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- AUTHOR INDEX (2023)
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Abstract: Advances in Complex Systems, Volume 26, Issue 07n08, November & December 2023.
Citation: Advances in Complex Systems
PubDate: 2024-03-19T07:00:00Z
DOI: 10.1142/S0219525923990011
Issue No: Vol. 26, No. 07n08 (2024)
- UNDERSTANDING MEMORY MECHANISMS IN SOCIO-TECHNICAL SYSTEMS: THE CASE OF AN
AGENT-BASED MOBILITY MODEL-
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Authors: GESINE A. STEUDLE, STEFANIE WINKELMANN, STEFFEN FÜRST, SARAH WOLF
Abstract: Advances in Complex Systems, Ahead of Print.
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.
Citation: Advances in Complex Systems
PubDate: 2024-06-27T07:00:00Z
DOI: 10.1142/S0219525924400034
- IMine: A CUSTOMIZABLE FRAMEWORK FOR INFLUENCE MINING IN COMPLEX NETWORKS
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Authors: OWAIS A. HUSSAIN, FARAZ A. ZAIDI
Abstract: Advances in Complex Systems, Ahead of Print.
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.
Citation: Advances in Complex Systems
PubDate: 2024-06-27T07:00:00Z
DOI: 10.1142/S0219525924500048
- TRACES OF UNEQUAL ENTRY REQUIREMENT FOR ILLUSTRIOUS PEOPLE ON WIKIPEDIA
BASED ON THEIR GENDER-
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Authors: LEA KRIVAA, MICHELE COSCIA
Abstract: Advances in Complex Systems, Ahead of Print.
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.
Citation: Advances in Complex Systems
PubDate: 2024-06-14T07:00:00Z
DOI: 10.1142/S0219525924500036
- EVALUATE NODE IMPORTANCE BY DECOMPOSING NETWORK WITH A RECURSIVE
PERCOLATION PROCESS-
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Authors: HUI WANG, ZHENYU YANG, RUN-RAN LIU, DONGHUI HU, MING LI
Abstract: Advances in Complex Systems, Ahead of Print.
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.
Citation: Advances in Complex Systems
PubDate: 2024-05-29T07:00:00Z
DOI: 10.1142/S0219525924500024
- COMPLEX CONTAGION IN SOCIAL SYSTEMS WITH DISTRUST
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Authors: JEAN-FRANÇOIS DE KEMMETER, LUCA GALLO, FABRIZIO BONCORAGLIO, VITO LATORA, TIMOTEO CARLETTI
Abstract: Advances in Complex Systems, Ahead of Print.
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.
Citation: Advances in Complex Systems
PubDate: 2024-05-17T07:00:00Z
DOI: 10.1142/S0219525924400010
- AMPLITUDE EQUATIONS AND ORDER PARAMETERS OF HUMAN SARS-COV-2 INFECTIONS
AND IMMUNE REACTIONS: A MODEL-BASED APPROACH-
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Authors: T. D. FRANK
Abstract: Advances in Complex Systems, Ahead of Print.
Several recent modeling studies have attempted to understand the human immune reaction to SARS-CoV-2 infections. Such a model-based understanding provides a sound basis for fighting COVID-19 on the level of individual patients. However, in this context, a worked-out nonlinear physics analysis providing insights into underlying amplitude equations and potential COVID-19 order parameters has not been conducted so far. In order to conduct such an analysis, a three-variable virus dynamics model is considered that can account for the human immune reaction to SARS-CoV-2 infections. The model amplitudes equations are derived and the relevant order parameter is determined. In line with theoretical reasoning, it is demonstrated that the order parameter and its amplitude determine the initial stage of SARS-CoV-2 infections, in general, and the initial dynamics of immune reactions, in particular. Explicitly, this finding is demonstrated for data from four COVID-19 patients. For those patients it is also demonstrated that the remnant of the order parameter determines the final disease decline phase. In this context, a time-resolved eigenvalue analysis is conducted that reveals that the transition from the initial stage to the decline stage which is associated with a switch of the leading eigenvalue from a positive to a negative value. It is argued that the immune reaction essentially contributes to this switch. From a medical-physics point of view, this observation suggests that the immune reaction of COVID-19 patients can stabilize the virus-free fixed point of affected sites.
Citation: Advances in Complex Systems
PubDate: 2024-05-15T07:00:00Z
DOI: 10.1142/S0219525924500012
- THE OPPORTUNITIES, LIMITATIONS, AND CHALLENGES IN USING MACHINE LEARNING
TECHNOLOGIES FOR HUMANITARIAN WORK AND DEVELOPMENT-
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Authors: VEDRAN SEKARA, MÁRTON KARSAI, ESTEBAN MORO, DOHYUNG KIM, ENRIQUE DELAMONICA, MANUEL CEBRIAN, MIGUEL LUENGO-OROZ, REBECA MORENO JIMÉNEZ, MANUEL GARCIA-HERRANZ
Abstract: Advances in Complex Systems, Ahead of Print.
Novel digital data sources and tools like machine learning (ML) and artificial intelligence (AI) have the potential to revolutionize data about development and can contribute to monitoring and mitigating humanitarian problems. The potential of applying novel technologies to solving some of humanity’s most pressing issues has garnered interest outside the traditional disciplines studying and working on international development. Today, scientific communities in fields like Computational Social Science, Network Science, Complex Systems, Human Computer Interaction, Machine Learning, and the broader AI field are increasingly starting to pay attention to these pressing issues. However, are sophisticated data driven tools ready to be used for solving real-world problems with imperfect data and of staggering complexity' We outline the current state-of-the-art and identify barriers, which need to be surmounted in order for data-driven technologies to become useful in humanitarian and development contexts. We argue that, without organized and purposeful efforts, these new technologies risk at best falling short of promised goals, at worst they can increase inequality, amplify discrimination, and infringe upon human rights.
Citation: Advances in Complex Systems
PubDate: 2024-05-03T07:00:00Z
DOI: 10.1142/S0219525924400022
- STRUCTURAL INSULATORS AND PROMOTORS IN NETWORKS UNDER GENERIC
PROBLEM-SOLVING DYNAMICS-
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Authors: JOHANNES FALK, EDWIN EICHLER, KATJA WINDT, MARC-THORSTEN HÜTT
Abstract: Advances in Complex Systems, Ahead of Print.
The collective coordination of distributed tasks in a complex system can be represented as decision dynamics on a graph. This abstract representation allows studying the performance of local decision heuristics as a function of task complexity and network architecture. Here, we identify hard-to-solve and easy-to-solve networks in a social differentiation task within the basic model of small-world graphs. We show that, depending on the details of the decision heuristic as well as the length of the added links, shortcuts can serve as structural promotors, which speed up convergence toward a solution, but also as structural insulators, which make the network more difficult to solve. Our findings have implications for situations where, in distributed decision systems, regional solutions emerge, which are globally incompatible as, for example, during the emergence of technological standards.
Citation: Advances in Complex Systems
PubDate: 2024-03-08T08:00:00Z
DOI: 10.1142/S0219525923500121
- INVOLUTION GAME WITH SPECIALIZATION STRATEGY
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Authors: BO LI
Abstract: Advances in Complex Systems, Ahead of Print.
Involution now refers to the phenomenon that competitors in the same field make more efforts to struggle for limited resources but get lower individual “profit effort ratio”. In this work, we investigate the evolution of the involution game when competitors in the same field can adopt not only the strategy of making more efforts but also a specialization strategy which allows competitors to devote all their efforts to part of the competitive field. Based on the existing model, we construct the involution game with the specialization strategy and simulate the evolution of it on a square lattice under different social resource, allocation parameter (characterizing the intensity of social competition), effort and other conditions. In addition, we also conduct a theoretical analysis to further understand the underlying mechanism of our model and to avoid illusive results caused by the model settings. Our main results show that, when the total effort of the specialization strategy and the ordinary strategy is equal, the group composed of all the agents has a certain probability to choose the ordinary strategy if the allocation parameter is very large (that is to say, the intensity of competition is very weak), otherwise the group will choose the specialization strategy; when the total effort of the two strategies is not equal, the proportion of the specialization strategy adoption is related to the social resource, the effort and the allocation parameter. To some extent, our study can explain why division of labor appears in human society and provide suggestions for individuals on competition strategy selection and governments on competition policy development.
Citation: Advances in Complex Systems
PubDate: 2024-02-24T08:00:00Z
DOI: 10.1142/S0219525923500133
- INFLUENCE OF NETWORK STRUCTURE AND AGENT PROPERTY ON SYSTEM PERFORMANCE
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Authors: HONGZHONG DENG, JI LI, HONGQIAN WU, BINGFENG GE
Abstract: Advances in Complex Systems, Ahead of Print.
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.
Citation: Advances in Complex Systems
PubDate: 2024-01-23T08:00:00Z
DOI: 10.1142/S021952592350011X
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