Article of the Year 2021
Complex Urban Systems: Challenges and Integrated Solutions for the Sustainability and Resilience of CitiesRead the full article
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.
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.
Latest ArticlesMore articles
Existence of a Unique Solution and the Hyers–Ulam-H-Fox Stability of the Conformable Fractional Differential Equation by Matrix-Valued Fuzzy Controllers
In this paper, we consider a conformable fractional differential equation with a constant coefficient and obtain an approximation for this equation using the Radu–Mihet method, which is derived from the alternative fixed- point theorem. Considering the matrix-valued fuzzy k-normed spaces and matrix-valued fuzzy H-Fox function as a control function, we investigate the existence of a unique solution and Hyers–Ulam-H-Fox stability for this equation. Finally, by providing numerical examples, we show the application of the obtained results.
Novel Feature Selection Method for Nonlinear Support Vector Regression
The development of sparse techniques presents a major challenge to complex nonlinear high-dimensional data. In this paper, we propose a novel feature selection method for nonlinear support vector regression, called FS-NSVR, which first attempts to solve the nonlinear feature selection problem in the regression technology field. FS-NSVR preserves the representative features selected in the complex nonlinear system due to its use of a feature selection matrix in the original space. FS-NSVR is a challenging mixed-integer programming problem that is solved efficiently by using an alternate iterative greedy algorithm. Experimental results on three artificial datasets and five real-world datasets confirm that FS-NSVR effectively selects representative features and discards redundant features in a nonlinear system. FS-NSVR outperforms L1-norm support vector regression, L1-norm least squares support vector regression, and Lp-norm support vector regression on both feature selection ability and regression efficiency.
Potential Consequence of Interconnected Intervention against Systemic Risk (COVID-19) via a Model-Driven Network-Agent Dynamic
This study estimates the consequences of risk propagation, such as that of COVID-19, using network-agent dynamics. Given several scenarios, the network-agent model provides critical insights into infection risk using a model-driven approach to interconnected interventions. The simulation results suggest that employing a nonevolutionary governing structure with evolutionary individual interaction parameters guided by testing can help suppress outbreaks to levels below the standard critical-care capacity. Furthermore, setting the protection level as the macroscale and the shrinking of individual interactions as the microscale, the effects of social distancing on transmission rates are reflected in the disease model. In addition, the parameters that reflect the best feasible scenarios can be determined. These findings are relevant to COVID-19 pandemic policies wherein interconnected interventions reduce the socioeconomic costs of risk propagation.
Strong Emergence Arising from Weak Emergence
Predictions of emergent phenomena, appearing on the macroscopic layer of a complex system, can fail if they are made by a microscopic model. This study demonstrates and analyses this claim on a well-known complex system, Conway’s Game of Life. Straightforward macroscopic mean-field models are easily capable of predicting such emergent properties after they have been fitted to simulation data in an after-the-fact way. Thus, these predictions are macro-to-macro only. However, a micro-to-macro model significantly fails to predict correctly, as does the obvious mesoscopic modeling approach. This suggests that some macroscopic system properties in a complex dynamic system should be interpreted as examples of phenomena (properties) arising from “strong emergence,” due to the lack of ability to build a consistent micro-to-macro model, that could explain these phenomena in a before-the-fact way. The root cause for this inability to predict this in a micro-to-macro way is identified as the pattern formation process, a phenomenon that is usually classified as being of “weak emergence.” Ultimately, this suggests that it may be in principle impossible to discriminate between such distinct categories of “weak” and “strong” emergence, as phenomena of both types can be part of the very same feedback loop that mainly governs the system’s dynamics.
The Impacts of Multiscale Urban Road Network Centrality on Taxi Travel: A Case Study in Shenzhen
As a crucial part of the urban system, road networks play a key role in the evolution of the urban structure. Therefore, studying the structural characteristics of urban road networks is pivotal for improving the efficiency of traffic network nodes and for relieving traffic pressure. This paper applies an urban road network analysis method to measure the centrality of the multiscale road network in Shenzhen, China. Taxi GPS data from October 17 to October 23, 2017, were selected for analysis of spatial distribution characteristics. This paper also established a regression model of taxi pick-up and drop-off frequency and road network centrality for further analysis. Several interesting observations were made. With respect to the increasing search radius, the closeness centrality indicator shifts from a multicentered distribution to a single-centered distribution, while the betweenness centrality indicator shifts from a patchy distribution to a distribution along the main roads. In addition, the straightness centrality indicator turns from a dispersed distribution to a point-axis distribution, concentrated in the southern part of the city. Second, there were variations between the centrality of the road network and the location of taxi pick-up and drop-off points. The regression model gets the highest value of R2, indicating a significant correlation in cases where the search radius is 3 km. Finally, the relationship exhibits a clear positive correlation between the betweenness centrality and taxi pick-up and drop-off points. On the other hand, closeness centrality is not correlated with these points. The straightness centrality has a negative correlation with the frequency of taxi pick-up and drop-off at 3 km and 8 km scale.
Computational Study of Multiterm Time-Fractional Differential Equation Using Cubic B-Spline Finite Element Method
Due to the symmetry feature in nature, fractional differential equations precisely measure and describe biological and physical processes. Multiterm time-fractional order has been introduced to model complex processes in different physical phenomena. This article presents a numerical method based on the cubic B-spline finite element method for the solution of multiterm time-fractional differential equations. The temporal fractional part is defined in the Caputo sense while the B-spline finite element method is employed for space approximation. In addition, the four-point Gauss−Legendre quadrature is applied to evaluate the source term. The stability of the proposed scheme is discussed by the Von Neumann method, which verifies that the scheme is unconditionally stable. and norms are used to check the efficiency and accuracy of the proposed scheme. Computed results are compared with the exact and available methods in the literature, which show the betterment of the proposed method.