Complexity
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Acceptance rate11%
Submission to final decision120 days
Acceptance to publication21 days
CiteScore4.400
Journal Citation Indicator0.720
Impact Factor2.3

A New Mathematical Model for Cell Layout Problem considering Rotation of Unequal Dimensions of Cells and Machines

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

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

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We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

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Research Article

Ripple-Spreading Network of China’s Systemic Financial Risk Contagion: New Evidence from the Regime-Switching Model

A better understanding of financial contagion and systemically important financial institutions (SIFIs) is essential for the prevention and control of systemic financial risk. Considering the ripple effect of financial contagion, we integrate the relevant spatiotemporal information that affects financial contagion and propose to use the ripple-spreading network to simulate the dynamic process of risk contagion in China’s financial system. In addition, we introduce the smooth-transition vector autoregression (STVAR) model to identify “high” and “low” systemic risk regimes and set the relevant parameters of the ripple-spreading network on this basis. The results show that risk ripples spread much faster in high than in low systemic risk regimes. However, systemic shocks can also trigger large-scale risk contagion in the financial system even in a low systemic risk regime as the risk ripple continues. In addition, whether the financial system is in a high or low systemic risk regime, the risk ripples from a contagion source (i.e., a real estate company) spread first to the real estate sector and the banking sector. The network centrality results of the heterogeneous ripple-spreading network indicate that most securities and banks and some real estate companies have the highest systemic importance, followed by the insurance, and finally the diversified financial institutions. Our study provides a new perspective on the regulatory practice of systemic financial risk and reminds regulators to focus not only on large institutions but also on institutions with strong ripple capacity.

Research Article

The Core Might Change Anyhow We Define It: The Instability of Key Actors in Longitudinal Social Network Data

Central actors or opinion leaders are in the right structural position to spread relevant information or convince others about adopting an innovation or behaviour change. Who is a central actor or opinion leader might be conceptualised in various ways. Widely accepted centrality measures do not take into account that those in central positions in the social network may change over time. A longitudinal comparison of the set and importance of opinion leaders is problematic with these measures and therefore needs a novel approach. In this study, we investigate ways to compare the stability of the set of central actors over time. Using longitudinal survey data from primary schools (where the members of the social networks do not change much over time) on advice-seeking and friendship networks, we find a relatively poor stability of who is in the central positions anyhow we define centrality. We propose the application of combined indices in order to achieve more efficient targeting results. Our results suggest that because opinion leaders may change over time, researchers should be careful about relying on simple centrality indices from cross-sectional data to gain and interpret information (for example, in the design of prevention programs, network-based interventions or infection control) and must rely on more diverse structural information instead.

Research Article

The Complexities in the R&D Competition Model with Spillover Effects in the Supply Chain

This study aims to investigate the research and development (R&D) competition within the supply chain, focusing on two aspects: R&D competition at the manufacturing level and competition in pricing strategies. This paper establishes a dynamic game model of R&D competition, comprising two manufacturers and two retailers, with both manufacturers exhibiting bounded rationality. The key findings are as follows: (1) an increase in the adjustment speed positively affects the chaotic nature of the R&D competition system, leading to a state of disorder. This chaotic state has adverse implications for manufacturing profitability. (2) The spillover effect exhibits a positive relationship with the level of chaos in the R&D competition system. A greater spillover effect contributes to a more turbulent environment, which subsequently impacts the profitability of manufacturers. (3) R&D cost parameters exert a positive influence on the stability of the R&D competition system. When the system reaches a state of equilibrium, an escalation in the R&D cost parameters poses a threat to manufacturer profitability. (4) Retailer costs play a detrimental role in the stability of the R&D competition system. As retailer costs increase, there is a decline in R&D levels, thereby diminishing manufacturer profitability. (5) To mitigate the chaotic state, we propose the implementation of the time-delayed feedback control (TDFC) method, which reflects a more stable state in the R&D competition system.

Research Article

The Development Strategy of Dual-Channel Supply Chain of Smart Elderly Care Service from the Perspective of Time Perception

The development of smart elderly industry is an inevitable way to cope with the aging of the population. This research takes the smart elderly care service supply chain as the object, combines the social emotional choice theory for the first time, and uses the time perception to refine the elderly care demand into future-orientation demand and present-orientation demand. This paper analyzes the coordination effect of suppliers’ efforts to meet the needs of the elderly under the two conditions of no contract and benefit-sharing contract on the dual-channel supply chain of smart elderly care services. The results show that the benefit-sharing contract is more conducive to maximizing the profit of the supply chain, and the segmentation of elderly demand is conducive to giving full play to the advantages of dual-channel differentiated services, which is conducive to forming a win-win situation of improving the service efficiency of the smart elderly service supply chain and increasing the happiness index of elderly users. The main contributions of this paper are: Using geriatric behavioral psychology to analyze the motivation of the elderly and designing the service effort level index considering the needs of the elderly. Match online and offline channels with personalized services to give full play to the “smart” effect. Based on the game method of Hotelling and Stackelberg, the coordination and optimization of the smart elderly care services dual-channel supply chain, considering the needs of the elderly are realized. This is of great research significance for maximizing the benefits of the smart elderly service supply chain and promoting the development of the smart elderly industry.

Research Article

Research on the Statistical Properties and Stability of Complex Interindustrial Networks

This study consolidates input-output data from 42 sectors across 31 provinces and regions in China into a unified dataset for 42 industrial sectors within eight major economic zones. Leveraging the maximum entropy method, we identify significant interindustrial relationships, subsequently forming a directed, weighted, complex network of these ties. Building upon this intricate network, we analyze its foundational statistical attributes. The stability of the network’s structure is further assessed through simulations of varied network attacks. Our findings demonstrate that the maximum entropy method is adept at extracting notable relationships between industrial sectors, facilitating the creation of a cogent complex interindustrial network. Although this established network exhibits high stability, it calls for targeted policy interventions and risk management, especially for industries with pronounced degree centrality and betweenness centrality. These pivotal industry nodes play a decisive role in the overall stability of the network. The insights derived from our examination of complex interindustrial networks illuminate the structure and function of industrial networks, bearing profound implications for policymaking and propelling sustainable, balanced economic progress.

Research Article

Echo State Property upon Noisy Driving Input

The echo state property (ESP) is a key concept for understanding the working principle of the most widely used reservoir computing model, the echo state network (ESN). The ESP is achieved most of the operation time under general conditions, yet the property is lost when a combination of driving input signals and intrinsic reservoir dynamics causes unfavorable conditions for forgetting the initial transient state. A widely used treatment, setting the spectral radius of the weight matrix below the unity, is not sufficient as it may not properly account for the nature of driving inputs. Here, we characterize how noisy driving inputs affect the dynamical properties of an ESN and the empirical evaluation of the ESP. The standard ESN with a hyperbolic tangent activation function is tested using the MNIST handwritten digit datasets at different additive white Gaussian noise levels. The correlations among the neurons, input mapping, and memory capacity of the reservoir nonlinearly decrease with the noise level. These trends agree with the deterioration of the MNIST classification accuracy against noise. In addition, the ESP index for noisy driving input is developed as a tool to help easily assess ESPs in practical applications. Bifurcation analysis explicates how the noise destroys an asymptotical convergence in an ESN and confirms that the proposed index successfully captures the ESP against noise. These results pave the way for developing noise-robust reservoir computing systems, which may promote the validity and utility of reservoir computing for real-world machine learning applications.

Complexity
Publishing Collaboration
More info
Wiley Hindawi logo
 Journal metrics
See full report
Acceptance rate11%
Submission to final decision120 days
Acceptance to publication21 days
CiteScore4.400
Journal Citation Indicator0.720
Impact Factor2.3
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