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Evolutionary studies of cultural complexity often assume that group members select the best information available in the group, effectively diffusing the best innovations, whose advantages are subsequently passed on to the next generation. This would seem to describe the ideal of the scientific process — each cohort of papers in a field surfacing the best innovations, refining them and passing on to the next "layer" or cohort of scientific works. Here, we use academic journal databases to explore this "forking" (branching) process in the evolution of a scientific paradigm. We apply citation network visualization and Latent Dirichlet allocation topic analysis to three different paradigms defined pragmatically as the set of papers citing a highly influential paper in each respective case. Our three case studies indicate a founder effect in how the seminal paper is highly-embedded in the citation network, and yet peripheral to the evolution of topics in subsequent "layers" of publications within the paradigm. This and additional evidence suggest certain topics are selected and followed, while others are left behind. From these case studies, we discuss how hitherto undeveloped ideas of the past might be located in the topic space of seminal works of the same fruitful time period. [ABSTRACT FROM AUTHOR]

This paper explores citation-based metrics, how they differ in ranking papers and authors, and why. We initially take as example three main metrics that we believe significant; the standard citation count, the more and more popular h-index, and a variation we propose of PageRank applied to papers (called PaperRank), that is appealing as it mirrors proven and successful algorithms for ranking web pages. As part of analyzing them, we develop generally applicable techniques and metrics for qualitatively and quantitatively analyzing indexes that evaluate content and people, as well as for understanding the causes of their different behaviors. Finally, we extend the analysis to other popular indexes, to show whether the choice of the index has a significant effect in how papers and authors are ranked. We put the techniques at work on a dataset of over 260 K ACM papers, and discovered that the difference in ranking results is indeed very significant (even when restricting to citation-based indexes), with half of the top-ranked papers differing in a typical 20-element long search result page for papers on a given topic, and with the top researcher being ranked differently over half of the times in an average job posting with 100 applicants. [ABSTRACT FROM AUTHOR]

In this paper we present a phenomenological approach to describe a complex systemcolon; scientific research impact through Citation Mining. The novel concept of Citation Mining, a combination of citation bibliometrics and text mining, is used for the phenomenological description. Citation Mining starts with a group of core papers whose impact is to be examined, retrieves the papers that cite these core papers, and then analyzes the bibliometrics characteristics of the citing papers as well as their linguistic and thematic characteristics. The Science Citation Index is used as the source database for the core of citing papers, since its citation-based structure enables the capability to perform citation studies easily. This paper presents illustrative examples in photovoltaics (applied research) and sandpile dynamics (basic research) to show the types of output products possible. Bibliometric profiling is performed over a number of the citing papers' record fields to offer different perspectives on the citing (user) community. Text mining is performed on the aggregate citing papers, to identify aggregate citing community themes, and to identify extra-discipline and applications themes. The photovoltaics applied research papers had of the order of hundreds of citations in aggregate. All of the citing papers ranged from applied research to applications, and their main themes were fully aligned with those of the aggregate cited papers. This seems to be the typical case with applied research. The sandpile dynamics basic research papers had hundreds of citations in aggregate. Most of the citing papers were also basic research whose main themes were aligned with those of the cited paper. This is typically the case with basic research. However, about twenty percent of the citing papers were research or development in other disciplines, or development within the same discipline. In addition, there was a four year lag time between publication and citation by the... [ABSTRACT FROM AUTHOR]

Understanding the peer review process could help research and shed light on the mechanisms that underlie crowdsourcing. In this paper, we present an agent-based model of peer review built on three entities - the paper, the scientist and the conference. The system is implemented on a BDI platform (Jason) that allows to define a rich model of scoring, evaluating and selecting papers for conferences. Then, we propose a programme committee update mechanism based on disagreement control that is able to remove reviewers applying a strategy aimed to prevent papers better than their own to be accepted ("rational cheating"). We analyze a homogeneous scenario, where all conferences aim to the same level of quality, and a heterogeneous scenario, in which conferences request different qualities, showing how this affects the update mechanism proposed. We also present a first step toward an empirical validation of our model that compares the amount of disagreements found in real conferences with that obtained in our simulations. [ABSTRACT FROM AUTHOR]

The central subject of this paper is the relation between the macro-level of the Society and the micro-level of its individuals. In complex real-life systems, this relationship is not trivial and even the result at the macro-level can be counter-intuitive. One of the first models that highlights this is the Sakoda and Schelling's model of spatial segregation where a person uses a selfish rule to decide whether or not to leave his current place; on the basis of the ratio of "compatible" agents in the neighborhood of each individual, simulations have shown that tolerant agents group together beyond what their own tolerance imposes. This paper compares the macro-behavior arising from either selfish or altruist micro-motives; for this purpose, we propose agent-based computational models based, respectively, on selfishness, altruism and a mixture of both. Computational simulations show that (i) in a totally altruistic population, a vast majority becomes satisfied as if the agents were themselves selfish and end up spatially segregated; (ii) altruist micro-motives allow to reduce both the micro-macro gap and the segregation phenomenon; (iii) in a mixed population, the macro-effect depends both on the respective weight of each micro-motives and the allocation strategy. [ABSTRACT FROM AUTHOR]

To discern the role social and cultural networks play in the emergence of preeminent historical figures and ideas in History, we use a method based on complex networks analysis to reveal emergent interactions in Wikipedia. We built a network constituted by derivative links, where nodes are connected if they are co-linked by other papers or co-link other papers within Wikipedia. We apply this method, focused on the structural distance, to three significant individuals associated with the Italian Renaissance: Copernicus, Michelangelo, and Pico della Mirandola. The results point to the effectiveness of this approach for discovering new knowledge about the interdisciplinary transactions between people and ideas coming from artistic, scientific and philosophical domains during this period. The emergent network reflects the apparently strong network-level interactions between Michelangelo and Mirandola's clusters; the importance of Hermeticism across the three clusters; and how the so-called "knowledge dealers" related to Neoplatonism contribute to the depiction of the period by future historians. Finally, we advance the notion of "focus reading", in which complex networks analysis allows us to build bridges between close and distant forms of reading historical evidence. [ABSTRACT FROM AUTHOR]

This paper further investigates the Talent versus Luck (TvL) model described by [Pluchino et al. Talent versus luck: The role of randomness in success and failure, Adv. Complex Syst.21 (2018) 1850014] which models the relationship between 'talent' and 'luck' on the impact of an individuals career. It is shown that the model is very sensitive to both random sampling and the choice of value for the input parameters. Running the model repeatedly with the same set of input parameters gives a range of output values of over 50% of the mean value. The sensitivity of the inputs of the model is analyzed using a variance-based approach based upon generating Sobol sequences of quasi-random numbers. When using the model to look at the talent associated with an individual who has the maximum capital over a model run it has been shown that the choice for the standard deviation of the talent distribution contributes to 67% of the model variability. When investigating the maximum amount of capital returned by the model the probability of a lucky event at any given epoch has the largest impact on the model, almost three times more than any other individual parameter. Consequently, during the analysis of the model results one must keep in mind the impact that only small changes in the input parameters can have on the model output. [ABSTRACT FROM AUTHOR]

This paper studies the basins of attraction of fully asynchronous cellular automata (ACAs) that converge to point attractor. Unlike traditional (synchronous) cellular automata (CAs), an ACA may approach to different point attractors from the same initial condition for different runs. In this direction, this paper introduces the notion of cloud behavior in convergence dynamics of ACAs. Depending on the cloud behavior, we classify the convergent ACAs into three classes—eccentric cloud, partially eccentric cloud and deterministic cloud system. In addition, this study observes the computational ability of addressing density classification problem (i.e. distributed consensus problem) of this asynchronous cloud system. [ABSTRACT FROM AUTHOR]

Science, Technology, Engineering and Mathematics (STEM) disciplines are notorious for gender imbalance favouring men. From these, Computer Science appears to among the worst. The imbalance comes despite the fact that, the proportion of women publishing impactful papers has increased over the last couple of decades. Here, we explore the potential gender differences in collaboration dynamics of leading academics in Computer Science and contrast it with other researchers in the same discipline. This work aims to demonstrate that productivity patterns differ between men and women; understanding such patterns is fundamental to improving the current situation by driving well-thought interventions. We start by looking at topological features of academics ego-networks and their variability over their career lifespan; we found that women have a higher rate of repeated collaborators, while men have slightly more new collaborators. Second, we show gender differences in the acquisition patterns and maintenance of collaborations over the researchers' careers. Third, we look at alters' gender composition and gender role in introducing new collaborations to researchers and found that women are more homophilic than men and that the gender of the person being introduced (alter) tends to match the gender of the ego (the researcher). Last, we show that women reach their highest productivity levels earlier in their careers than men, but the levels attained are not at the same magnitude as men. Our work highlights the importance of studying gender differences by productivity levels, the role of gender-mixed groups in academia and the relation to academic production within these groups. This work contributes to a better understanding of gender roles in Computer Science and points to ways that can help reduce possible gaps. [ABSTRACT FROM AUTHOR]

Social systems are characterized by the presence of group interactions and by the existence of both trust and distrust relations. Although there is a wide literature on signed social networks, where positive signs associated to the links indicate trust, friendship, agreement, while negative signs represent distrust, antagonism, and disagreement, very little is known about the effect that signed interactions can have on the spreading of social behaviors when, not only pairwise, but also higher-order interactions are taken into account. In this paper, we introduce a model of complex contagion on signed simplicial complexes, and we investigate the role played by trust and distrust on the dynamics of a social contagion process, where exposure to multiple sources is needed for the contagion to occur. The presence of higher-order signed structures in our model naturally induces new infection and recovery mechanisms, thus increasing the richness of the contagion dynamics. Through numerical simulations and analytical results in the mean-field approximation, we show how distrust determines the way the system moves from a state where no individuals adopt the social behavior, to a state where a finite fraction of the population actively spreads it. Interestingly, we observe that the fraction of spreading individuals displays a non-monotonic dependence with respect to the average number of connections between individuals. We then investigate how social balance affects social contagion, finding that balanced triads have an ambivalent impact on the spreading process, either promoting or impeding contagion based on the relative abundance of fully trusted relations. Our results shed light on the nontrivial effect of trust on the spreading of social behaviors in systems with group interactions, paving the way to further investigations of spreading phenomena in structured populations. [ABSTRACT FROM AUTHOR]

Wikipedia is a widely used tool people use to gather knowledge about the world, causing it to have a vast impact on the way individuals perceive the reality they live in. It is then of paramount importance that the picture of the world Wikipedia provides is accurate. We cannot afford such an important tool to eschew inclusiveness or a fair representation of reality: an inaccurate picture of the world in such a tool can be used to claim unjust and unfair positions — such as that women are inferior to men — as if they were facts, because they are enshrined on an encyclopedia. In this paper, we study issues of fair gender representations for people in history noted by multiple language editions of Wikipedia: are women underrepresented on Wikipedia? We do so via a combination of natural language processing and network science. Our results indicate that there is indeed a higher bar for women to have their own biographical page on Wikipedia: women are only included when they have more significant connections than men to the rest of the network. There are visible effects of the initiatives Wikipedia is taking to fix this issue, showing that the gap is narrowing, which validates our interpretation of the data. [ABSTRACT FROM AUTHOR]

This paper explores memory mechanisms in complex socio-technical systems, using a mobility demand model as an example case. We simplify a large-scale agent-based mobility model, formulate the corresponding stochastic process, and observe that the mobility decision process is non-Markovian. This is due to its dependence on the system's history, including social structure and local infrastructure, which evolve based on prior mobility decisions. Complementing the mobility process with two history-determined components leads to an extended mobility process that is Markovian. Although our model is a very much reduced version of the original one, it remains too complex for the application of usual analytic methods. Instead, we employ simulations to examine the functionalities of the two history-determined components. We think that the structure of the analyzed stochastic process is exemplary for many socio-technical, -economic, -ecological systems. Additionally, it exhibits analogies with the framework of extended evolution, which has previously been used to study cultural evolution. [ABSTRACT FROM AUTHOR]

In many engineered and natural networked systems, there has been great interest in leader selection and/or edge assignment during the optimal design of controllable networks. In this paper, we present our pioneering work in leader–follower network design via memetic algorithms, which focuses on minimizing the number of leaders or the amount of control energy while ensuring network controllability. We consider three problems in this paper: (1) selecting the minimum number of leaders in a pre-defined network with guaranteed network controllability; (2) selecting the leaders in a pre-defined network with the minimum control energy; and (3) assigning edges (interactions) between nodes to form a controllable leader–follower network with the minimum control energy. The proposed framework can be applied in designing signed, unsigned, directed, or undirected networks. It should be noted that this work is the first to apply memetic algorithms in the design of controllable networks. We chose memetic algorithms because they have been shown to be more efficient and more effective than the standard genetic algorithms in solving some optimization problems. Our simulation results provide an additional demonstration of their efficiency and effectiveness. [ABSTRACT FROM AUTHOR]

In this paper, we consider a system in which knowledge in a dispersed form is available. In the system local classifiers are combined into coalitions. Two methods of combining classifiers in coalitions are discussed in this paper — with a hierarchical agglomeration algorithm and with Pawlak's conflict model. The purpose of this paper is to apply methods for reducing dimensionality in these two approaches. Two methods of attribute reduction are considered — based on the rough set theory and based on attribute correlation with decision class. The most important conclusions formulated in the paper are as follows. The use of attribute selection method improves the quality of classification of the dispersed system. Better results are generated by the system with a hierarchical agglomeration algorithm. [ABSTRACT FROM AUTHOR]

This paper examines the impact of countercyclical capital buffers (CCyBs) on financial stability and contributes to the discussion of CCyBs in an agent-based framework. The main focus of this work lies upon the question whether the implementation of CCyBs according to the Basel III framework improves financial stability unconditionally, or at the expense of unintended side effects. For this purpose, we extend the agent-based model of [Riccetti, L., Russo, A. and Gallegati, M., Firm–bank credit network, business cycle and macroprudential policy, J. Econ. Interact. Coord. (2021)] to investigate the effect of credit-to-GDP gap-based CCyBs in general as well as the effects of varying the smoothing parameter lambda λ of the Hodrick–Prescott (HP) filter within the method of calculating CCyBs. To the best of our knowledge, this study is the first one that investigates systematically how the method of calculating credit-to-GDP gap-based CCyBs may affect macrofinancial dynamics and stability in such an agent-based framework. [ABSTRACT FROM AUTHOR]

This paper proposes a macroeconometric analysis to depict and measure possible financial cycles that emerge due to the dynamic interaction between heterogeneous market participants. We consider two-type heterogeneous speculative agents: Trend followers tend to follow the price trend while contrarians go against the wind. As agents' beliefs are unobserved variables, we construct a state-space model where heuristics are considered as unobserved state components and from which the conditions for endogenous cycles can be mathematically derived and empirically tested. Further, we specifically measure the length of endogenous financial cycles. The model is estimated using the equity price index for the 1960–2020 period for the UK, France, Germany, Italy, Ireland, and the USA. We find empirical evidence of endogenous financial cycles for all countries, with the highest frequencies in the USA and the UK. [ABSTRACT FROM AUTHOR]

Artificial Intelligence and associated technologies are rapidly automating routine and non-routine tasks across industries and can severely disrupt labor markets. This paper presents an agent-based, evolutionary model of labor market dynamics where workers adjust to technology shocks induced by automation. Firms produce a homogeneous service by combining the outputs of tasks performed by workers while stochastically adapting to automation of tasks causing displacement of workers. We develop a model that includes: (i) the description of occupation mobility as a directed graph where nodes represent occupations, and the directed edges represent the mobility pathways along which displaced workers can get retrained and redeployed (ii) explicit microfoundations of the processes of job matching and wage setting between firms and heterogeneously skilled workers. The model focuses on the influence of workers' retraining choices on the employment levels and wage inequality in the labor market. Simulation results indicate distinct tipping points for unemployment and wage inequality with changes in mobility pathways along which workers retrain and redeploy across occupations. An increase in the density of mobility pathways induces a reinforcement effect on employment. Retraining displaced workers without building dense and well-distributed mobility pathways across occupations could widen wage inequality due to excessive crowding of workers around specific tasks. Our work focuses on the finance and insurance industry dataset, where we observe that the reskilling of displaced workers along occupation mobility pathways assisted by a lower retraining cost improves the unemployment levels. Also, if the firms aggressively automate their tasks, an increase in the cost of retraining increases inequality of wages in the labor market. [ABSTRACT FROM AUTHOR]

This paper considers patterns of organization in distributions of the rate of return on capital (RoC) realized by individual enterprises. It shows that large-sample cross sections of RoC across several European economies are persistently well described by the same functional form: Sharply peaked distributions with stretched-exponential tails. To account for this observation, the paper develops a systemic model of the competitive regulation of profitability by the pursuit of arbitrage profits latent in any heterogeneity across values of RoC. Under the model, the observed distributional forms embody two simple, emergent results of capital-market competition: The competitive maximization of aggregate arbitrage profits; and the endogenous formation of the cost of capital from individual measures of RoC, in the presence of dynamic entrepreneurial and monopolistic quasi-rents. The paper's discussion defines a series of new, observable macroscopic measures of competitive performance in decentralized market economies. It also points to the aptness of understanding prices as parts of structures of generalized Marx–Sraffa "prices of production," predicated on the characteristics of capital-market statistical equilibria; to a general theoretical approach to the regulation of certain economic quantities by arbitrage; and to the role the costs of informational gains play in shaping observable outcomes in the operation of certain types of goal-seeking, self-organizing systems. [ABSTRACT FROM AUTHOR]

The inability of investors and academics to consistently predict, and understand the behavior of financial markets has forced the search for alternative analytical frameworks. Analyzing financial markets as complex systems is a framework that has demonstrated great promises, with the use of agent-based models (ABMs) and the inclusion of network science playing an important role in increasing the relevance of the framework. Using an artificial stock market created via an ABM, this paper provides a significant insight into the mechanisms that drive the returns in financial markets, including periods of elevated prices and excess volatility. The paper demonstrates that the network topology that investors form and the dividend policy of firms significantly affect the behavior of the market. However, if investors have a bias to following their neighbors then the topology becomes redundant. By successfully addressing these issues this paper helps refine and shape a variety of additional research tasks for the use of ABMs in uncovering the dynamics of financial markets. [ABSTRACT FROM AUTHOR]

The idea of discovering a few nodes with potential to impact an entire network, is known as Influence Maximization (IM) and has many real-world applications which make it one of well-studied research problems in the domain of network analysis. IM typically requires a fixed criteria of budget (number of influential nodes to be identified) as input. The fundamental premise of this research is that the budget is not the sole criteria for real-world applications. This study challenges the conventional method to identify influential nodes, and proves that it requires specification of the stoppage criteria and the model used to quantify influence. We analyze the complex interplay of various criteria that can be used to solve IM problem, and prove that changing the criterion also changes the algorithm determined as the top performer. A number of criteria are presented in this paper apart from budget, such as the spread achieved by the algorithm (in terms of number of nodes influenced) and absolute time. The proposed IMine framework provides an interface to apply influence problem on various stoppage criteria, while also providing customization option to change the model of quantifying influence spread. [ABSTRACT FROM AUTHOR]

Due to structural heterogeneity, the main function and structure of networked systems are significantly influenced by some important nodes rather than each member. In practical, the properties of important nodes could be different from network to network, and thus a variety of algorithms have been specially designed to identify important nodes of different networks and of different dynamics. In this paper we propose a widely applicable algorithm by employing the percolation model in statistical physics, which describes the behavior of connected clusters when nodes are connected randomly. This algorithm appropriately combines the local and global properties of a network, thus it stresses the significance of nodes that neither have a visibly local importance, such as degree and clustering, nor have a visibly global importance, such as betweenness. The effectiveness of our algorithm has been illustrated in a series of networks, including model networks with different degree distributions and different degree correlations, and empirical networks. As a shell decomposition process, the framework of our algorithm has extensive application prospects in analyzing network structure, such as community, core–periphery structure, and shell structures. [ABSTRACT FROM AUTHOR]

A recent highly publicized study [Park, M., Leahey, E. and Funk, R. J., Papers and patents are becoming less disruptive over time, Nature613 (2023) 138–144] claiming that science has become less disruptive over recent decades represents an extraordinary achievement but with deceptive results. The measure of disruption, CD5, in this study does not account for differences in citation amid decades of exponential growth in publication rate. In order to account for both the exponential growth as well as the differential impact of research works over time, here we apply a weighted disruption index to the same dataset. We find that, among research papers in the dataset, this weighted disruption index has been close to its expected neutral value over the last fifty years and has even increased modestly since 2000. We also show how the proportional decrease in unique words is expected in an exponentially growing corpus. Finding little evidence for recent decrease in disruption, we suggest that it is actually increasing. Future research should investigate improved definitions of disruption. [ABSTRACT FROM AUTHOR]

This paper studies a model for describing opinions' evolution in a community linked by microblogging directed network systems. The network can evolve in time by means of the creation and the deletion of some connections. When a connexion is created, the individuals have access to the whole history of posts written by the corresponding author and, when a connexion is destroyed, all the posts written by the corresponding author become invisible. The agents' opinions are described by a set of continuous opinion variables in the closed interval Ω = [ − 1 , 1 ]. They represent the agreement (or disagreement) of the corresponding agent with respect to a binary question (such as a referendum or an election with two candidates). The model takes into account the effects on public opinion caused by the sign and the intensity of the initial opinions of the agents, their activity in microblogging platforms the presence of leaders and the possible manipulations of the visibility of the posts by the microblogging platform owner. The model is written as a system of integro-differential equations and simulated by using a Runge–Kutta method. We show that hidden manipulation can have an important impact on the public opinion formation and that very mild interventions of the network owner may induce major effects on the population. In our simulations, the effect of hidden manipulation is shown to be more efficient in driving the public opinion than the action of a leader, in the case of bounded confidence models and for short-time intervals. [ABSTRACT FROM AUTHOR]

We analyze a science collaboration network, i.e. a network whose nodes are scientists with edges connecting them for each paper published together. Furthermore, we develop a model for the simulation of discontiguous small-world networks that shows good coherence with empirical data. [ABSTRACT FROM AUTHOR]

Recent studies on international networks have investigated the role of global cities as catalysts of knowledge flows. In this paper, we examine global cities in transnational networks of innovators. To assess the likelihood of scientists and inventors from different cities to take part in international teams, we propose a measure based on 3-hyperedges in the collaboration network constructed from scientific publications and patents. This measure is used to identify the most competitive global cities in the international network of innovators. To this end, we construct a null model based on the hypergeometric ensembles of random graphs. We find that five US cities play a leading role in transnational networks of innovators. Among them, San Francisco stands out as the most globalized city. [ABSTRACT FROM AUTHOR]

System structure can affect or decide the system function. Many pioneers have analyzed the impact of system's macro-statistical characteristics, such as degree distribution and giant component, on system performance. But only few research works were conducted on the relation of mesoscopic structure and agent property with system task performance. In this paper, we designed a scenario that, in a multiagent system, agents will try their best to form a qualified team to fulfill more system tasks under the requirements from agent property, structure and task. The theoretical and simulation results show that the agent link network, agent properties and task requirement will co-affect the dynamic team formation and at last have serious effects on a system's task completion ratio and performance. Some factors such as network density and task introduction period have positive influence. Task execution time and team size have negative influence. Some factors show a counter-intuitive influence. The clustering coefficient has not much influence as people expected and the task publicity time isn't bigger the better. Notably, system performance is affected by the coupling effect, instead of the independent effects of all factors. The effect of system structure on system function conditionally relies on the support from agent ability and task requirement. [ABSTRACT FROM AUTHOR]

We recently introduced the concept of dynamical score network to represent the harmonic progressions in any composition. Through a process of chord slicing, we obtain a representation of the score as a complex network, where every chord is a node and each progression (voice leading) links successive chords. In this paper, we use this representation to extract quantitative information about harmonic complexity from the analysis of the topology of these networks using state-of-the-art statistical mechanics techniques. Since complex networks support the communication of information by encoding the structure of allowed messages, we can quantify the information associated with locating specific addresses through the measure of the entropy of such network. In doing so, we then characterize properties of network topology, such as the degree distribution of a graph or the shortest paths between couples of nodes. Here, we report on two different evaluations of network entropy, diffusion entropy analysis (DEA) and the Kullback–Leibler divergence applied to the conditional degree matrix, and the measurements of complexity they provide, when applied to an extensive corpus of scores spanning 500 years of western classical music. Although the analysis is limited in scope, our results already provide quantitative evidence of an increase of such measures of harmonic complexity over the corpora we have analyzed. [ABSTRACT FROM AUTHOR]

The task of extracting and analyzing character networks from works of fiction, such as novels and movies, has been the object of a number of recent publications. However, only a very few of them focus on graphic novels, and even fewer on European graphic novels. In this paper, we focus on Thorgal, a bande dessinée, i.e. a comic of the French-Belgian tradition. We manually annotate all the volumes of this series, in order to constitute a corpus allowing us to extract its character network. We perform a descriptive analysis of the network structure and compare it to real-world and fictional social networks. We also study the effect of character filtering over the network structure. Finally, we leverage complex network analysis tools to answer two research questions from the literature, related to the similarity between Thorgal and the Saga of Icelanders; and to the position of women in the series. Our data and source code are both publicly available online. [ABSTRACT FROM AUTHOR]

This paper examines the evolution of agent strategies in a commons dilemma using a tag interaction model. Using a tag-mediated interaction model, individual's can determine their interactions based on their tag similarity. The experimental results show the significance of agent strategies that contribute to the overall value of the commons. The paper also examines the significance of viscosity on the emergence of cooperation in the commons dilemma. Viscosity represents a specific form of openness that will be examined in the paper. An initial series of experiments demonstrates the role of tags in the n-player dilemma while subsequent experiments examine the impact of openness on the evolution of agent strategies. The paper shows the emergence of cooperation through tag-mediated interactions. Simulation results show the emergence of strategies that contribute heavily to the value of the shared commons. The role of viscosity on the population strategy set is examined and this is similar to some forms of openness in multi-agent environments. [ABSTRACT FROM AUTHOR]

In this paper, to reveal the influence of multilayer network structure on opinion diffusion in social networks, we study an opinion dynamics model based on DeGroot model on multilayer networks. We find that if the influence matrix integrating the information of connectedness for each layer and correlation between layers is strongly connected and aperiodic, all agents' opinions will reach a consensus. However, if there are stubborn agents in the networks, regular agents' opinions will finally be confined to the convex combinations of the stubborn agents'. Specifically, if all stubborn agents hold the same opinion, even if the agents only exist on a certain layer, their opinions will diffuse to the entire multilayer networks. This paper not only characterizes the influence of multilayer network topology and agent attribute on opinion diffusion in a holistic way, but also demonstrates the importance of coupling agents which play an indispensable role in some social and economic situations. [ABSTRACT FROM AUTHOR]

This special journal issue of Advances in Complex Systems presents a collection of papers describing current research delivered by recognized researchers actively working in various corners of the field of modeling of complex systems by cellular automata and related methods. All included papers are self-contained and present the latest developments in the areas where the authors work. Hence, all papers can be read independently, but it is strongly recommended to study the issue as a whole to get a general overview of the various methods and techniques from the field. The main aim of this special issue is to provide researchers from neighboring fields with sufficient information and vital examples of how to design models in complex systems. The whole issue is organized in such a way that common features occurring repeatedly in most models of complex systems are highlighted. [ABSTRACT FROM AUTHOR]

Cellular automaton models of complex systems (CSs) are gaining greater popularity; simultaneously, they have proven the capability to solve real scientific and engineering applications. To enable everybody a quick penetration into the core of this type of modeling, three real applications of cellular automaton models, including selected open source software codes, are studied: laser dynamics, dynamic recrystallization (DRX) and surface catalytic reactions. The paper is written in a way that it enables any researcher to reach the cutting edge knowledge of the design principles of cellular automata (CA) models of the observed phenomena in any scientific field. The whole sequence of design steps is demonstrated: definition of the model using topology and local (transition) rule of a cellular automaton, achieved results, comparison to real experiments, calibration, pathological observations, flow diagrams, software, and discussions. Additionally, the whole paper demonstrates the extreme expressiveness and flexibility of massively parallel computational approaches compared to other computational approaches. The paper consists of the introductory parts that are explaining CSs, self-organization and emergence, entropy, and CA. This allows readers to realize that there is a large variability in definitions and solutions of this class of models. [ABSTRACT FROM AUTHOR]

Theoretical and computational frameworks of complexity science are dominated by binary structures. This binary bias, seen in the ubiquity of pair-wise networks and formal binary operations in mathematical models, limits our capacity to faithfully capture irreducible polyadic interactions in higher-order systems. A paradigmatic example of a higher-order interaction is the Borromean link of three interlocking rings. In this paper, we propose a mathematical framework via hypergraphs and hypermatrix algebras that allows to formalize such forms of higher-order bonding and connectivity in a parsimonious way. Our framework builds on and extends current techniques in higher-order networks — still mostly rooted in binary structures such as adjacency matrices — and incorporates recent developments in higher-arity structures to articulate the compositional behavior of adjacency hypermatrices. Irreducible higher-order interactions turn out to be a widespread occurrence across natural sciences and socio-cultural knowledge representation. We demonstrate this by reviewing recent results in computer science, physics, chemistry, biology, ecology, social science, and cultural analysis through the conceptual lens of irreducible higher-order interactions. We further speculate that the general phenomenon of emergence in complex systems may be characterized by spatio-temporal discrepancies of interaction arity. [ABSTRACT FROM AUTHOR]

Multiplex networks have proven to be valuable tools for modeling and analyzing real complex system. Extensive work has been done on the traffic dynamics on multiplex networks, but there remains a lack of sufficient attention towards studying routing strategies for the purpose of suppressing epidemic spreading. In this paper, the impact of global awareness routing (GAR), improved global awareness routing (IGAR), and improved active routing (IAR) strategies on traffic-driven epidemic spreading are investigated. Our findings indicate that in the case of infinite node-delivery capacity and no traffic congestion in the network, adjusting routing parameters can effectively suppress epidemic spreading. In this context, these three strategies show better abilities on the multiplex network built by WS or ER model to minimize the density of infected nodes, thus contributing to the overall inhibition of the epidemic spread. However, in the multiplex network constructed by BA model, GAR strategy has a promoting effect on epidemic spreading compared with the shortest routing strategy. In addition, by controlling traffic flow, limiting node delivery capabilities can contain outbreaks. Our results suggest that adopting appropriate routing strategies in multiplex networks can play a proactive role in controlling epidemic spreading. This is crucial for formulating effective prevention and control measures and improving public health security. [ABSTRACT FROM AUTHOR]

Empirical studies have consistently demonstrated the presence of a core–periphery structure within social network communities. Nevertheless, a formal model and comprehensive analysis to fully understand the structural characteristics of these communities are still lacking. This paper seeks to characterize these properties, focusing on agents' interconnections and their allocation of rates. Employing a game-theoretic approach, our analysis unveils several novel insights. First, we show that periphery agents not only follow core agents but also other periphery agents who share similar primary interests. Second, our results illuminate the emergence of core–periphery communities, revealing the conditions under which they form, and how they form. [ABSTRACT FROM AUTHOR]

In a globalized world, the distribution of value-added across firms depends on a number of factors that vary across space. A key factor is related to the different types of competition on the multilayer structure of production, which are characterized by different types of (market) power. In this paper, we first argue that PageRank centrality is able to capture sectoral market power within the complex structure of global production. We then study the empirical properties of this market power measure and demonstrate a power-law relationship between sectoral PageRank centrality and relative sectoral profits. This power-law relationship has (international) political economy implications as it demonstrates the high incentives of sectors to become more central to increase their relative profits. [ABSTRACT FROM AUTHOR]

Tax credit management system (TCMS) implemented by the State Administration of Taxation (SAT) of China in 2014 is an innovative mechanism of tax collection and management. This paper attempts to incorporate the TCMS into the model of Allingham and Sandmo [Allingham, M. G. and Sandmo, A., Income tax evasion: A theoretical analysis, J. Public Econ.1(3–4) (1972) 323–338], and then proposes an agent-based tax compliance model combined with EWA model. In this model, enterprises are divided into three categories: enterprises paying taxes for better credit, paying taxes to avoid a punishment, and not paying taxes. Enterprises can borrow from banks based on their own tax credit rating. The experimental results based on the model show that: (i) When the probability of spot check is low, the actual tax behavior of some enterprises is inconsistent with the results of tax credit publicized by tax authorities. (ii) In order to reduce the proportion of tax evasion, we try to change the parameter variables of integral reward, audit probability, punishment, financial subsidy and loan interest rate. The results show that the punishment of fixed high loan interest rate on tax evasion enterprises can effectively restrain tax evasion and increase the proportion of enterprises with high credit rating. [ABSTRACT FROM AUTHOR]

In this paper, we investigate how contagion risk is affected by bank activities in four types of interbank network structures, that is, random, small-world, scale-free and tiered networks. We vary the key parameters that define bank activities in the interbank market - including the size of interbank exposures, the size of liquid assets, the heterogeneity of the size of credit lending and the heterogeneity of banks - and analyze the impact of these parameters on contagion risk. First, we find that the size of interbank exposures is the main factor in determining the effect of contagion risk, that increases in the size of interbank exposures may lead to an increase in the threat of contagion risk, that after the size of interbank exposures rises beyond a threshold, the effect of contagion risk in small-world networks is the most significant, followed by that in tiered, random and scale-free networks, respectively. Second, increases in the size of liquid assets can decrease the effect of contagion risk. Third, the impact of the heterogeneity of the size of credit lending on contagion risk varies with interbank network structures. Finally, the effect of contagion risk among heterogeneous banks is stronger than that among homogeneous banks, and there is a positive relationship between the effect of contagion risk and the heterogeneity of banks. [ABSTRACT FROM AUTHOR]

We consider the fraction of nodes that default in large, stochastic, inhomogeneous financial networks following an initial shock to the system. Results for deterministic sequences of networks are generalized to stochastic networks to account for interbank lending relationships that change frequently. A general class of inhomogeneous stochastic networks is proposed for use in systemic risk research, and we illustrate how results that hold for Erdős–Rényi networks can be generalized to the proposed network class. The network structure of a system is determined by interbank lending behavior which may vary according to the relative sizes of the banks. We then use the results of the paper to illustrate how network structure influences the systemic risk inherent in large banking systems. [ABSTRACT FROM AUTHOR]

An analytical framework for the analysis of multi-hump solitons is proposed in this paper. Multi-hump solitons are defined by imposing special symmetry conditions on the classical soliton expression. Such soliton solutions have a wide range of potential applications in the field of optical communications. The proposed algebras of soliton solutions enable a new look at the propagation dynamics of complex nonlinear wave phenomena. The efficiency of the presented analytical scheme is demonstrated using a system of Riccati differential equations with diffusive and multiplicative coupling. [ABSTRACT FROM AUTHOR]

This paper develops an analytical model of contagion risk in banking systems with tiered structure. It explores the respective effects of banking network structure and bank activity on contagion risk in banking network evolution. The findings suggest that increasing interbank connections is conducive to handling banking crisis and reducing the effect of contagion risk, but its positive effect is limited; raising bank reserve ratio will enhance the stability of individual banks to a certain extent, but it may immediately lead to liquidity problems for banks that have less excess reserves, causing the occurrence of contagion risk; an excessive drive for risk assets with high return may bring high risk to banks and lead to instability of banking systems; the bank risk preference is crucial to the stability of banking systems, and the radicalness of it may lead to greater systemic instability. [ABSTRACT FROM AUTHOR]

This paper establishes a trade credit network with a guarantee mechanism. Based on the established network model, the trade credit network and the guarantee network are visualized. The characteristics of the distributions of both networks and the firm size distribution are then researched. Concurrently, through computer simulations, the risk contagion among firms is analyzed on the basis of the interactions between the trade credit network and the guarantee network. The results are as follows: (1) the degree of the trade credit network takes on a power-law distribution, as does the upper tail of firm size. (2) For the degree distribution of the guarantee network, the test result of the power-law features changes with a varying value of ϖ based on the test method of Clauset, [Shalizi and Newman Power-law distributions in empirical data, SIAM Rev.51 (2009) 661–703]. The change in ϖ directly affects the establishment of the guarantee links, thus affecting the structure of the guarantee network and its power-law features. (3) Finally, the introduction of a guarantee mechanism aggravates the extent of the risk contagion among firms. [ABSTRACT FROM AUTHOR]

The paper introduces a general sharing structure and presents sufficient and necessary conditions for the agents to approach by the dynamics of sharing an equal distribution of assets. For the special case of a ring structure with a uniform sharing rate, robustness is analyzed in case the rate does change during the process of sharing. The search for an equal distribution is similar to that for consensus in opinion dynamics and multiagent systems as a result of which tools from the latter are used in proving the results. [ABSTRACT FROM AUTHOR]

This is a reply to Ramon Ferrer-I-Cancho's paper in this issue "Some Word Order Biases from Limited Brain Resources: A Mathematical Approach." In this reply, I challenge the Euclidean distance model proposed in that paper by proposing a simple alternative model based on linear ordering. [ABSTRACT FROM AUTHOR]

Large crowds are challenging the comfort and safety level of big cities, while music may be a potential method to improve pedestrian flow. This paper focuses on the influence of different tempos and types of background music on pedestrian dynamics. Three tempos (90 beats/min (BPM), 120 BPM and 150 BPM) and two types (pure music and metronome stimuli) of music are considered. It is found that more frequent stop-and-go behaviors emerge with rhythms. Compared with that under a low tempo (90 BPM) of rhythm condition, stopping is more frequent with a high tempo one (120 BPM or 150 BPM). The number of stopping pedestrians per unit time increases 68.57%, 376.00%, 298.29%, 224.00%, 438.29% and 393.71% with 90 BPM, 120 BPM and 150 BPM music, 90 BPM, 120 BPM and 150 BPM metronome, compared with that without any rhythm, respectively. The velocity and flow are lower, and higher local densities appear with background music. The step frequency at high density with rhythms (1. 1 6 ± 0. 1 1 , 1. 1 6 ± 0. 1 2 and 1. 1 5 ± 0. 1 0 Hz for 90 BPM, 120 BPM and 150 BPM music; 1. 1 5 ± 0. 1 3 , 1. 1 1 ± 0. 0 9 and 1. 1 6 ± 0. 1 1 Hz for 90 BPM, 120 BPM and 150 BPM metronome) is lower than that without any rhythm (1. 4 0 ± 0. 1 4 Hz). Pedestrians need more time to avoid collisions and to step under background music conditions, because they are influenced by the music and not fully focusing on walking. As a result, step frequency decreases and stopping behavior is more frequent. This in turn leads to the decrease of the velocity and flow and the emergence of higher local densities. Our study will be helpful for understanding the effect of background music on pedestrian dynamics. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the ownership structure of the 3143 EU28 aerospace companies in 2019, and extend the analysis to the 2428 neighbor partners outside EU28 and/or aerospace. Different from the previous studies, we consider all equity capital flows regardless of their size, and their monetary value instead of the corresponding ownership share. We further innovate by applying new methods to measure degree of influence power and hierarchical structure. The resulting picture shows that between the pure EU28 aerospace companies: (i) ownership relationships concern only relatively few companies (10%), which trigger horizontal and vertical structures; (ii) density is extremely low; (iii) relationships are fully hierarchical with no cross-ownership; (iv) capital is seldom transferred across business groups; (v) most of the main topological parameters have a typically polarized scale-free structure. When including also the ownership neighbors, some of those traits change substantially: (i) the share of connected companies substantially grows up to 63%; (ii) size and length of the largest pyramidal structures will grow remarkably, reaching a top of 874 companies; (iii) the industry becomes a full small-world structure, thus allowing huge capital transfer across business groups. Finally, a dramatic financialization, meant as a pivotal and quantitatively heavy role of financial operators, emerges also as a clear characteristic of the extended network. [ABSTRACT FROM AUTHOR]

In this paper, we use the financial network contagion model of Gai P. and Kapadia S. [Contagion in financial networks, Proc. R. Soc. A 466 (2010) 2401-2423] to investigatethe interaction of several types of heterogeneity found in real world banking systems. The first source of heterogeneity originates in the distribution of assets across banks in the financial system. The second source is in how individual banks then distribute these assets among their neighbors. We characterize how these two sources of heterogeneity interact to affect the probability and extent of financial contagions in three network structures. We find that greater heterogeneity has a stabilizing effect for networks that are sparsely connected and a destabilizing effect for networks that are highly interconnected. Finally, we consider multiple sequential shocks and find that when banks redistribute assets following an initial mild contagion it increases the stability, on average, of the system to subsequent shocks originating at weakened banks. [ABSTRACT FROM AUTHOR]

We discuss the application of synchrotron X-ray microtomography (XMT) to granular matter, foams, crumpled membranes, and paper. XMT provides rapid, high-resolution, fully three-dimensional characterization of each of these classes of material. In some cases, subsequent three-dimensional image processing allows the virtual reconstruction of the disordered material as a specified assemblage of idealized basic structural units. This allows measurement of otherwise inaccessible correlation functions and can also be used as the starting point for data-initiated simulations. [ABSTRACT FROM AUTHOR]