Advances in Complex Systems

This article explains a simulation of the Co-Author Model (CAM) applied in strategic alliance setting where firms use and optimize their network resources. To understand its process casualty better, I explain the model's mechanisms in terms of its interactive dynamics and resulting equilibrium structure. Classified as a Boolean network with two inputs, CAM's dynamics cause a biased equilibrium structure that could then be explained by the Simmelian tie and the completion of structure through structural balance. These explanations not only could answer the criticism that a simulation model is merely a toy model without much realism but could also explain and give insights to both substantive theory and methodology development in strategic alliance research. The results include the effect of a third party, the embeddedness of firms in their alliances, and the real-world implications of the "frozen web": a phenomenon drawn parallel to the financial crisis beginning in 2007.

In this paper, we show that the small world and weak ties phenomena can spontaneously emerge in a social network of interacting agents. This dynamics is simulated in the framework of a simplified model of opinion diffusion in an evolving social network where agents are made to interact, possibly update their beliefs and modify the social relationships according to the opinion exchange.

How can networking affect the turnout in an election? We present a simple model to explain turnout as a result of a dynamic process of formation of the intention to vote within Erdös–Rényi networks. Citizens have fixed preferences for one of two parties and are embedded in a social network. They decide whether or not to vote on the basis of the attitude of their immediate contacts. They may simply follow the behavior of the majority (followers) or make an adaptive local calculus of voting (calculators). So they have the intention of voting either when the majority of their neighbors are willing to vote too, or when they perceive in their social neighborhood that elections are "close". We study the long-run average intention to vote, interpreted as the actual turnout observed in an election. Depending on the values of the average connectivity and the probability of behaving as a follower/calculator, the system exhibits monostability (zero turnout), bistability (zero and moderate/high turnout) or tristability (zero, moderate and high turnout). By obtaining realistic turnout rates for a wide range of values of both parameters, our model suggests a mechanism behind the observed relevance of social networks in recent elections.

We examine the role of self-maintenance (collective autocatalysis) in the evolution of computational biochemical networks. In primitive proto-cells (lacking separate genetic machinery) self-maintenance is a necessary condition for the direct reproduction and inheritance of what we here term Cellular Information Processing Networks (CIPNs). Indeed, partially reproduced or defective CIPNs may generally lead to malfunctioning or premature death of affected cells. We explore the interaction of this self-maintenance property with the evolution and adaptation of CIPNs capable of distinct information processing abilities. We present an evolutionary simulation platform capable of evolving artificial CIPNs from a bottom-up perspective. This system is an agent-based multi-level selectional Artificial Chemistry (AC) which employs a term rewriting system called the Molecular Classifier System (MCS.bl). The latter is derived from the Holland broadcast language formalism. Using this system, we successfully evolve an artificial CIPN to improve performance on a simple pre-specified information processing task whilst subject to the constraint of continuous self-maintenance. We also describe the evolution of self-maintaining, cross-talking and multi-tasking, CIPNs exhibiting a higher level of topological and functional complexity. This proof of concept aims at contributing to the understanding of the open-ended evolutionary growth of complexity in artificial systems.

Chemical organization theory has been suggested as a new approach to analyze complex reaction networks. Concerning the long-term behavior of the network dynamics we will study its foundations mathematically. Therefore we consider a chemical reactor containing molecules of different species reacting with each other according to a set of reaction rules. We further assume that the dynamical behavior of the concentration of each species is given by a continuous chemical ordinary differential equation. Abstracting from dedicated concentration values we consider a discrete problem: Which species can appear in the reactor after a long time? We define the limit set abstraction, which contains the subsets of species characterizing the long-term behavior. We prove that all these subsets are closed and that for all bounded limit sets, at least one of them is self-maintaining and thus is an organization. This implies for a chemical ordinary differential equation systems that any attractor that does not touch the state space boundaries (in particular, any periodic attractor) lies within one organization, that is, the set of species with positive concentrations in any state of such an attractor is an organization. This in turn explains why in a deterministic system evolving according to reaction rules one can observe species sets that are closed and self-maintaining, thus organizations.

On the basis of the relative daily logarithmic returns of 88 different funds in the Chinese fund market (CFM) from June 2005 to October 2009, we construct the cross-correlation matrix of the CFM. It is shown that the logarithmic returns follow an exponential distribution, which is commonly shared by some emerging markets. We hereby analyze the statistical properties of the cross-correlation coefficients in different time periods, such as the distribution, the mean value, the standard deviation, the skewness and the kurtosis. By using the method of the scaled factorial moment, we observe the intermittence phenomenon in the distribution of the cross-correlation coefficients. Also by employing the random matrix theory (RMT), we find a few isolated large eigenvalues of the cross-correlation matrix, and the distribution of eigenvalues exhibits the power-law tails. Furthermore, we study the features of the correlation strength with a simple definition.