This paper focuses on the multiattribute group decision making problems with linguistic intuitionistic fuzzy information. Firstly the concept of linguistic intuitionistic fuzzy numbers (LIFNs) is introduced, and then based on the LIFNs, some new aggregation operators based on Bonferroni mean and power operator are proposed, such as linguistic intuitionistic fuzzy power Bonferroni mean (LIFPBM) operator, linguistic intuitionistic fuzzy weighted power Bonferroni mean (LIFWPBM) operator, linguistic intuitionistic fuzzy geometric power Bonferroni mean (LIFGPBM) operator, and linguistic intuitionistic fuzzy weighted geometric power Bonferroni mean (LIFWGPBM) operator. Then, some properties are proved such as idempotency, permutation, and boundedness. Besides, some special situations of the operators are explored. After that, an approach based of the LIFWGPBM and LIFWGPBM operators is proposed. Finally an example is used to illustrate the validity of the developed method.

Peide Liu

Xi Liu

Multiattribute Group Decision Making Methods Based on Linguistic Intuitionistic Fuzzy Power Bonferroni Mean Operators

We consider distributed units that interact by message-passing. Each message carries a tag and causes the receiving unit to send out messages as a function of the tags it has received and a threshold. This simple model abstracts some of the essential characteristics of several artificial intelligence systems and of biological systems epitomized by the brain. We study the integration of information inside a temporal window as the dynamics unfolds. We quantify information integration by the total correlation, relative to the window&#x2019;s duration, of a set of random variables valued as a function of message arrival. Total correlation refers to the rise of information gain above that which the units achieve individually, being therefore related to some models of consciousness. We report on extensive computational experiments exploring the interrelations of the model&#x2019;s parameters (two probabilities and the threshold). We find that total correlation can occur at significant fractions of the maximum possible value and reinterpret the model&#x2019;s parameters in terms of the current best estimates of some quantities pertaining to cortical structure and dynamics. We find the resulting possibilities to be well aligned with the time frames within which percepts are thought to be processed and eventually rendered conscious.

Valmir C. Barbosa

Information Integration from Distributed Threshold-Based Interactions

Dengue virus infection is one of those epidemic diseases that require much consideration in order to save the humankind from its unsafe impacts. According to the World Health Organization (WHO), 3.6 billion individuals are at risk because of the dengue virus sickness. Researchers are striving to comprehend the dengue threat. This study is a little commitment to those endeavors. To observe the robustness of the dengue network, we uprooted the links between nodes randomly and targeted by utilizing different centrality measures. The outcomes demonstrated that 5&#x25; targeted attack is equivalent to the result of 65&#x25; random assault, which showed the topology of this complex network validated a scale-free network instead of random network. Four centrality measures (Degree, Closeness, Betweenness, and Eigenvector) have been ascertained to look for focal hubs. It has been observed through the results in this study that robustness of a node and links depends on topology of the network. The dengue epidemic network presented robust behaviour under random attack, and this network turned out to be more vulnerable when the hubs of higher degree have higher probability to fail. Moreover, representation of this network has been projected, and hub removal impact has been shown on the real map of Gombak (Malaysia).

Hafiz Abid Mahmood Malik

Faiza Abid

Mohamed Ridza Wahiddin

Zeeshan Bhatti

Robustness of Dengue Complex Network under Targeted versus Random Attack

We investigate finite-time stability of a class of nonlinear large-scale systems with interval time-varying delays in interconnection. Time-delay functions are continuous but not necessarily differentiable. Based on Lyapunov stability theory and new integral bounding technique, finite-time stability of large-scale systems with interval time-varying delays in interconnection is derived. The finite-time stability criteria are delays-dependent and are given in terms of linear matrix inequalities which can be solved by various available algorithms. Numerical examples are given to illustrate effectiveness of the proposed method.

T. La-inchua

P. Niamsup

Xinzhi Liu

Finite-Time Stability of Large-Scale Systems with Interval Time-Varying Delay in Interconnection

A new four-dimensional, hyperchaotic dynamic system, based on Lorenz dynamics, is presented. Besides, the most representative dynamics which may be found in this new system are located in the phase space and are analyzed here. The new system is especially designed to improve the complexity of Lorenz dynamics, which, despite being a paradigm to understand the chaotic dissipative flows, is a very simple example and shows great vulnerability when used in secure communications. Here, we demonstrate the vulnerability of the Lorenz system in a general way. The proposed 4D system increases the complexity of the Lorenz dynamics. The trajectories of the novel system include structures going from chaos to hyperchaos and chaotic-transient solutions. The symmetry and the stability of the proposed system are also studied. First return maps, Poincar&#xe9; sections, and bifurcation diagrams allow characterizing the global system behavior and locating some coexisting structures. Numerical results about the first return maps, Poincar&#xe9; cross sections, Lyapunov spectrum, and Kaplan-Yorke dimension demonstrate the complexity of the proposed equations.

Mar&#xed;a Pilar Mareca

Borja Bordel

Improving the Complexity of the Lorenz Dynamics

A company in a competitive environment that wishes to be a benchmark in the business world needs a management model that enables the development of systemic thinking on the part of its executives. In addition to systemic thinking, it is also necessary that executives (i) are aware that the decision-making processes should be shared, (ii) have bounded rationality, and (iii) exert political influence according to their preferences. In this context, the aim of this paper is to describe a conceptual scientific model for strategic decision-making from rules originating from Complex Adaptive Systems and the following mathematical techniques: Analytic Network Process and Linear Programming. This applied and quantitative study is a theoretical essay developed from an integrative review of the aforementioned concepts and techniques, resulting in the proposition of a scientific and conceptual mathematical model that can be applied to a wide variety of business environments. The results obtained from a hypothetical example (Strategic Operation Management Decision) show that the model is able to rank a set of strategic decisions in the environment of most companies and generate information to minimize the negative effects of shared decisions.

Dewey Wollmann

Maria Teresinha Arns Steiner

The Strategic Decision-Making as a Complex Adaptive System: A Conceptual Scientific Model

The question of how complex systems become more organized and efficient with time is open. Examples are the formation of elementary particles from pure energy, the formation of atoms from particles, the formation of stars and galaxies, and the formation of molecules from atoms, of organisms, and of the society. In this sequence, order appears inside complex systems and randomness (entropy) is expelled to their surroundings. Key features of self-organizing systems are that they are open and they are far away from equilibrium, with increasing energy flows through them. This work searches for global measures of such self-organizing systems, which are predictable and do not depend on the substrate of the system studied. Our results will help to understand the existence of complex systems and mechanisms of self-organization. In part we also provide insights, in this work, about the underlying physical essence of Moore&#x2019;s law and the multiple logistic growth observed in technological progress.

Georgi Yordanov Georgiev

Atanu Chatterjee

Germano Iannacchione

Exponential Self-Organization and Moore&#x2019;s Law: Measures and Mechanisms

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Complexity publishes original research and review articles across a broad range of disciplines with the purpose of reporting important advances in the scientific study of complex systems.

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Chief Editor, Prof Sayama, is currently researching complex dynamical networks, human and social dynamics, artificial life, and interactive systems while working at Binghamton University, State University of New York.

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Complexity publishes original research and review articles across a broad range of disciplines with the purpose of reporting important advances in the scientific study of complex systems.

Complexity