Dehmer and Mowshowitz introduced a class of generalized graph entropies using known information-theoretic measures. These measures rely on assigning a probability distribution to a graph. In this article, we prove some extremal properties of such generalized graph entropies by using the graph energy and the spectral moments. Moreover, we study the relationships between the generalized graph entropies and compute the values of the generalized graph entropies for special graph classes. © 2014 Wiley Periodicals, Inc. Complexity, 2014

]]>This article calls attention to flaws in the scientific enterprise, providing a case study of the lack of professionalism in published journal articles in a particular area of research, namely, network complexity. By offering details of a special case of poor scholarship, which is very likely indicative of a broader problem, the authors hope to stimulate editors and referees to greater vigilance, and to strengthen authors' resolve to take their professional responsibilities more seriously. © 2014 Wiley Periodicals, Inc. Complexity, 2014

]]>Infrastructure, which is used to extract, transport, store, and transform resources into products or services to meet our utility needs faces numerous challenges caused by the agency of the various actors in the system. To understand these challenges, we propose it is necessary to move beyond considering each utility system as a distinct silo. In this paper, a conversion points approach is developed to characterize multiutility systems at any scale and for any specific or theoretical location. The story is told of the development of a conversion points approach and its application is examined using an agent-based model. Transport, energy, water, waste, and telecommunications systems are governed and run independently but in practice are highly interdependent. A way to represent all utility systems in an integrated way is described and the benefits of this representation are applied to UK household consumers. © 2014 Wiley Periodicals, Inc. Complexity, 2014

]]>This article proposes a new integrated diagnostic system for islanding detection by means of a neuro-fuzzy approach. Islanding detection and prevention is a mandatory requirement for grid-connected distributed generation (DG) systems. Several methods based on passive and active detection scheme have been proposed. Although passive schemes have a large non-detection zone (NDZ), concern has been raised on active method due to its degrading power-quality effect. Reliably detecting this condition is regarded by many as an ongoing challenge as existing methods are not entirely satisfactory. The main emphasis of the proposed scheme is to reduce the NDZ to as close as possible and to keep the output power quality unchanged. In addition, this technique can also overcome the problem of setting the detection thresholds inherent in the existing techniques. In this study, we propose to use a hybrid intelligent system called ANFIS (the adaptive neuro-fuzzy inference system) for islanding detection. This approach utilizes rate of change of frequency (ROCOF) at the target DG location and used as the input sets for a neuro-fuzzy inference system for intelligent islanding detection. This approach utilizes the ANFIS as a machine learning technology and fuzzy clustering for processing and analyzing the large data sets provided from network simulations using MATLAB software. To validate the feasibility of this approach, the method has been validated through several conditions and different loading, switching operation, and network conditions. The proposed algorithm is compared with the widely used ROCOF relays and found working effectively in the situations where ROCOF fails. Simulation studies showed that the ANFIS-based algorithm detects islanding situation accurate than other islanding detection algorithms. © 2014 Wiley Periodicals, Inc. Complexity, 2014

]]>Different from the short-term risk measure for traditional financial assets (stocks, bonds, etc.), the key to illiquid inventory portfolio traded in the over-the-counter markets is to estimate the long-term extreme price risk with time varying volatility. In this article, a new long-term extreme price risk (value at risk and conditional value at risk) measure method for inventory portfolio and an application to dynamic impawn rate interval are proposed. To realize this, we first establish AutoRegressive Moving Average-Exponential Generalized Autoregressive Conditional Heteroskedasticity-Extreme Value Theory model and multivariatet-Copula to depict the autocorrelation, fat tails, and volatility clustering of returns of inventories and the nonlinear dependence structure of inventories. Furthermore, we obtain the long-term extreme price risk with time varying volatility via Monte Carlo simulation instead of square-root-of time rule. The results show that, first, benefits from risk diversification is significant; second, long-term extreme price risk measure of inventory portfolio via Monte Carlo method outperforms the square-root-of time rule; the last is that the dynamic rate interval based on the long-term price risk is superior to the crude rules of thumb in terms of reducing efficiency loss and improving risk coverage. In summary, this article provides a new quantitative framework for managing the risk of portfolio in inventory financing practice for banks constrained by risk limitation. © 2014 Wiley Periodicals, Inc. Complexity, 2014

]]>This article discusses the meaning and scope of biological hypercomputation (BH) that is to be considered as new research problem within the sciences of complexity. The framework here is computational, setting out that life is not a standard Turing Machine. Living systems, we claim, hypercompute, and we aim at understanding life not by what it is, but rather by what it does. The distinction is made between classical and nonclassical hypercomputation. We argue that living processes are nonclassical hypercomputation. BH implies then new computational models. Finally, we sketch out the possibilities, stances, and reach of BH. © 2014 Wiley Periodicals, Inc. Complexity, 2014

]]>Often relegated to the methods section of genetic research articles, the term “degeneracy” is regularly misunderstood and its theoretical significance widely understated. Degeneracy describes the ability of different structures to be conditionally interchangeable in their contribution to system functions. Frequently mislabeled redundancy, degeneracy refers to structural variation whereas redundancy refers to structural duplication. Sources of degeneracy include, but are not limited to, (1) duplicate structures that differentiate yet remain isofunctional, (2) unrelated isofunctional structures that are dispersed endogenously or exogenously, (3) variable arrangements of interacting structures that achieve the same output through multiple pathways, and (4) parcellation of a structure into subunits that can still variably perform the same initial function. The ability to perform the same function by drawing upon an array of dissimilar structures contributes advantageously to the integrity of a system. Drawing attention to the heterogeneous construction of living systems by highlighting the concept of degeneracy valuably enhances the ways scientists think about self-organization, robustness, and complexity. Labels in science, however, can sometimes be misleading. In scientific nomenclature, the word “degeneracy” has calamitous proximity to the word “degeneration” used by pathologists and the shunned theory of degeneration once promoted by eugenicists. This article disentangles the concept of degeneracy from its close etymological siblings and offers a brief overview of the historical and contemporary understandings of degeneracy in science. Distinguishing the importance of degeneracy will hopefully allow systems theorists to more strategically operationally conceptualize the distributed intersecting networks that comprise complex living systems. © 2014 Wiley Periodicals, Inc. Complexity, 2014

]]>This article deals with the fractional-order modeling of a complex four-dimensional energy supply-demand system (FOESDS). First, the fractional calculus techniques are adopted to describe the dynamics of the energy supply-demand system. Then the complex behavior of the proposed fractional-order FOESDS is studied using numerical simulations. It is shown that the FOESDS can exhibit stable, chaotic, and unstable states. When it exhibits chaos, the FOESDS's strange attractors are plotted to validate the chaotic behavior of the system. Moreover, we calculate the maximal Lyapunov exponents of the system to confirm the existence of chaos. Accordingly, to stabilize the system, a finite-time active fractional-order controller is proposed. The effects of model uncertainties and external disturbances are also taken into account. An estimation of the stabilization time is given. Based on the latest version of the fractional Lyapunov stability theory, the finite-time stability and robustness of the proposed method are proved. Finally, two illustrative examples are provided to illustrate the usefulness and applicability of the proposed control scheme. © 2014 Wiley Periodicals, Inc. Complexity, 2014

]]>This article introduces a new case-based density approach to modeling big data longitudinally, which uses ordinary differential equations and the linear advection partial differential equations (PDE) to treat macroscopic, dynamical change as a transport issue of aggregate cases across continuous time. The novelty of this approach comes from its unique data-driven treatment of cases: which are K dimensional vectors; where the velocity vector for each case is computed according to its particular measurements on some set of empirically defined social, psychological, or biological variables. The three main strengths of this approach are its ability to: (1) translate the data driven, nonlinear trajectories of microscopic constituents (cases) into the linear movement of macroscopic trajectories, which take the form of densities; (2) detect the presence of multiple, complex steady state behaviors, including sinks, spiraling sources, saddles, periodic orbits, and attractor points; and (3) predict the motion of novel cases and time instances. To demonstrate the utility of this approach, we used it to model a recognized cohort dynamic: the longitudinal relationship between a country's per capita gross domestic product (GDP) and its longevity rates. Data for the model came from the widely used Gapminder dataset. Empirical results, including the strength of the model's fit and the novelty of its results (particularly on a topic of such extensive study) support the utility of our new approach. © 2014 Wiley Periodicals, Inc. Complexity, 2014

]]>Humanitarian crises and related complex emergencies caused by natural hazards or conflicts are marked by uncertainty. Disasters are extreme events mitigated through preparedness, response, and recovery. This article uses social complexity theory as a novel framework for deriving actionable insights on the onset T and severity S of disasters. Disaster distributions often show heavy tails, symptomatic of nonequilibrium dynamics, sometimes approximating a power law with critical or near-critical exponent value of 2, not “normal” (bell-shaped) or Gaussian equilibrium features. This theory-based method is applicable to existing datasets. Policy implications include the usefulness of real-time and anticipatory analytical strategies to support preparedness. © 2014 Wiley Periodicals, Inc. Complexity , 2014

]]>This study suggests that cross-fertilization between complexity and social science could provide a new rationale for policy. We look at the weakness of conventional policy thinking and excessive faith in incentives and the underestimation of social interaction on individual choices. Recent examples of experimental and computational research on social interaction indicate the importance of understanding preexisting social norms and network structures for targeting appropriately contextualized policies. This would allow us to conceive policy not as something that takes place “off-line” outside systems but as a constitutive process interacting with self-organized system behavior. This article aims to pave the way for a complexity-friendly policy that allows us to understand and manage more than predict and control top-down. © 2014 Wiley Periodicals, Inc. Complexity, 2014

]]>This article investigates exponential stability of uncertain discrete-time nonlinear switched systems with parameter uncertainties and randomly occurring delays via Takagi–Sugeno fuzzy approach. The randomness of time-varying delay is characterized by introducing a Bernoulli stochastic variable that follows certain probability distribution. By adopting the average dwell-time approach with Lyapunov–Krasovskii functional and using convex reciprocal lemma, delay-dependent sufficient conditions for exponential stability of the switched fuzzy system are derived in terms of linear matrix inequalities (LMIs), which can be solved readily using any LMI solvers. Finally, illustrative examples are provided to demonstrate the effectiveness of the proposed approach. © 2014 Wiley Periodicals, Inc. Complexity, 2014

]]>In this article, the synchronization problem of uncertain complex networks with multiple coupled time-varying delays is studied. The synchronization criterion is deduced for complex dynamical networks with multiple different time-varying coupling delays and uncertainties, based on Lyapunov stability theory and robust adaptive principle. By designing suitable robust adaptive synchronization controllers that have strong robustness against the uncertainties in coupling matrices, the all nodes states of complex networks globally asymptotically synchronize to a desired synchronization state. The numerical simulations are given to show the feasibility and effectiveness of theoretical results. © 2014 Wiley Periodicals, Inc. Complexity, 2014

]]>To model agent relationships in agent-based models, it is often necessary to incorporate a social network whose topology is commonly assumed to be “small-world.” This is potentially problematic, as the classification is broad and covers a wide-range of network statistics. Furthermore, real networks are often dynamic, in that edges and nodes can appear or disappear, and spatial, in that connections are influenced by an agent's position within a particular social space. These properties are difficult to achieve in current network formation tools. We have, therefore, developed a novel social network formation model, that creates and dynamically adjusts small-world networks using local spatial interactions, while maintaining tunable global network statistics from across the broad space of possible small-world networks. It is, therefore, a useful tool for multiagent simulations and diffusion processes, particularly those in which agents and edges die or are constrained in their movement within some social space. We also show, using a simple epidemiological diffusion model, that a range of networks can all satisfy the small-world criterion, but behave quite differently. This demonstrates that it is problematic to generalize results across the whole space of small-world networks. © 2014 Wiley Periodicals, Inc. Complexity, 2014

]]>We compared entropy for texts written in natural languages (English, Spanish) and artificial languages (computer software) based on a simple expression for the entropy as a function of message length and specific word diversity. Code text written in artificial languages showed higher entropy than text of similar length expressed in natural languages. Spanish texts exhibit more symbolic diversity than English ones. Results showed that algorithms based on complexity measures differentiate artificial from natural languages, and that text analysis based on complexity measures allows the unveiling of important aspects of their nature. We propose specific expressions to examine entropy related aspects of tests and estimate the values of entropy, emergence, self-organization, and complexity based on specific diversity and message length. © 2014 Wiley Periodicals, Inc. Complexity, 2014

]]>This article introduces new methods for ranking alternatives in multicriteria decision making situations. Each is based on the normative position that the strength of an alternative is inversely related to the number of alternatives that could prevent it from being chosen. The scores discriminate among elements of the Banks set (Banks, Soc Choice Welfare, 1985, 1, 295–306). The new scoring methods are compared to traditional scoring methods and related to the amount of intransitivity (specifically, the size of the top-cycle) of aggregated preference. The new scores are shown to measure important aspects of alternatives not captured by extant scoring methods and are illustrated in collective choice settings. © 2014 Wiley Periodicals, Inc. Complexity, 2014

]]>Innovation and entrepreneurship are the most important catalysts of dynamism in market economies. While it is known that entrepreneurial activities are locally embedded, mutual effects of entrepreneurs and their local regional environment have not been adequately addressed in the existing literature. In this article, we use agent-based simulation experiments to investigate the role of entrepreneurship in the emergence of regional industrial clusters. We present fundamental extensions to the Simulating Knowledge Dynamics in Innovation Networks model (Ahrweiler et al., Industry and Labor Dynamics: The Agent-based Computational Economics Approach; World Scientific: Singapore, 2004; pp 284–96) by using a multilevel modeling approach. We analyze the effects of changing entrepreneurial character of regions on the development industrial clusters in two simultaneously simulated regions. We find that an increase in the entrepreneurship of one region has a negative effect on the other region due to competition for factors of production and innovative outputs. The major policy implication of this finding is the limitation it posits on regional innovation and development policies that aspire to support clusters in similar areas of industrial specialization. © 2014 Wiley Periodicals, Inc. Complexity, 2014

]]>Complex social-ecological systems (SES) are not amenable to simple mathematical modeling. However, to address critical issues in SES (e.g., understanding ecological resilience/amelioration of poverty) it is necessary to describe such systems in their entirety. Based on empirical knowledge of local stakeholders and experts, we mapped their conceptions of one SES. Modelers codified what actors told us into two models: a local-level model and an overarching multiple-entity description of the system. Looking at these two representations together helps us understand links between the locally specific and other levels of decision taking and vice-versa. This “bimodeling” approach is investigated in one SES in coastal Kenya. © 2014 Wiley Periodicals, Inc. Complexity, 2014

]]>Complex systems in causal relationships are known to be circular rather than linear; this means that a particular result is not produced by a single cause, but rather that both positive and negative feedback processes are involved. However, although interpreting systemic interrelationships requires a language formed by circles, this has only been developed at the diagram level, and not from an axiomatic point of view. The first difficulty encountered when analysing any complex system is that usually the only data available relate to the various variables, so the first objective was to transform these data into cause-and-effect relationships. Once this initial step was taken, our discrete chaos theory could be applied by finding the causal circles that will form part of the system attractor and allow their behavior to be interpreted. As an application of the technique presented, we analyzed the system associated with the transcription factors of inflammatory diseases. © 2013 Wiley Periodicals, Inc. Complexity 19: 15–19, 2014

]]>This article considers the leader-following consensus problem of heterogeneous multi-agent systems. The proposed multi-agent system is consisted of heterogeneous agents where each agents have their own nonlinear dynamic behavior. To overcome difficulty from heterogeneous nonlinear intrinsic dynamics of agents, a fuzzy disturbance observer is adopted. In addition, based on the Lyapunov stability theory, an adaptive control method is used to compensate the observation error caused by the difference between the unknown factor and estimated values. Two numerical examples are given to illustrate the effectiveness of the proposed method. © 2013 Wiley Periodicals, Inc. Complexity 19: 20–31, 2014

]]>This article deals with the state estimation problem of memristor-based recurrent neural networks (MRNNs) with time-varying delay based on passivity theory. The main purpose is to estimate the neuron states, through available output measurements such that for all admissible time delay, the dynamics of the estimation error is passive from the control input to the output error. Based on the Lyapunov–Krasovskii functional (LKF) involving proper triple integral terms, convex combination technique, and reciprocal convex technique, a delay-dependent state estimation of MRNNs with time-varying delay is established in terms of linear matrix inequalities (LMIs). The information about the neuron activation functions and lower bound of the time-varying delays is fully used in the LKF. Then, the desired estimator gain matrix is accomplished by solving LMIs. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed theoretical results. © 2013 Wiley Periodicals, Inc. Complexity 19: 32–43, 2014

]]>The mechanism that activates a bi-junction power generator under the effects of heat is the Seebeck effect, that is, the production of voltage difference ΔV(t) is directly proportional to the temperature difference ΔT(t) between the “hot” and “cold” junctions of the device. This phenomenon is well established and is known as thermoelectric power generation. Here, it is shown that, instead, the causal and linear relationship between ΔV(t) and ΔT(t) is lost when continuous broadband infrared (CB-IR) radiation illuminates a bi-junction power generator in an insulated compartment. The observed phenomenon is IR power generation. Heat transfer calculations fail in explaining the experimental trends. The interaction between CB-IR radiation and the charge carriers in the bi-junction power generator might play a role in the ΔV(t) production, depending upon the geometry of the experimental setup. The longitudinal propagation of collective oscillations, for example, polaritons, in the plates protecting the “hot” and “cold” junctions of the bi-junction power generator could explain the ΔV(t) production and the characteristic time constants. The findings should be considered in the design, fabrication, and improvement of thermopiles, power meters, and IR energy-harvesting devices. © 2013 Wiley Periodicals, Inc. Complexity 19: 44–55, 2014

]]>While scale-free power-laws are frequently found in social and technological systems, their authenticity, origin, and gained insights are often questioned, and rightfully so. The article presents a newly found rank-frequency power-law that aligns the top-500 supercomputers according to their performance. Pursuing a cautious approach in a systematic way, we check for authenticity, evaluate several potential generative mechanisms, and ask the “so what” question. We evaluate and finally reject the applicability of well-known potential generative mechanisms such as preferential attachment, self-organized criticality, optimization, and random observation. Instead, the microdata suggest that an inverse relationship between exponential technological progress and exponential technology diffusion through social networks results in the identified fat-tail distribution. This newly identified generative mechanism suggests that the supply and demand of technology (“technology push” and “demand pull”) align in exponential synchronicity, providing predictive insights into the evolution of highly uncertain technology markets. © 2013 Wiley Periodicals, Inc. Complexity 19: 56–65, 2014

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