Advances in Complex Systems

The 1995 model of Bonabeau et al. is generalized by giving each individual a different ability to win or lose a fight. We also introduce different groups such that the losers of fights between different groups are eliminated. The overall phase diagram for the first-order transition between egalitarian and hierarchical societies does not change much after these generalizations.

The cellular learning automata, which is a combination of cellular automata, and learning automata, is a new recently introduced model. This model is superior to cellular automata because of its ability to learn and is also superior to a single learning automaton because it is a collection of learning automata which can interact with each other. The basic idea of cellular learning automata, which is a subclass of stochastic cellular learning automata, is to use the learning automata to adjust the state transition probability of stochastic cellular automata. In this paper, we first provide a mathematical framework for cellular learning automata and then study its convergence behavior. It is shown that for a class of rules, called commutative rules, the cellular learning automata converges to a stable and compatible configuration. The numerical results also confirm the theoretical investigations.

In this paper, we propose a model of studying attitude evolution on campus. With the Cellular Automata (CA) model, we divide the students into five groups with different studying attitudes, and discuss the influence of different proportions of students with different studies on the whole studying attitude. With the definition of the concrete correlations of the individuals, we simulate the evolution of studying attitude on campus.

We analyze how difficult it is to synchronize to a periodic sequence whose structure is known, when an observer is initially unaware of the sequence's phase. We examine the transient information T, a recently introduced information-theoretic quantity that measures the uncertainty an observer experiences while synchronizing to a sequence. We also consider the synchronization time τ, which is the average number of measurements required to infer the phase of a periodic signal. We calculate T and τ for all periodic sequences up to and including period 23. We show which sequences of a given period have the maximum and minimum possible T and τ values, develop analytic expressions for the extreme values, and show that in these cases the transient information is the product of the total phase information and the synchronization time. Despite the latter result, our analyses demonstrate that the transient information and synchronization time capture different and complementary structural properties of individual periodic sequences — properties, moreover, that are distinct from source entropy rate and mutual information measures, such as the excess entropy.

Experimental evidence suggests that human decisions involve a mixture of self-interest and internalized social norms which cannot be accounted for by the Nash equilibrium behavior of Homo Economicus. This led to the notion of strong reciprocity (or altruistic punishment) to capture the human trait leading an individual to punish norm violators at a cost to himself. For a population with small autonomous groups with collective monitoring, the interplay of intra- and intergroup dynamics shows this to be an adaptive trait, although not fully invasive of a selfish population. However, the absence of collective monitoring in a larger society changes the evolution dynamics. Clustering seems to be the network parameter that controls maintenance and evolution of the reciprocator trait.

In this paper we have used a simple and efficient color-based approach to segment human skin pixels from background, using a 2D histogram-based approach as a preprocess stage for human face detection. For skin segmentation, a total of 446,007 skin samples from the training set is manually cropped from the RGB color images, to calculate three lookup tables based on the relationship between each pair of the triple components (R, G, B). Derivation of skin classifier rules from the lookup tables are based on how often each attribute value (interval) occurs, and their associated certainty values. For face detection, we assume the face-appearance as blob-like, and that the face has an approximately elliptical shape. Accordingly, an ellipse-fitting algorithm is appropriate, which is based on statistical moments, and those blobs that have an elliptical shape are retained as face candidates.

This paper considers a non-autonomous competitive two-species model with stage structure in one species. The conditions of permanence obtained. Furthermore, the existence and asymptotic stability of the periodic solution are proved under some assumptions if this model turns out to be a periodic system.

In this paper we study the evolution of sequential dynamical systems as a result of the erroneous replication of the SDS words. An consists of (a) a finite, labeled graph Y in which each vertex has a state, (b) a vertex labeled sequence of functions (F_vi,Y), and (c) a word w, i.e. a sequence (w₁,…,w_k), where each w_i is a Y-vertex. The function F_wi,Y updates the state of vertex w_i as a function of the states of w_i and its Y-neighbors and leaves the states of all other vertices fixed. The over the word w and Y is the composed map: . The word w represents the genotype of the in a natural way. We will randomly flip consecutive letters of w with independent probability q and study the resulting evolution of the . We introduce combinatorial properties of which allow us to construct a new distance measure for words. We show that captures the similarity of corresponding . We will use the distance measure to study neutrality and mutation rates in the evolution of words. We analyze the structure of neutral networks of words and the transition of word populations between them. Furthermore, we prove the existence of a critical mutation rate beyond which a population of words becomes essentially randomly distributed, and the existence of an optimal mutation rate at which a population maximizes its mutant offspring.

We ask whether natural selection has shaped three biologically important features of 15 signal transduction networks and two genome-scale transcriptional regulation networks. These features are regulatory cycles, the lengths of the longest pathways through a network — a measure of network compactness — and the abundance of node pairs connected by many alternative regulatory pathways. We determine whether these features are significantly more or less abundant in biological networks than in randomized networks with the same distribution of incoming and outgoing connections per network node. We find that autoregulatory cycles are of exceptionally high abundance in transcriptional regulation networks. All other cycles, however, are significantly less abundant in several signal transduction networks. This suggests that the multistability caused by complex feedback loops in a network may interfere with the functioning of such networks. We also find that several of the networks we examine are more compact than expected by chance alone. This raises the possibility that the transmission of information through such networks, which is fastest in compact networks, is a biologically important characteristic of such networks.

Ideas on educational psychology suggest that a learning process occurs when people participate within social communities. A model is constructed based on two primary factors in the learning process : knowledge storage and interactive ability of each person. Results of simulations are consistent with some actual phenomena including the average knowledge achieved and different educational effects under different conditions. Functions of two main factors and teachers' influences are also studied and discussed.