From effects-based operations to effects-based force : on causality, complex adaptive system, and the biology of war

The author addresses a recent force employment concept called effects-based operations, which first appeared during the 1991 war against Iraq. The attributes of effects-based operations can be grouped around three common, but interrelated elements such as effects focus, advanced technology, and systems thinking. However, the characteristics upon which the common elements are built, such as causality/deduction for effects focus, intangibles/control for advanced technology, and categorisation/analysis for systems thinking bear dangerous simplifications regarding the nature of war. These characterictics are in sharp contrast with war__s frictional nature as outlined by Clausewitz, who stated that effects in war cannot be traced back to single causes, as several concurrent causes are normally at work. Novelty must always be expected in war as friction dims expectations in terms of causality and the ability to achieve desired effects. The author suggests an organic approach to address the challenge posed by war. According to him the emphasis must shift towards learning and adaptation, instead of planning for desired effects. Friction indicates that often it is more important in war how we do things than what things we do, which has a clear practical limitation for the concept of effects-based operations.LEI Universiteit LeidenPolitieke Instituties: Ontwerp, functioneren, effecte

Self Organized Multi Agent Swarms (SOMAS) for Network Security Control

Computer network security is a very serious concern in many commercial, industrial, and military environments. This paper proposes a new computer network security approach defined by self-organized agent swarms (SOMAS) which provides a novel computer network security management framework based upon desired overall system behaviors. The SOMAS structure evolves based upon the partially observable Markov decision process (POMDP) formal model and the more complex Interactive-POMDP and Decentralized-POMDP models, which are augmented with a new F(*-POMDP) model. Example swarm specific and network based behaviors are formalized and simulated. This paper illustrates through various statistical testing techniques, the significance of this proposed SOMAS architecture, and the effectiveness of self-organization and entangled hierarchies

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Adaptive Estimation of Distribution Algorithms for Low-Thrust Trajectory Optimization

A direct adaptive scheme is presented as an alternative approach for minimum-fuel low-thrust trajectory design in non-coplanar orbit transfers, utilizing fitness landscape analysis (FLA). Spacecraft dynamics is modeled with respect to modified equinoctial elements, considering $J_2$ orbital perturbations. Taking into account the timings of thrust arcs, the discretization nodes for thrust profile, and the solution of multi-impulse orbit transfer, a constrained continuous optimization problem is formed for low-thrust orbital maneuver. An adaptive method within the framework of Estimation of Distribution Algorithms (EDAs) is proposed, which aims at conserving feasibility of the solutions within the search process. Several problem identifiers for low-thrust trajectory optimization are introduced, and the complexity of the solution domain is analyzed by evaluating the landscape feature of the search space via FLA. Two adaptive operators are proposed, which control the search process based on the need for exploration and exploitation of the search domain to achieve optimal transfers. The adaptive operators are implemented in the presented EDA and several perturbed and non-perturbed orbit transfer problems are solved. Results confirm the effectiveness and reliability of the proposed approach in finding optimal low-thrust transfer trajectories.BEAZ Bizkaia, 3/12/DP/2021/00150; SPRI Group, Ekintzaile Program EK-00112-202

Nonlinear Dynamics and Interpersonal Correlates of Verbal Turn-Taking Patterns in a Group Therapy Session

Interpersonal processes and dynamics are ubiquitous topics in psychotherapy, yet they are difficult to study and are theoretically fragmented across therapeutic subdisciplines. The current study tests an integrative model of interpersonal dynamics in small groups using nonlinear dynamical systems theory. The conversation of one group therapy session (with six adolescent sex offenders) is analyzed using orbital decomposition, which allows for the identification of patterns in categorical time series data. The results show evidence of selforganizing social patterns, based on formal measures of turbulence (Lyapunov dimension), information novelty (Shannon\u27s entropy), and complexity (fractal dimension). The degree of patterning in turn taking is significantly correlated with measurements of control, closeness, and conflict among group members. Clinical implications and directions for future research are discussed

Leadership and crisis: incubation, emergence, and transitions

No abstract available

Individual-Based Modeling and Nonlinear Analysis for Complex Systems with Application to Theoretical Ecology

One approach to understanding the behaviour of complex systems is individual-based modeling, which provides a bottom-up approach allowing for the consideration of the traits and behaviour of individual organisms. Ecosystem models aim to characterize the major dynamics of ecosystems, in order to synthesize the understanding of such systems and to allow predictions of their behaviour. Moreover, ecosystem simulations have the potential to help scientists address theoretical questions as well as helping with ecological resource management. Because in reality biologists do not have much data regarding variations in ecosystems over long periods of time, using the results of ecological computer simulation for making reasonable predictions can help biologists to better understand the long-term behaviour of ecosystems. Different versions of ecosystem simulations have been developed to investigate several questions in ecology such as how speciation proceeds in the absence of experimenter-defined functions. I have investigated some of these questions relying on complex interactions between the many individuals involved in the system, as well as long-term evolutionary patterns and processes such as speciation and macroevolution. Most scientists now believe that natural phenomena have to be looking as a chaotic system. In the past few years, chaos analysis techniques have gained increasing attention over a variety of applications. I have analyzed results of complex models to see whether chaotic behaviour can emerge, since any attempt to model a realistic system needs to have the capacity to generate patterns as complex as the ones that are observed in real systems. To further understand the complex behaviour of real systems, a new algorithm for long-term prediction of time series behaviour is also proposed based on chaos analysis. We evaluated the performance of our new method with respect to the prediction of the Dow-Jones industrial index time series, epileptic seizure and global temperature anomaly

Organised Chaos: Bringing Complexity to Criminology and the Study of Organised Crime, Terrorism and the Crime-Terror Nexus

Given the complexities of our time, it is not surprising that criminological models, theories and perspectives often fall short of providing complete and satisfactory explanations of complex phenomena. Organised crime and terrorism, and the emerging crime-terror nexus, are examples of such phenomena. If they were simple, they would be easy to understand and prevent, but they are not. Complexity science (‘complexity’) studies complex phenomena. Given the nature of organised crime, terrorism and the crime-terror nexus, one would expect that the family of complexity, including chaos theory, might lend itself to furthering our understanding and knowledge of these phenomena. Drawing on the natural and social sciences, this thesis explores the notion. In doing so, a new complexity model, using borrowed science, is developed to apply complexity in a criminological context, and to critically examine organised crime, terrorism and the crime-terror nexus through a complexity lens. The new complexity model is tested using the case study method and considers whether the new model furthers our understanding and knowledge of these complex phenomena, together with practical and policy implications. The thesis also considers whether the new complexity model adds a new tool to the criminologist's toolbox to provide fresh and novel insights into complex problems

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin