For such athermal dynamics one must define (\(z+1\)) spin flips probabilities \(w(n)\), where \(z\) is a number of the nearest-neighbours for given regular lattice and \(n=0,\dots ,z\) indicates the number of nearest spins with the same value as the considered spin. Recently, such dynamics has been successfully applied for the simulation of a cooperative and competitive strategy selection by pedestrians in crowd. For the triangular lattice (\(z=6\)) and flips probabilities dependence on a single control parameter \(x\) chosen as \(w(0)=1\), \(w(1)=3x\), \(w(2)=2x\), \(w(3)=x\), \(w(4)=x/2\), \(w(5)=x/4\), \(w(6)=x/6\) the ordered phase (where most of pedestrians adopt the same strategy) vanishes for \(x\gt x_{\rm C}\approx 0.429\). In order to introduce long-range interactions between pedestrians, the bonds of triangular lattice are randomly rewired with the probability \(p\). The amount of rewired bonds can be interpreted as the probability of communicating by mobile phones. The critical value of control parameter \(x_{\rm C}\) increases monotonically with the number of rewired links \(M=pzN/2\) from \(x_{\rm C}(p=0)\approx 0.429\) to \(x_{\rm C}(p=1)\approx 0.81\).

abstract

The investigation of community structures in networks is a task of great importance in many disciplines, namely physics, sociology, biology and computer science, where systems are often represented as graphs. One of the challenges is to find local communities in a graph from a local viewpoint, in the absence of the access to global information, and to reproduce the subjective hierarchical vision for each vertex. In this paper, we present the improvement of an information dynamics algorithm in which the label propagation of nodes is based on the Markovian flow of information in the network under cognitive-inspired constraints. We introduced two more complex heuristics that allow to detect the hierarchical community structure of the networks from a source vertex or a community, adopting fixed values of model’s parameters. Experimental results show that the proposed methods are efficient and well-behaved in both the real-world and synthetic networks.

abstract

In this study, we propose a modification of a method for the extraction of the dynamics of a system from a time series generated by it. We focused on the problem of rare events for which a statistical characterization is difficult because of their small number. We developed a method for the separation of the deterministic and stochastic components of the time series through the computation of probability densities. Our previous method used a constant width of bins in the histograms for determination of the probability densities. Here, we replace them by bins with a constant number of counts. We have tested the method and presented an application to heart rate variability showing advantages of the modified procedure.