An evolutionary complex systems perspective on urban health

Deliberations about how to govern complex problems of urban health and wellbeing sustainably have often been implicitly biased by ideas such as being ‘human-scale’ or ‘people-centered.’ With increasing urban populations and increasing urban system interconnectivity, many cities have transformed into city regions or clusters, and the external effects of urban growth are carried mainly by the marginalized and the environment putting urban health increasingly at risk. Here we address the question of why human societies have not been better at collectively adapting to the challenges of urbanization and global environmental change? We build a theoretical framework of multi-level selection, complex systems evolution, and governance, following which we then present ‘human-scale’ and ‘people-centered’ ideas of urban development as expressions of two types of socio-political organization with different degrees of self-organization. We found several reasons for which the maladies of current urban development emerged and the seeming inability to resolve them. First, urban systems became increasingly interconnected and evolved into ultrasocial superorganisms, displaying preference to sustain themselves as a whole rather than their subordinates. Second, the difference in scaling effects between the biological and the social network contributed to the mismatch between rapid urban growth and slow adaptation. Furthermore, institutions of decreased variety reinforce themselves and become dominant, creating a positive feedback mechanism and promoting invasive and exploitative exponential growth, but they also reduce the creativity and resilience of urban systems. We also found that both the “human-scale” and the “people-centered” approaches acknowledge the exponential growth and decreasing variety in urban systems, and advocate for correcting the mismatches. To incorporate people's needs and values for long-term, truly sustainable urban health governance, we recommend combining the self-organizing, evolutionary feature of “human-scale” and the coordinative, political feature of “people-centeredness.”     

A hybrid machine learning model for forecasting a billing period’s peak electric load days

Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period.     

Interbank tiering and money center banks

This paper provides evidence that interbank markets are tiered rather than flat, in the sense that most banks do not lend to each other directly but through money center banks acting as intermediaries. We capture the concept of tiering by developing a core-periphery model, and devise a procedure for fitting the model to real-world networks. Using Bundesbank data on bilateral interbank exposures among 2000 banks from 1999 to 2012, we find strong evidence of tiering in the German banking system. This extent of tiering is unlikely to arise in standard random networks. Indeed, we show that bank specialization and balance sheet variables predict how banks position themselves in the interbank market. This link provides a promising avenue for understanding the formation of financial networks.     

How did COVID-19 impact air transportation? A first peek through the lens of complex networks

The current outbreak of COVID-19 is an unprecedented event in air transportation. This is probably the first time that global aviation contributed to the planet-wide spread of a pandemic, with casualties in over two hundred countries. As of August 23rd, 2020, the number of infected cases has topped 23 million, reportedly relating to more than 800,000 deaths worldwide. However, there is also a second side of the pandemic: it has led to an unmatched singularity in the global air transportation system. In what could be considered a highly uncoordinated, almost chaotic manner, countries have closed their borders, and people are reluctant/unable to travel due to country-specific lock-down measures. Accordingly, aviation is one of the industries that has been suffering most due to the consequences of the pandemic outbreak, despite probably being one of its largest initial drivers. In this study, we investigate the impact of COVID-19 on global air transportation at different scales, ranging from worldwide airport networks where airports are nodes and links between airports exist when direct flights exist, to international country networks where countries are contracted as nodes, and to domestic airport networks for representative countries/regions. We focus on the spatial-temporal evolutionary dynamics of COVID-19 in air transportation networks. Our study provides a comprehensive empirical analysis on the impact of the COVID-19 pandemic on aviation from a complex system perspective using network science tools.     

基于重大科技基础设施建设迈向一流大学的路径分析——复杂产品系统动态能力演化视角

科技强国和创新型国家建设越来越依赖于重大科技基础设施的支撑能力。当前我国高校承担重大科技基础设施任务大多处于在建阶段，符合高校特点的能力建设模式尚在探索中。通过对2007-2020年脉冲强磁场重大科技基础设施发展的纵向案例研究，建立复杂产品系统（CoPS）的动态能力演化分析框架，从技术能力和组织能力分析高校承担重大科技基础设施动态能力搜索—获取—转型的演化过程。研究发现，高校通过建立新型“举校”体制，发展自组织网络调动校内外组织和技术互补性资源，能够有效构建高校的复杂组织和技术能力，统筹提升科研成效、学生质量和教师质量，实现面向服务国家战略目标的转型，从而获得一流大学的持续竞争优势。     

The relationship between brokers’ influence, strength of ties and NPD project outcomes in innovation-driven horizontal networks

This paper examines the impact of a broker's perceived use of power – position (i.e., coercive, reward and legitimate) and personal (i.e., expert, information and referent) – on strength of ties between network members and new product development (NPD) project outcomes. Our sample consists of 100 individuals drawn from 42 organizations that were involved in different innovation-driven horizontal networks. The results of structural equation modeling suggest that the perceived use of both personal power bases and position power bases by the broker are positively related to the strength of ties between members. Strength of ties, on the other hand, is positively related to NPD project outcomes of design performance and development time. Finally, results show that the relationships between a broker's use of different power bases and NPD project outcomes are fully mediated by the strength of ties between networks members. Implications for research, theory, and practice are discussed.     

In search of efficient network structures: the needle in the haystack

Searching for efficient networks can prove a very difficult analytical and even computational task. In this paper, we explore the possibility of using the genetic algorithms (GA) technique to identify efficient network structures in the case of non-trivial payoff functions. The robustness of this method in predicting optimal networks is tested on the two simple stylized models introduced by Jackson and Wolinsky (1996), for which the efficient networks are known over the whole state space of the parameters’ values. This approach allows us to obtain new exploratory results in the case of the linear-spatialized connections model proposed by Johnson and Gilles (Rev Econ Des 5:273–299, 2000), for which the efficient allocation of bilateral connections is driven by contradictory forces that push either for a centralized structure around a coordinating agent, or for only locally and evenly distributed connections. Murat Yıldızoğlu gratefully acknowledges the support of the CCRDT program of Aquitaine Region.     

基于AHP-FCE模型的复杂产品系统项目风险评估研究

针对复杂产品系统(CoPS)的特点,在对CoPS项目风险识别的基础上,利用层次分析(AHP)法建立了CoPS项目风险的评价指标体系,确定了风险因素的权重;并引入模糊综合评价(FCE)的基本思想和方法,构建了CoPS项目风险的模糊综合评价模型。并且结合案例介绍了该模型的应用过程。     

基于轨道交通整体网络的市域铁路线路规划研究

随着我国城市化进程不断推进,市域铁路发展需求不断增加,科学制定市域铁路线路规划,对推进新型城镇化市域铁路发展具有重要意义。在阐述市域铁路线路规划影响因素及结构模式选择的基础上,综合线路选择模型与线网规划模型的特点,建立市域铁路线路规划两阶段联合模型,先通过基于自身建设目标的线路规划模型中所得到较优备选线路集合,再联合基于轨道交通整体网络的线路比选模型的二次筛选,得出一个综合优化的市域铁路线路选择方案。以成德市域铁路线路规划为例,验证线路规划结果,为市域铁路线路规划研究提供借鉴。