Advances in Complex Systems

Actors in the Prisoner’s Dilemma agent-based model presented here decide between cooperation and defection in binary interactions determined by distance and overall gains. The paper thus tries to answer one of the fundamental questions of international politics: how does cooperative behavior perform in an environment governed by power and location? Two kinds of noise and the reward for mutual cooperation oscillating between temptation and punishment payoffs with a variable speed were added similarly like few completely new strategies inspired by foreign policy behavior of states. The initial success of generous reciprocal altruists is no surprise, but the lacking relationship between frequency of interactions and cooperativeness at the level of pairs already suggests some similarity with the system of states. Yet, the most important outcome is victory of the balance of threat strategy in all reruns with a heterogeneous pool of actors, despite the fact that this strategy was one of the least cooperative ones. At the same time, rules pre-selected by their success in the homogeneous and cooperative environment were still able to sustain intensive cooperation among themselves even within the heterogeneous pool of strategies.

Evolution of influenza viruses is a highly complex process that is still poorly understood. Multiyear persistence of similar variants and accumulating evidences of existence of multigenic traits indicates that influenza viruses operate as integrated units and not only as sets of distinct genes. However, there is still no consensus on whether it is the case, and to what extent. One of the main problems is the lack of framework for analyzing and interpreting large body of available high dimensional genomic, clinical and epidemiological data. By reducing dimensionality of data we intend to show whether in addition to gene-centric selective pressure, the evolution of influenza RNA segments is also shaped by their mutual interactions. Therefore, we will analyze how different complexity/entropy measures (Shannon entropy, topological entropy and Lempel–Ziv complexity) can be used to study evolution of nucleotide segments of different influenza subtypes, while reducing data dimensionality. We show that, at the nucleotide level, multiyear clusters of genome-wide entropy/complexity correlations emerged during the H1N1 pandemic in 2009. Our data are the first empirical results that indirectly support the suggestion that a component of influenza evolutionary dynamics involves correlation between RNA segments. Of all used complexity/entropy measures, Shannon entropy shows the best correlation with epidemiological data.

This paper, proposes a complex Laplacian-based distributed control scheme for convergence in the multi-agent network. The proposed scheme has been designated as cascade formulation. The proposed technique exploits the traditional method of organizing large scattered networks into smaller interconnected clusters to optimize information flow within the network. The complex Laplacian-based approach results in a hierarchical structure, with the formation of a meta-cluster leading other clusters in the network. The proposed formulation enables flexibility to constrain the eigenspectra of the overall closed-loop dynamics, ensuring desired convergence rate and control input intensity. The sufficient conditions ensuring globally stable formation for the proposed formulation are also asserted. Robustness of the proposed formulation to uncertainties like loss in communication links and actuator failure have also been discussed. The effectiveness of the proposed approach is illustrated by simulating a finitely large network of 30 vehicles.