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This is a call for papers that should contribute to the unification of behavioral sciences and team management, focusing on the biological origin of cooperation and swarm intelligence, moving from biology to psychology and from sociology to political science, with the help of the theoretical tools of complex networks. This issue should shed light into the origin of ergodicity breaking and contribute to establishing a connection, still lacking theoretical support, between complexity properties that are expected to be correlated. Examples are: non-Poisson renewal events and multi-fractality; complexity matching and chaos synchronization; criticality and extended criticality of small size systems. Although the emphasis is on systems of small size, and especially on the search of the size maximizing both information transport and cooperation emergence, special attention will be devoted to the interaction between small groups and their larger societies. [ABSTRACT FROM AUTHOR]

Purpose: The links between moral communication and legal communication have long been studied in sociology of law. Little has yet been said about moral communication invoking when communication in the legal system is impossible, ineffective or uncertain. The paper fills this gap to demonstrate that systems theory-based sociology of law can effectively recognise the role of moral communication in such situations. Design/methodology/approach: The paper presents an empirical study of moral communication in small and medium-sized enterprises (SMEs). It focused on situations when SMEs' interactions with function systems, particularly the legal system, result in irremovable legal uncertainty. The data depict strategies of managing such uncertainty and were obtained in a paths-to-justice survey of 7,292 owners and managers of SMEs and 101 in-depth interviews. The findings are interpreted using the author's concept of "uncertainty translation", rooted in Luhmann's systems theory. It suggests that business organisations such as SMEs deal with the ubiquitous uncertainty in their operations by translating it into a convenient type. Findings: The study distinguishes between formative and absorbing moral communication and finds that both types play a role in steering the uncertainty translation mechanism in SMEs. Six scenarios of invoking moral communication are identified in SMEs dealing with legal uncertainty. In such scenarios, moral communication facilitates the translation of business uncertainty "away from law". Under some circumstances, this, in turn, leads to latent systematic results, reflexively affecting the legal system, the economic system and the SMEs. Research limitations/implications: In its core argument, the study is based on qualitative material. While it identifies empirical scenarios of invoking moral communication, it does not report the prevalence of these scenarios due to methodological limitations. Originality/value: The study results pose questions related to the staple theoretical issue in post-Luhmannian social systems theory: functional differentiation. If moral communication–a type of communication not linked to any social system–can produce far-reaching, systematic results that affect function systems, then the functional differentiation thesis should be less pronounced than Luhmann typically stressed. This said, the paper argues that the contradiction between the findings and Luhmannian theory of morality is only apparent and may be reconciled. [ABSTRACT FROM AUTHOR]

With the prevalence of COVID-19, the modeling of epidemic propagation and its analyses have played a significant role in controlling epidemics. However, individual behaviors, in particular the self-protection and migration, which have a strong influence on epidemic propagation, were always neglected in previous studies. In this paper, we mainly propose two models from the individual and population perspectives. In the first individual model, we introduce the individual protection degree that effectively suppresses the epidemic level as a stochastic variable to the SIRS model. In the alternative population model, an open Markov queueing network is constructed to investigate the individual number of each epidemic state, and we present an evolving population network via the migration of people. Besides, stochastic methods are applied to analyze both models. In various simulations, the infected probability, the number of individuals in each state and its limited distribution are demonstrated. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the paper is to discuss particular features of the public debate around the COVID-19 pandemic and its mitigation strategies in Croatian media from the beginning of 2020 to mid-September of the same year. Design/methodology/approach: The discussion is theoretically grounded on Luhmann's concept of moral communication combined with the key assumption of critical discourse analysis that language reflects a position of power of social actors. Based on these premises, the analysis of a sample of articles in a chosen online media was conducted to uncover the moral codes in the public debate concerning the corona outbreak and connect them with specific moral discourses of particular social actors. Findings: The findings clearly indicate that the communication about the pandemic is considerably imbued with moralization and that moral coding is profoundly used to generate preferred types of behaviour of citizens and their compliance with the imposed epidemiologic measures. In conclusion, Luhmann's claim of moralization as a contentious form of communication is confirmed as the examined public discussion fosters confrontations and generates disruptions rather than contributing to a productive dialogue among diverse social actors. Originality/value: The novelty of the approach lies in the combination of Luhman's conceiving of moral communication with critical discourse analysis that, taken together, entails a pertinent research tool for analysing relevant attributes of the ongoing vibrant debate on the coronavirus outbreak. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to review Luhmann's theory of moral communication while focusing on symmetry conditions, in light of Armin Nassehi's criticism, to clarify issues regarding this concept. Then, Luhmann's symmetry condition is reconstructed as a concept containing double meaning via a case study in Japan. Correspondingly, interesting situations and characteristics of moral communication, such as "inflation," the "polemogene" and ubiquity of moral communication, are interpreted more consistently. Design/methodology/approach: In today's society, moral communication may spiral out of control and even be fatal. By examining Niklas Luhmann's theory, in this paper, the author elaborates on why and how this mechanism occurs. Findings: The author emphasizes that the suspicion pertaining to the asymmetry of communication is stressed in the case of anonymity. When an individual communicates using a moral code, it is impossible to discern whether the implications of self-bindingness are undermined or not through observations or consequences of communication and can only be questioned or confirmed through communication. However, criticizing the outburst of the masses and exchanging blame by isolating only one aspect of such a phenomenon will only be superficial. Originality/value: This study reveals that the very condition that makes moral communication possible enables people to communicate respectfully or contemptuously with others without any special qualification. Such an analysis can serve as a theoretical underpinning for the analysis of today's phenomena. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to explain the US society's insignificant mitigation of climate change using Niklas Luhmann's (1989) autopoietic social systems theory in ecological communication. Specifically, the author's analysis falls within the context of Luhmann re-moralized while focusing on particular function systems' binary codes and their repellence of substantive US climate change mitigation policy across systems. Design/methodology/approach: The author achieves this purpose by resituating Luhmann's conception of evolution to forgo systems teleology and better contextualize the spatial-temporal scale of climate change; reinforcing complexity reduction and differentiation by integrating communication and media scholar John D. Peters's (1999) "communication chasm" concept as one mechanism through which codes sustain over time; and applying these integrated concepts to prominent the US climate change mitigation attempts. Findings: The author concludes that climate change mitigation efforts are the amalgamation of the systems' moral communications. Mitigation efforts have relegated themselves to subsystems of the ten major systems given the polarizing nature of their predominant care/harm moral binary. Communication chasms persist because these moral communications cannot both adhere to the systems' binary codes and communicate the climate crisis's urgency. The more time that passes, the more codes force mitigation organizations, activist efforts and their moral communications to adapt and sacrifice their actions to align with the encircling systems' code. Social implications: In addition to the conceptual contribution, the social implication is that by identifying how and why climate change mitigation efforts are subsumed by the larger systems and their codes, climate change activists and practitioners can better tool their tactics to change the codes at the heart of the systems if serious and substantive climate change mitigation is to prevail. Originality/value: To the author's knowledge, there has not been an integration of a historical communication concept into, and sociological application of, ecological communication in the context of climate change mitigation. [ABSTRACT FROM AUTHOR]

Purpose: The paper combines the systems theoretical perspective on the evolution of societal differentiation and the emergence of codes in communication. By combining the approach by Niklas Luhmann with a historical theology on the development of Christian morality split between God and Devil, it recreates a sociological point of observation on contemporary moral forms by a temporary occupation of the retired Christian Devil. Design/methodology/approach: The article combines a Luhmannian systems theoretical perspective on the evolution of societal differentiation with a concept of emerging codes in communication. The latter is based on on the development of a Christian view of morality being split between God and Devil. It establishes a sociological point of observation on contemporary moral forms through the temporary invocation of the retired figure of the Christian Devil. Findings: The proposed perspective develops a healthy perspective on the exuberant distribution of a health(y) morality across the globe during the pandemic crisis of 2020–21. The temporary invocation of the retired Christian Devil as point of departure in this sociological analysis allows for a disturbing view on the unlimited growth of the morality of health and its inherent dangers of dedifferentiating the highly specialised forms of societal differentiation and organisation. Originality/value: By applying the diabolical perspective, the analytical framework creates a unique opportunity to observe the moral encodings of semantic forms in detail, while keeping the freedom of scientific enquiry to choose amongst available distinctions in the creation of sound empirical knowledge. This article adopts a neutral stance, for the good of sociological analysis. The applications of the term "evil" to observations of communication are indifferent to anything but itself and its qualities as scientific enquiry. [ABSTRACT FROM AUTHOR]

The article describes an integrated lens with an epistemological base in social work, sociology and theology that facilitates multidimensional, multidisciplinary analysis of the strengths and challenging aspects of society and their impacts. It describes the steps of the integrated approach to analysis and its development due to gaps in knowledge about rurality, particularly the rural Anglican church's engagement with different groups. It discusses examples showing the efficacy of the lens.

Purpose: This paper aims to build around an abductive argument: the epistemological value of the Arts-derived knowledge is equivalent and may be supplementary to that of science, contributing to the literature on the epistemological mistrust between both systems of knowledge. Design/methodology/approach: This essay proposes a conceptual model – a tool, in Kuhn's terms – grounded on the sociology of knowledge (Berger and Luckmann, 1967; Schütz, 1951), to frame the apprehension of reality from a social perspective, and the philosophical pragmatism (Peirce, 2012), considering the fixation of beliefs as the seminal concept that leads to the legitimation of knowledge in society. The proposed conceptual model guides analysis on the epistemological value of the knowledge derived from the Arts and supports reflection on the commonalities between both finite provinces of meaning. Findings: Reproducibility, doxastic grounding, community/membership, intersubjectivity and evidence are criteria identified as commonalities between the Arts and Science. Acceptance and legitimation across finite provinces of meaning emerge to produce minimally acceptable objectivity, made possible by the mutual validation of impressions. Research limitations/implications: The discussion on greater levels of aesthetic appreciation has been eclipsed by the authors' intention to find specific epistemological properties of knowledge derived from the Arts. Practical implications: As practitioners in applied social science, management researchers are supposed to have mastery over how to apply what they know. So, the findings suggest participation (becoming accepted, first of all) in communities of practice, learning from and contributing to distinct finite provinces of meaning. The role of organizations in the understanding of knowledge derived from the Arts and its application might be that of a protagonist, promoting creativity and innovation through openness to new perspectives on knowledge. Originality/value: This essay rescues knowledge as not a justified true belief, but the result of fixed beliefs continuously and socially legitimated. This rescue escapes previous attempts that appeal to Gettier-type counterexamples. A conceptual model was proposed to frame knowledge from a philosophical and sociological perspective and represent a methodological contribution of this essay. The proposition of third-order interdisciplinarity, also represents a contribution, of conceptual nature. [ABSTRACT FROM AUTHOR]

This paper presents a systematic method to analyze the stability of systems with single delay in which the coefficient polynomials of the characteristic equation depend on the delay. Such systems often arise in, for example, life science and engineering systems. A method to analyze such systems was presented by Beretta and Kuang in a 2002 paper, but with some very restrictive assumptions. This paper extends their results to the general case with the exception of some degenerate cases. It is found that a much richer behavior is possible when the restrictive assumptions are removed. The interval of interest for the delay is partitioned into subintervals so that the magnitude condition generates a fixed number of frequencies as functions of the delay within each subinterval. The crossing conditions are expressed in a general form, and a simplified derivation for the first-order derivative criterion is obtained. Illustrative examples are also presented in this paper. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this study is to examine the unintended consequences of actions as one of the central and constituent elements of sociological theory and long debated in the history of sociology. This question has been treated under varying sociological terminologies, including, providence, social forces, social paradoxes, heterogeneity of ends, immanent causality and the principle of emergency.Design/methodology/approach This paper is concerned with "adverse effects". The thematic contexts of "unintended consequences of social action" the authors wish to focus attention on are specific types of consequences which may merit the adjective "adverse".Findings The analysis of the intentions of our actions and their unwanted or foreseen consequences allows us to understand how societies work. Many historical facts are probably "unintentional." But, most continuous or changing life forms must be interpreted as a mixture of intentional (social reproduction) and unintentional consequences (social change).Originality/value This paper focuses on four points of view: the object of sociology, the problems of order and social change, the methodological status of the discipline and the nature of social explanation, and mathematical theory. Four classifications of unintended consequences are formulated from the works of Boudon, Baert and Ramos, as well as the authors. [ABSTRACT FROM AUTHOR]

This paper presents the integration of reinforcement learning-based differential evolution (DE) with the cooperative coevolution (R-CCDE) method in a compensatory neuro-fuzzy controller (CNFC). The CNFC model employs compensatory fuzzy operations, which increase the adaptability and effectiveness of the controller. The R-CCDE method was used to determine an adequate control policy for nonlinear system problems. The evolution of a population involved the use of DE with cooperative coevolution to adjust CNFC parameters, and the fitness function of the R-CCDE method is used by a reinforcement signal to determine the controller that can be used to solve the control problem. This paper identified the best performing controller to solve nonlinear system problems. The simulation results of the proposed R-CCDE method were compared with those of various DE methods and the performance of the proposed R-CCDE method was superior to that of the other methods. [ABSTRACT FROM AUTHOR]

As the integrated circuit (IC) technology advances into smaller nanometre feature sizes, a fixed-error noise known as device mismatch is introduced owing to the dissimilarity between transistors, and this degrades the accuracy of analog circuits. In this paper, we present an analog co-processor that uses this fixed-pattern noise to its advantage to perform complex computation. This circuit is an extension of our previously published trainable analogue block (TAB) framework and uses multiple inputs that substantially increase functionality. We present measurement results of our two-input analogue co-processor built using a 130-nm process technology and show its learning capabilities for regression and classification tasks. We also show that the co-processor, comprised of 100 neurons, is a low-power system with a power dissipation of only 1.1~\mu \textW . The IC fabrication process contributes to randomness and variability in ICs, and we show that random device mismatch is favorable for the learning capability of our system as it causes variability among the neuronal tuning curves. The low-power capability of our framework makes it suitable for use in various battery-powered applications ranging from biomedical to military as a front-end analog co-processor. [ABSTRACT FROM AUTHOR]

Purpose Cross tables are omnipresent in management, academia and popular culture. The Matrix has us, despite all criticism, opposition and desire for a way out. This paper draws on the works of three agents of the matrix. The paper shows that Niklas Luhmann criticised Talcott Parsons’ traditional matrix model of society and proceeded to update systems theory, the latest version of which is coded in the formal language of George Spencer Brown. As Luhmann failed to install his updates to all components of his theory platform, however, regular reoccurrences of Parsonian crosstabs are observed, particularly in the Luhmannian differentiation theory, which results in compatibility issues and produces error messages requesting updates. This paper aims to code the missing update translating the basic matrix structure from Parsonian into Spencer Brownian formal language.Design/methodology/approach This paper draws on work by Boris Hennig and Louis Kauffman and a yet unpublished manuscript by George Spencer Brown, to demonstrate that the latter introduced his cross as a mark to indicate NOR gates in circuit diagrams. The paper also shows that this NOR gate marker has been taken out of and may be observed to contain the tetralemma, an ancient matrix structure already present in traditional Indian logic. It then proceeds to translate the basic structure of traditional contingency tables into a Spencer Brownian NOR equation and to demonstrate the difference this translation makes in the modelling of social systems.Findings The translation of cross tables from Parsonian into Spencer Brownian formal language results in the design of a both matrix-shaped and compatible test routine that works as a virtual window for the observation of the actually unobservable medium in which a form is drawn, and can be used for consistency checks of expressions coded in Spencer Brownian formal language.Originality/value This paper quotes from and discusses a so far unpublished manuscript finalised by Spencer Brown in April 1961. The basic matrix structure is translated from Parsonian into Spencer Brownian formal language. A Spencer Brownian NOR matrix is coded that may be used to detect errors in expressions coded in Spencer Brownian formal language. [ABSTRACT FROM AUTHOR]

Pairwise-interaction graphical games have been widely used in the study and design of strategic interaction in multiagent systems. With regard to this issue, one entitative problem is actually to understand how the interaction structure of agents affects the strategy configuration of Nash equilibria. This paper intends to study the effect of interaction networks on Nash equilibria in pairwise-interaction graphical games. We first show that interaction networks may induce new strategy equilibria in pairwise-interaction graphical games and then provide graphical conditions for the existence of these network-induced equilibria. Furthermore, to determine Nash equilibria of pairwise-interaction graphical games, a graphical replicator dynamics model is formulated, and its connection with graphical games is established. In detail, it is shown that every Nash equilibrium of the graphical games corresponds to a fixed point of the graphical replicator dynamics and that every asymptotically stable fixed point of the graphical replicator dynamics corresponds to a strict pure Nash equilibrium of the graphical games. The obtained results are applied in understanding coordination in complex networks and determination of structural conflicts in signed graphs. This work may provide new insights into understanding and designing strategy equilibria and dynamics in games on networks. [ABSTRACT FROM AUTHOR]

In dynamic optimization problems (DOPs), as the environment changes through time, the optima also dynamically change. How to adapt to the dynamic environment and quickly find the optima in all environments is a challenging issue in solving DOPs. Usually, a new environment is strongly relevant to its previous environment. If we know how it changes from the previous environment to the new one, then we can transfer the information of the previous environment, e.g., past solutions, to get new promising information of the new environment, e.g., new high-quality solutions. Thus, in this paper, we propose a neural network (NN)-based information transfer method, named NNIT, to learn the transfer model of environment changes by NN and then use the learned model to reuse the past solutions. When the environment changes, NNIT first collects the solutions from both the previous environment and the new environment and then uses an NN to learn the transfer model from these solutions. After that, the NN is used to transfer the past solutions to new promising solutions for assisting the optimization in the new environment. The proposed NNIT can be incorporated into population-based evolutionary algorithms (EAs) to solve DOPs. Several typical state-of-the-art EAs for DOPs are selected for comprehensive study and evaluated using the widely used moving peaks benchmark. The experimental results show that the proposed NNIT is promising and can accelerate algorithm convergence. [ABSTRACT FROM AUTHOR]

Mean field game theory has been developed largely following two routes. One of them, called the direct approach, starts by solving a large-scale game and next derives a set of limiting equations as the population size tends to infinity. The second route is to apply mean field approximations and formalize a fixed point problem by analyzing the best response of a representative player. This paper addresses the connection and difference of the two approaches in a linear quadratic (LQ) setting. We first introduce an asymptotic solvability notion for the direct approach, which means for all sufficiently large population sizes, the corresponding game has a set of feedback Nash strategies in addition to a mild regularity requirement. We provide a necessary and sufficient condition for asymptotic solvability and show that in this case the solution converges to a mean field limit. This is accomplished by developing a re-scaling method to derive a low-dimensional ordinary differential equation (ODE) system, where a non-symmetric Riccati ODE has a central role. We next compare with the fixed point approach which determines a two-point boundary value (TPBV) problem, and show that asymptotic solvability implies feasibility of the fixed point approach, but the converse is not true. We further address non-uniqueness in the fixed point approach and examine the long time behavior of the non-symmetric Riccati ODE in the asymptotic solvability problem. [ABSTRACT FROM AUTHOR]

In this paper, we present necessary and sufficient conditions for two given functions to be the Lyapunov and the upper Bohl exponent of a certain discrete linear system with diagonal coefficients. The obtained conditions have a form of easily verifiable algebraic conditions. [ABSTRACT FROM AUTHOR]

Purpose The paper aims to relate the four modes of coexistence of goal-orientated systems – conflict, hierarchy, the niche and cooperation – to static behavioral descriptions of social systems.Design/methodology/approach Analyzing the options for interactions of goal-orientated systems leads the four modes of coexistence. These show certain behavioral characteristics. Searching for these characteristics in selected behavioral descriptions in organizational theory, sociology, political science and system science allows relating them to certain modes.Findings In organizations, the four modes of coexistence show in general (Cameron and Quinn) and dyadic interaction patterns (Argyris). In sociology, they show in educational schemes preparing for specific modes (Bernstein) and in different exchange patterns (Polanyi). In political theory, the four modes of coexistence show in preferred policies of parties, in institutional development, in oligarch power politics (Winters) and in external policy. In system science, they show in problem solving strategies (Jackson) and “mindscapes” (Murayama).Research limitations/implications The analysis is restricted to static patterns leaving out dynamic developments, for example, in networks.Practical implications Together with previous investigations, the paper shows how behavioral descriptions ranging from psychology to politics correspond to the four modes of coexistence, and it suggests interrelations and alignments of the modes via various levels of societal organization.Originality/value The four modes of coexistence provide an unequivocal theoretical framework that allows finding parallels in behavioral descriptions ranging from psychology via sociology to politics. [ABSTRACT FROM AUTHOR]

Active learning (AL) is a machine-learning approach for reducing the data labeling effort. Given a pool of unlabeled samples, it tries to select the most useful ones to label so that a model built from them can achieve the best possible performance. This paper focuses on pool-based sequential AL for regression (ALR). We first propose three essential criteria that an ALR approach should consider in selecting the most useful unlabeled samples: informativeness, representativeness, and diversity, and compare four existing ALR approaches against them. We then propose a new ALR approach using passive sampling, which considers both the representativeness and the diversity in both the initialization and subsequent iterations. Remarkably, this approach can also be integrated with other existing ALR approaches in the literature to further improve the performance. Extensive experiments on 11 University of California, Irvine, Carnegie Mellon University StatLib, and University of Florida Media Core data sets from various domains verified the effectiveness of our proposed ALR approaches. [ABSTRACT FROM AUTHOR]

This paper presents a discrete-time distributed and noncooperative routing algorithm, which is proved, via Lyapunov arguments, to asymptotically converge to a specific equilibrium condition among the traffic flows over the network paths, known as Wardrop equilibrium. This convergence result improves the discrete-time algorithms in the literature, which achieve approximate convergence to the Wardrop equilibrium. Numerical simulations show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Linear discriminant analysis (LDA) is a well-known technique for linear classification, feature extraction, and dimension reduction. To improve the accuracy of LDA under the high dimension low sample size (HDLSS) settings, shrunken estimators, such as Graphical Lasso, can be used to strike a balance between biases and variances. Although the estimator with induced sparsity obtains a faster convergence rate, however, the introduced bias may also degrade the performance. In this paper, we theoretically analyze how the sparsity and the convergence rate of the precision matrix (also known as inverse covariance matrix) estimator would affect the classification accuracy by proposing an analytic model on the upper bound of an LDA misclassification rate. Guided by the model, we propose a novel classifier, $\mathcal {DBSDA}$ , which improves classification accuracy through debiasing. Theoretical analysis shows that $\mathcal {DBSDA}$ possesses a reduced upper bound of misclassification rate and better asymptotic properties than sparse LDA (SDA). We conduct experiments on both synthetic datasets and real application datasets to confirm the correctness of our theoretical analysis and demonstrate the superiority of $\mathcal {DBSDA}$ over LDA, SDA, and other downstream competitors under HDLSS settings. [ABSTRACT FROM AUTHOR]

Aggregate load modeling is of fundamental importance for systematic analysis and design of various demand response strategies. Instead of keeping track of the trajectories of individual loads, the aggregate modeling problem focuses on characterizing the density evolution of the load population. Most existing models are only applicable to thermostatically controlled loads (TCLs) with first-order linear dynamics. This paper develops a unified aggregate modeling approach that can be used for general TCLs as well as deferrable loads. We propose a deterministic hybrid system model to describe individual load dynamics under demand response rules, and develop a general stochastic hybrid system (SHS) model to capture the population dynamics. We also derive a set of partial differential equations (PDEs) that govern the probability density evolution of the SHS. Our results cannot be obtained using the existing SHS tools in the literature as the proposed SHS model involves both random and deterministic switchings with general switching surfaces in multidimensional domains. The derived PDE model includes many existing aggregate load modeling results as special cases and can be used in many other realistic modeling scenarios that have not been studied in the literature. [ABSTRACT FROM AUTHOR]

This paper studies the linear–quadratic mean-field game (MFG) for a class of stochastic delayed systems. We consider a large-population system, where the dynamics of each agent is modeled by a stochastic differential delayed equation. The consistency condition is derived through an auxiliary system, which is an anticipated forward–backward stochastic differential equation with delay (AFBSDDE). The wellposedness of such an AFBSDDE system can be obtained using a continuation method. Thus, the MFG strategies can be defined on an arbitrary time horizon, not necessary on a small time horizon by a commonly used contraction mapping method. Moreover, the decentralized strategies are verified to satisfy the $\epsilon$ -Nash equilibrium property. For illustration, three special cases of delayed systems are further explored, for which the closed-loop and open-loop MFG strategies are derived, respectively. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to propose a holistic structural framework for a sustainable renewal that embraces all relevant contexts – individual, organizational, local-regional and worldwide. This should help humanity achieve a future in which society, economy and ecology are united in an evolutionary process based on multiple symbiosis. Design/methodology/approach – An integrative concept for sustainable renewal is presented, based on Beer’s Viable System Model (VSM). The core of that concept is a recursive structure, which organizes the tasks necessary for such renewal. The approach is both analytical and synthetic, proposing a design for the levels of recursion, making up a coherent whole. Findings – A structure is developed that enables agents at all recursive strata to generate variety in balance with the complexities they face. The organizational architecture based on the VSM, applied to each one of those levels, ensures the necessary and sufficient structural preconditions for the sustainability of the system under study. Practical implications – The concept proposed here is ready to be used as a blueprint for organizing the efforts for sustainability. It can help decision makers understand that the quest for sustainable renewal is a recursive issue involving all planes, from individual to global. Originality/value – The quest for the ecological sustainability of planet earth at this stage is not at all successful. The cybernetic model used here organizes the efforts for sustainability in a more effective way than conventional approaches. It also delivers powerful clues for sustainable renewal that are new, in particular a key to the sufficient structural preconditions for sustainability. This paper is an extended version of the Ross Ashby Memorial Lecture delivered by the author at the European Meeting of Cybernetics and Systems Research, Vienna, 24 April 2014, under the title “Organizing for Sustainability”. [ABSTRACT FROM AUTHOR]

Most learning methods contain optimization as a substep, where the nondifferentiability and multimodality of objectives push forward the interplay of evolutionary optimization algorithms and machine learning models. The recently emerged evolutionary multimodal optimization (MMOP) technique enables the learning of diverse sets of effective parameters for the models simultaneously, providing new opportunities to the applications requiring both accuracy and diversity, such as ensemble, interactive, and interpretive learning. Targeting at locating multiple optima simultaneously in the multimodal landscape, this paper develops an efficient neighborhood-based niching algorithm. Bare-bones differential evolution is used as the baseline. Further, using Gaussian mutation with local mean and standard deviations, the neighborhoods capture niches that match well with the contours of peaks in the landscape. To increase diversity and enhance global exploration, the proposed algorithm embeds a diversity preserving operator to reinitialize converged or overlapped neighborhoods. The experimental results verify that the proposed algorithm has superior and consistent performance for a wide range of MMOP problems. Further, the algorithm has been successfully applied to train neural network ensembles, which validates its effectiveness and benefits of learning multimodal parameters. [ABSTRACT FROM AUTHOR]

Within the realm of smart buildings and smart cities, dynamic response management is playing an ever-increasing role, thus attracting the attention of scientists from different disciplines. Dynamic demand response management involves a set of operations aiming at decentralizing the control of loads in large and complex power networks. Each single appliance is fully responsive and readjusts its energy demand to the overall network load. A main issue is related to mains frequency oscillations resulting from an unbalance between supply and demand. In a nutshell, this paper contributes to the topic by equipping each consumer with strategic insight. In particular, we highlight three main contributions and a few other minor contributions. First, we design a mean-field game for a population of thermostatically controlled loads, study the mean-field equilibrium for the deterministic mean-field game, and investigate on asymptotic stability for the microscopic dynamics. Second, we extend the analysis and design to uncertain models, which involve both stochastic or deterministic disturbances. This leads to robust mean-field equilibrium strategies guaranteeing stochastic and worst-case stability, respectively. Minor contributions involve the use of stochastic control strategies rather than deterministic and some numerical studies illustrating the efficacy of the proposed strategies. [ABSTRACT FROM PUBLISHER]

This paper considers the consensus behavior of a spatially distributed 3-D dynamical network composed of heterogeneous agents: leaders and followers, in which the leaders have the preferred information about the destination, while the followers do not have. All followers move in a 3-D Euclidean space with a given speed and with their headings updated according to the average velocity of the corresponding neighbors. Compared with the 2-D model, a key point lies in how to analyze the dynamical behavior of a “linear” nonhomogeneous equation where the nonhomogeneous term strongly nonlinearly depends on the states of all agents. Using the network structure and the estimation of some characteristics for the initial states, we present a proper decaying rate for the nonhomogeneous term and then establish lower bounds on the ratio of the number of leaders to the number of followers that is needed for the expected consensus by considering two cases: 1) fixed speed and neighborhood radius and 2) variable speed and neighborhood radius with respect to the population size. Some simulation examples are given to justify the theoretical results. [ABSTRACT FROM PUBLISHER]

Nonnegative matrix factorization (NMF) is an advanced method for nonnegative feature extraction, with widespread applications. However, the NMF solution often entails to solve a global optimization problem with a nonconvex objective function and nonnegativity constraints. This paper presents a collective neurodynamic optimization (CNO) approach to this challenging problem. The proposed collective neurodynamic system consists of a population of recurrent neural networks (RNNs) at the lower level and a particle swarm optimization (PSO) algorithm with wavelet mutation at the upper level. The RNNs act as search agents carrying out precise local searches according to their neurodynamics and initial conditions. The PSO algorithm coordinates and guides the RNNs with updated initial states toward global optimal solution(s). A wavelet mutation operator is added to enhance PSO exploration diversity. Through iterative interaction and improvement of the locally best solutions of RNNs and global best positions of the whole population, the population-based neurodynamic systems are almost sure able to achieve the global optimality for the NMF problem. It is proved that the convergence of the group-best state to the global optimal solution with probability one. The experimental results substantiate the efficacy and superiority of the CNO approach to bound-constrained global optimization with several benchmark nonconvex functions and NMF-based clustering with benchmark data sets in comparison with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to show how insight obtained from a mean-field model can be used to create an architecture for approximate dynamic programming (ADP) for a certain class of games comprising of a large number of agents. The general technique is illustrated with the aid of a mean-field oscillator game model introduced in our prior work. The states of the model are interpreted as the phase angles for a collection of nonhomogeneous oscillators, and in this way the model may be regarded as an extension of the classical coupled oscillator model of Kuramoto. The paper introduces ADP techniques for design and adaptation (learning) of approximately optimal control laws for this model. For this purpose, a parameterization is proposed, based on an analysis of the mean-field PDE model for the game. In an offline setting, a Galerkin procedure is introduced to choose the optimal parameters while in an online setting, a steepest descent algorithm is proposed. The paper provides a detailed analysis of the optimal parameter values as well as the Bellman error with both the Galerkin approximation and the online algorithm. Finally, a phase transition result is described for the large population limit when each oscillator uses the approximately optimal control law. A critical value of the control penalty parameter is identified: above this value, the oscillators are incoherent; and below this value (when control is sufficiently cheap) the oscillators synchronize. These conclusions are illustrated with results from numerical experiments. [ABSTRACT FROM AUTHOR]

Huang (2010) and Nguyen and Huang (2012) solved the linear quadratic mean field systems and control problem in the case where there is a major agent (i.e. non-asymptotically vanishing as the population size goes to infinity) together with a population of minor agents (i.e. individually asymptotically negligible). The new feature in this case is that the mean field becomes stochastic and then, by minor agent state extension, the existence of $\epsilon$-Nash equilibria together with the individual agents' control laws that yield the equilibria may be established. This paper presents results initially announced by Caines and Kizilkale (2013, 2014) where it is shown that if the major agent's state is partially observed by the minor agents, and if the major agent completely observes its own state, all agents can recursively generate estimates (in general individually distinct) of the major agent's state and the mean field, and thence generate feedback controls yielding $\epsilon$ -Nash equilibria. [ABSTRACT FROM PUBLISHER]

This paper considers two classes of large population stochastic differential games connected to optimal and robust decentralized control of large-scale multiagent systems. The first problem (P1) is one where each agent minimizes an exponentiated cost function, capturing risk-sensitive behavior, whereas in the second problem (P2 ) each agent minimizes a worst-case risk-neutral cost function, where the “worst case” stems from the presence of an adversary entering each agent’s dynamics characterized by a stochastic differential equation. In both problems, the individual agents are coupled through the mean field term included in each agent’s cost function, which captures the average or mass behavior of the agents. We solve both P1 and P2 via mean field game theory. Specifically, we first solve a generic risk-sensitive optimal control problem and a generic stochastic zero-sum differential game, where the corresponding optimal controllers are applied by each agent to construct the mean field systems of P1 and P2. We then characterize an approximated mass behavior effect on an individual agent via a fixed-point analysis of the mean field system. For each problem, P1 and P2, we show that the approximated mass behavior is in fact the best estimate of the actual mass behavior in various senses as the population size, $N$, goes to infinity. Moreover, we show that for finite $N$, there exist $\epsilon$- Nash equilibria for both P1 and P2, where the corresponding individual Nash strategies are decentralized in terms of local state information and the approximated mass behavior. We also show that $\epsilon$ can be taken to be arbitrarily small when $N$ is sufficiently large. We show that the $\epsilon$- Nash equilibria of P1 and P2 are partially equivalent in the sense that the individual Nash strategies share identical control laws, but the approximated mass behaviors for P1 and P2 are different, since in P2, the mass behavior is also affected by the associated worst-case disturbance. Finally, we prove that the Nash equilibria for P1 and P2 both feature robustness, and as the parameter characterizing this robustness becomes infinite, the two Nash equilibria become identical and equivalent to that of the risk-neutral case, as in the one-agent risk-sensitive and robust control theory. [ABSTRACT FROM AUTHOR]

This paper concerns state estimation problems in a mean field control setting. In a finite population model, the goal is to estimate the joint distribution of the population state and the state of a typical individual. The observation equations are a noisy measurement of the population. The general results are applied to demand dispatch for regulation of the power grid, based on randomized local control algorithms. In prior work by the authors it is shown that local control can be designed so that the aggregate of loads behaves as a controllable resource, with accuracy matching or exceeding traditional sources of frequency regulation. The operational cost is nearly zero in many cases. The information exchange between grid and load is minimal, but it is assumed in the overall control architecture that the aggregate power consumption of loads is available to the grid operator. It is shown that the Kalman filter can be constructed to reduce these communication requirements, and to provide the grid operator with accurate estimates of the mean and variance of quality of service (QoS) for an individual load. [ABSTRACT FROM AUTHOR]

Natural systems are inextricably affected by noise. Within recent decades, the manner in which noise affects the collective behavior of self-organized systems, specifically, has garnered considerable interest from researchers and developers in various fields. To describe the collective motion of multiple interacting particles, Vicsek et al. proposed a well-known self-propelled particle (SPP) system, which exhibits a second-order phase transition from disordered to ordered motion in simulation; due to its non-equilibrium, randomness, and strong coupling nonlinear dynamics, however, there has been no rigorous analysis of such a system to date. To decouple systems consisting of deterministic laws and randomness, we propose a general method which transfers the analysis of these systems to the design of cooperative control algorithms. In this study, we rigorously analyzed the original Vicsek model under both open and periodic boundary conditions for the first time, and developed extensions to heterogeneous SPP systems (including leader-follower models) using the proposed method. Theoretical results show that SPP systems switch an infinite number of times between ordered and disordered states for any noise intensity and population density, which implies that the phase transition indeed takes a nontraditional form. We also investigated the robust consensus and connectivity of these systems. Moreover, the findings presented in this paper suggest that our method can be used to predict possible configurations during the evolution of complex systems, including turn, vortex, bifurcation, and flock merger phenomena as they appear in SPP systems. [ABSTRACT FROM PUBLISHER]

This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance. [ABSTRACT FROM PUBLISHER]

This paper introduces the backward mean-field (MF) linear-quadratic-Gaussian (LQG) games (for short, BMFLQG) of weakly coupled stochastic large-population system. In contrast to the well-studied forward mean-field LQG games, the individual state in our large-population system follows the backward stochastic differential equation (BSDE) whose terminal instead initial condition should be prescribed. Two classes of BMFLQG games are discussed here and their decentralized strategies are derived through the consistency condition. In the first class, the individual agents of large-population system are weakly coupled in their state dynamics and the full information can be accessible to all agents. In the second class, the coupling structure lies in the cost functional with only partial information structure. In both classes, the asymptotic near-optimality property (namely, $\epsilon$- Nash equilibrium) of decentralized strategies are verified. To this end, some estimates to BSDE, are presented in the large-population setting. [ABSTRACT FROM AUTHOR]

Effective hashing for large-scale image databases is a popular research area, attracting much attention in computer vision and visual information retrieval. Several recent methods attempt to learn either graph embedding or semantic coding for fast and accurate applications. In this paper, a novel unsupervised framework, termed evolutionary compact embedding (ECE), is introduced to automatically learn the task-specific binary hash codes. It can be regarded as an optimization algorithm that combines the genetic programming (GP) and a boosting trick. In our architecture, each bit of ECE is iteratively computed using a weak binary classification function, which is generated through GP evolving by jointly minimizing its empirical risk with the AdaBoost strategy on a training set. We address this as greedy optimization by embedding high-dimensional data points into a similarity-preserved Hamming space with a low dimension. We systematically evaluate ECE on two data sets, SIFT 1M and GIST 1M, showing the effectiveness and the accuracy of our method for a large-scale similarity search. [ABSTRACT FROM PUBLISHER]

In this paper, an optimized event triggering scheme and an LQG controller design is proposed for a linearized wind turbine model in a microgrid system. The system is modeled as an event triggered system based on asynchronous-sampled data system (ASDS) approach. Networked induced delays are considered in the communication channel. Particle Swarm Optimization (PSO) is employed to optimize control and communication cost, while reducing the event rate. Simulation results are included to elucidate the proposed controller design. A comparative analysis is also presented to demonstrate the significance of optimization in event sampling. [ABSTRACT FROM AUTHOR]

This paper considers decentralized control and optimization methodologies for large populations of systems, consisting of several agents with different individual behaviors, constraints and interests, and influenced by the aggregate behavior of the overall population. For such large-scale systems, the theory of aggregative and mean field games has been established and successfully applied in various scientific disciplines. While the existing literature addresses the case of unconstrained agents, we formulate deterministic mean field control problems in the presence of heterogeneous convex constraints for the individual agents, for instance arising from agents with linear dynamics subject to convex state and control constraints. We propose several model-free feedback iterations to compute in a decentralized fashion a mean field Nash equilibrium in the limit of infinite population size. We apply our methods to the constrained linear quadratic deterministic mean field control problem. [ABSTRACT FROM PUBLISHER]

An accurate and noninvasive stress assessment from human physiology is a strenuous task. In this paper, a pattern recognition system to learn complex correlates between heart rate variability (HRV) features and salivary stress biomarkers is proposed. Using the Trier social stress test, heart rate and salivary measurements were obtained from volunteers under varying levels of stress induction. Measurements of salivary alpha-amylase and cortisol were used as objective measures of stress, and were correlated with the HRV features using fuzzy ARTMAP (FAM). In improving the predictive ability of the ARTMAPs, techniques, such as genetic algorithms for parameter optimization and voting ensembles, were employed. The ensemble of FAMs can be used for predicting stress responses of salivary alpha-amylase or cortisol using heart rate measurements as the input. Using alpha-amylase as the stress indicator, the ensemble was able to classify stress from heart rate features with 75% accuracy, and 80% accuracy when cortisol was used. [ABSTRACT FROM AUTHOR]

The increasing liberalization of European electricity markets, the growing proportion of intermittent renewable energy being fed into the energy grids, and also new challenges in the patterns of energy consumption (such as electric mobility) require flexible and intelligent power grids capable of providing efficient, reliable, economical, and sustainable energy production and distribution. From the supplier side, particularly, the integration of renewable energy sources (e.g., wind and solar) into the grid imposes an engineering and economic challenge because of the limited ability to control and dispatch these energy sources due to their intermittent characteristics. Time-series prediction of wind speed for wind power production is a particularly important and challenging task, wherein prediction intervals (PIs) are preferable results of the prediction, rather than point estimates, because they provide information on the confidence in the prediction. In this paper, two different machine learning approaches to assess PIs of time-series predictions are considered and compared: 1) multilayer perceptron neural networks trained with a multiobjective genetic algorithm and 2) extreme learning machines combined with the nearest neighbors approach. The proposed approaches are applied for short-term wind speed prediction from a real data set of hourly wind speed measurements for the region of Regina in Saskatchewan, Canada. Both approaches demonstrate good prediction precision and provide complementary advantages with respect to different evaluation criteria. [ABSTRACT FROM PUBLISHER]

Purpose - The purpose of this paper is to construct diverse granules and coverings derived from a semi-equivalence class and then to reveal the relationships between granules and coverings and even relations, and to help one to study pansystems relations in a trans-cross view. Design/methodology/approach - Forms regarding semi-equivalence classes as primitives, blocks or granules such as tolerance class, join class, meet class, optimist/pessimist selected compatible class, and tolerantly kernel class in an incomplete information system are defined and compared. Different approximations are also introduced. Furthermore, diverse coverings are also suggested. Findings - A tolerance class of an object is proved to be the join of semi-equivalence classes containing it, i.e. a result of acting union operation on some primitive classes. A compatibly kernel class of an object is a meet of semi-equivalence classes including it, i.e. a result of acting intersection operation on some primitive classes. Related coverings can also be regarded as coverings derived from the covering consisting of primitive granules. Several necessary and/or sufficient conditions for a general covering to become a semi-equivalence or complete covering are obtained. Meaningful property and relationship results are also exploited. Practical implications - Constructing diverse granules naturally from an incomplete information system to form a different knowledge expression system looks promising for data mining in the information society. It widens the approach and schema. Originality/value - The paper shows that the formation process of granules is natural, newly defined, and not similar and theoretic to those existing in a neighborhood system. The relationship between diverse granules and coverings is described by mathematical theorems in sufficient or necessary condition form. [ABSTRACT FROM AUTHOR]

Purpose - This paper seeks to argue that sociology is in need of reconstruction on a theoretical and conceptual foundation of cybernetics, specifically, managerial cybernetics and to show how this hitherto unsuccessful task might be brought about. Design/methodology/approach - The approach is one of a rigorous and deep querying of the reasons for the lack of successful fit heretofore between sociology and cybernetics. By taking a critical, historical and philosophical approach to the development of sciences it opens possibilities for the reconstruction of sociology as a "new science" based on the foundation of cybernetics, specifically the managerial cybernetics of Stafford Beer. Findings - The work argues that the appropriate conceptual foundation for the social sciences is the realm of communication and control, ideas that were given a rigorous formulation in cybernetics, information theory and systems thinking since the 1940s. Many people have seen the prima facie appropriateness of these ideas for the study of human society and numerous attempts have been made to apply them. Almost, all of these efforts have been failures, at least from a sociological point of view. The paper suggests that the problem with all such previous attempts is that they consisted of too direct an application of cybernetics to sociology, entailing a metaphoric reduction that threatened the intellectual integrity of the discipline. Work in the history of sciences suggests that, whereas deep theoretical, foundational work may well be achieved for a realm in the abstract, so to speak, it is when attempts are made to apply these results to more phenomenal domains, to which in principle they are deemed appropriate and relevant, that problems of an apparent "lack of fit" arise. It has been found that a group of intermediating concepts is necessary to draw the two domains together in a workable fit. This process has been called "finalization of science". Practical implications - Of immediate value is the reconstruction of sociology as a cybernetically informed science of society that actually delivers theoretical, analytical, research and practical results. Originality/value - The paper represents a highly original synthesis drawn from the history and philosophy of science development to yield immediate and useful results. [ABSTRACT FROM AUTHOR]

Collaboration enables weak species to survive in an environment where different species compete for limited resources. Cooperative coevolution (CC) is a nature-inspired optimization method that divides a problem into subcomponents and evolves them while genetically isolating them. Problem decomposition is an important aspect in using CC for neuroevolution. CC employs different problem decomposition methods to decompose the neural network training problem into subcomponents. Different problem decomposition methods have features that are helpful at different stages in the evolutionary process. Adaptation, collaboration, and competition are needed for CC, as multiple subpopulations are used to represent the problem. It is important to add collaboration and competition in CC. This paper presents a competitive CC method for training recurrent neural networks for chaotic time-series prediction. Two different instances of the competitive method are proposed that employs different problem decomposition methods to enforce island-based competition. The results show improvement in the performance of the proposed methods in most cases when compared with standalone CC and other methods from the literature. [ABSTRACT FROM PUBLISHER]

In 1960, Science published a paper by Heinz von Foerster predicting that on Friday, 13 November 2026, the “human population will approach infinity if it grows as it has grown in the last two millennia.” Just a few years after this preposterous doomsday alarm, the annual growth of global population peaked at about 2.1 percent and immediately began to decline. By 2020 the growth rate stood at just a bit more than 1 percent, the result of the steadily declining total fertility rate (TFR), the number of children born to a woman during her reproductive period. [ABSTRACT FROM AUTHOR]