Complexity in Economic and Social Systems

There is no term that better describes the essential features of human society than complexity. On various levels, from the decision-making processes of individuals, through to the interactions between individuals leading to the spontaneous formation of groups and social hierarchies, up to the collective, herding processes that reshape whole societies, all these features share the property of irreducibility, i.e., they require a holistic, multi-level approach formed by researchers from different disciplines. This Special Issue aims to collect research studies that, by exploiting the latest advances in physics, economics, complex networks, and data science, make a step towards understanding these economic and social systems. The majority of submissions are devoted to financial market analysis and modeling, including the stock and cryptocurrency markets in the COVID-19 pandemic, systemic risk quantification and control, wealth condensation, the innovation-related performance of companies, and more. Looking more at societies, there are papers that deal with regional development, land speculation, and the-fake news-fighting strategies, the issues which are of central interest in contemporary society. On top of this, one of the contributions proposes a new, improved complexity measure

Entropy-based subspace clustering for mining numerical data.

by Cheng, Chun-hung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1999.Includes bibliographical references (leaves 72-76).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgments --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Six Tasks of Data Mining --- p.1Chapter 1.1.1 --- Classification --- p.2Chapter 1.1.2 --- Estimation --- p.2Chapter 1.1.3 --- Prediction --- p.2Chapter 1.1.4 --- Market Basket Analysis --- p.3Chapter 1.1.5 --- Clustering --- p.3Chapter 1.1.6 --- Description --- p.3Chapter 1.2 --- Problem Description --- p.4Chapter 1.3 --- Motivation --- p.5Chapter 1.4 --- Terminology --- p.7Chapter 1.5 --- Outline of the Thesis --- p.7Chapter 2 --- Survey on Previous Work --- p.8Chapter 2.1 --- Data Mining --- p.8Chapter 2.1.1 --- Association Rules and its Variations --- p.9Chapter 2.1.2 --- Rules Containing Numerical Attributes --- p.15Chapter 2.2 --- Clustering --- p.17Chapter 2.2.1 --- The CLIQUE Algorithm --- p.20Chapter 3 --- Entropy and Subspace Clustering --- p.24Chapter 3.1 --- Criteria of Subspace Clustering --- p.24Chapter 3.1.1 --- Criterion of High Density --- p.25Chapter 3.1.2 --- Correlation of Dimensions --- p.25Chapter 3.2 --- Entropy in a Numerical Database --- p.27Chapter 3.2.1 --- Calculation of Entropy --- p.27Chapter 3.3 --- Entropy and the Clustering Criteria --- p.29Chapter 3.3.1 --- Entropy and the Coverage Criterion --- p.29Chapter 3.3.2 --- Entropy and the Density Criterion --- p.31Chapter 3.3.3 --- Entropy and Dimensional Correlation --- p.33Chapter 4 --- The ENCLUS Algorithms --- p.35Chapter 4.1 --- Framework of the Algorithms --- p.35Chapter 4.2 --- Closure Properties --- p.37Chapter 4.3 --- Complexity Analysis --- p.39Chapter 4.4 --- Mining Significant Subspaces --- p.40Chapter 4.5 --- Mining Interesting Subspaces --- p.42Chapter 4.6 --- Example --- p.44Chapter 5 --- Experiments --- p.49Chapter 5.1 --- Synthetic Data --- p.49Chapter 5.1.1 --- Data Generation ´ؤ Hyper-rectangular Data --- p.49Chapter 5.1.2 --- Data Generation ´ؤ Linearly Dependent Data --- p.50Chapter 5.1.3 --- Effect of Changing the Thresholds --- p.51Chapter 5.1.4 --- Effectiveness of the Pruning Strategies --- p.53Chapter 5.1.5 --- Scalability Test --- p.53Chapter 5.1.6 --- Accuracy --- p.55Chapter 5.2 --- Real-life Data --- p.55Chapter 5.2.1 --- Census Data --- p.55Chapter 5.2.2 --- Stock Data --- p.56Chapter 5.3 --- Comparison with CLIQUE --- p.58Chapter 5.3.1 --- Subspaces with Uniform Projections --- p.60Chapter 5.4 --- Problems with Hyper-rectangular Data --- p.62Chapter 6 --- Miscellaneous Enhancements --- p.64Chapter 6.1 --- Extra Pruning --- p.64Chapter 6.2 --- Multi-resolution Approach --- p.65Chapter 6.3 --- Multi-threshold Approach --- p.68Chapter 7 --- Conclusion --- p.70Bibliography --- p.71Appendix --- p.77Chapter A --- Differential Entropy vs Discrete Entropy --- p.77Chapter A.1 --- Relation of Differential Entropy to Discrete Entropy --- p.78Chapter B --- Mining Quantitative Association Rules --- p.80Chapter B.1 --- Approaches --- p.81Chapter B.2 --- Performance --- p.82Chapter B.3 --- Final Remarks --- p.8

Modelling spatial and temporal urban growth

Summary In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: ? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; ? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; ? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; ? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; ? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery. Summary In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: ? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; ? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; ? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; ? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; ? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery. Summary In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: ? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; ? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; ? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; ? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; ? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery. Summary In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: ? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; ? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; ? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; ? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; ? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery. In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery

Symmetric and Asymmetric Data in Solution Models

This book is a Printed Edition of the Special Issue that covers research on symmetric and asymmetric data that occur in real-life problems. We invited authors to submit their theoretical or experimental research to present engineering and economic problem solution models that deal with symmetry or asymmetry of different data types. The Special Issue gained interest in the research community and received many submissions. After rigorous scientific evaluation by editors and reviewers, seventeen papers were accepted and published. The authors proposed different solution models, mainly covering uncertain data in multicriteria decision-making (MCDM) problems as complex tools to balance the symmetry between goals, risks, and constraints to cope with the complicated problems in engineering or management. Therefore, we invite researchers interested in the topics to read the papers provided in the book

Tradition and Innovation in Construction Project Management

This book is a reprint of the Special Issue 'Tradition and Innovation in Construction Project Management' that was published in the journal Buildings

DATA-DRIVEN ANALYTICAL MODELS FOR IDENTIFICATION AND PREDICTION OF OPPORTUNITIES AND THREATS

During the lifecycle of mega engineering projects such as: energy facilities, infrastructure projects, or data centers, executives in charge should take into account the potential opportunities and threats that could affect the execution of such projects. These opportunities and threats can arise from different domains; including for example: geopolitical, economic or financial, and can have an impact on different entities, such as, countries, cities or companies. The goal of this research is to provide a new approach to identify and predict opportunities and threats using large and diverse data sets, and ensemble Long-Short Term Memory (LSTM) neural network models to inform domain specific foresights. In addition to predicting the opportunities and threats, this research proposes new techniques to help decision-makers for deduction and reasoning purposes. The proposed models and results provide structured output to inform the executive decision-making process concerning large engineering projects (LEPs). This research proposes new techniques that not only provide reliable timeseries predictions but uncertainty quantification to help make more informed decisions. The proposed ensemble framework consists of the following components: first, processed domain knowledge is used to extract a set of entity-domain features; second, structured learning based on Dynamic Time Warping (DTW), to learn similarity between sequences and Hierarchical Clustering Analysis (HCA), is used to determine which features are relevant for a given prediction problem; and finally, an automated decision based on the input and structured learning from the DTW-HCA is used to build a training data-set which is fed into a deep LSTM neural network for time-series predictions. A set of deeper ensemble programs are proposed such as Monte Carlo Simulations and Time Label Assignment to offer a controlled setting for assessing the impact of external shocks and a temporal alert system, respectively. The developed model can be used to inform decision makers about the set of opportunities and threats that their entities and assets face as a result of being engaged in an LEP accounting for epistemic uncertainty

Wavelet Theory

The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior

Three Risky Decades: A Time for Econophysics?

Our Special Issue we publish at a turning point, which we have not dealt with since World War II. The interconnected long-term global shocks such as the coronavirus pandemic, the war in Ukraine, and catastrophic climate change have imposed significant humanitary, socio-economic, political, and environmental restrictions on the globalization process and all aspects of economic and social life including the existence of individual people. The planet is trapped—the current situation seems to be the prelude to an apocalypse whose long-term effects we will have for decades. Therefore, it urgently requires a concept of the planet's survival to be built—only on this basis can the conditions for its development be created. The Special Issue gives evidence of the state of econophysics before the current situation. Therefore, it can provide excellent econophysics or an inter-and cross-disciplinary starting point of a rational approach to a new era

Stock Market Investment Using Machine Learning

Genetic Algorithm-Support Vector Regression (GA-SVR) and Random Forest Regression (RFR) were constructed to forecast stock returns in this research. 15 financial indicators were selected through fuzzy clustering from 42 financial indicators, then combined with 8 technical indicators as input space, the 10-day stocks return was used as labels. The results show that GA-SVR and RFR can make compelling forecasting and pass the robustness test. GA-SVR and RFR exhibit different processing preferences for features with different importance. Furthermore, by testing stock markets in China, Hong Kong (China) and the United States, the model shows different effectiveness