Abstract
The socio-economic system is a paradigm which can be defined as complex because behaviour change of free agents can result in other individuals and organisations experiencing chaotic dynamics, non-linear interactions and other cascading effects. To embrace sustainability in such a paradigm requires the adoption of emerging technologies that can lead to the next quantum leap including the big data platform. Big data provides us with a stream of new and digitized data exploring the interactions between individuals, companies and other organizations. However, to understand the underlying behavior of social and economic agents, organizations and researchers must manage large quantities of unstructured and heterogeneous data. To succeed in this undertaking requires careful planning and organization of the entire process of data analysis, taking into account the particularities of social and economic analyses such as the wide variety of heterogeneous sources of information and the existence of strict governance policy.
In recent years, many tools for both qualitative and quantitative models have been developed to describe and better understand complex systems. These tools include stochastic and dynamic systems, multivariate statistics, network models, social network analysis, inference and stochastic processes, fuzzy theory, relational calculus, partial order theory, multi-criteria decision methods and other tools which have been widely used to address problems in socio-economic systems. Traditional quantitative methods for acquiring socioeconomic data are limited in their ability to examine the complexities of socio-economic systems. Therefore, big data collected from satellites, mobile phones, and social media, among other data sources, allow researchers to build on and sometimes replace traditional methods providing greater frequency and timeliness, accuracy and objectiveness as well as defining sustainable models.
This Special Issue invites original research and review papers discussing complex socio-economic, financial and environmental problems, with a particular focus on the development and applications of new quantitative methods and models which combine new techniques in AI and big data analytics.
Potential topics include but are not limited to the following:
Geospatial modeling and machine learning
Intensive longitudinal data analysis with big data
Multilevel modeling techniques
AI based network analysis
Big data based causal inference
Visualization and scientometric analysis
Econometrics and demographic techniques
Machine learning and fuzzy theory
Complexity and simplicity
Reducing complexity to simplicity
Equilibrium or studying attractors
Non-linear dynamics
Self-organizing dynamics
Survival not optimality
Co-emergence of structure, beliefs and patterns of behavior
In recent years, many tools for both qualitative and quantitative models have been developed to describe and better understand complex systems. These tools include stochastic and dynamic systems, multivariate statistics, network models, social network analysis, inference and stochastic processes, fuzzy theory, relational calculus, partial order theory, multi-criteria decision methods and other tools which have been widely used to address problems in socio-economic systems. Traditional quantitative methods for acquiring socioeconomic data are limited in their ability to examine the complexities of socio-economic systems. Therefore, big data collected from satellites, mobile phones, and social media, among other data sources, allow researchers to build on and sometimes replace traditional methods providing greater frequency and timeliness, accuracy and objectiveness as well as defining sustainable models.
This Special Issue invites original research and review papers discussing complex socio-economic, financial and environmental problems, with a particular focus on the development and applications of new quantitative methods and models which combine new techniques in AI and big data analytics.
Potential topics include but are not limited to the following:
Geospatial modeling and machine learning
Intensive longitudinal data analysis with big data
Multilevel modeling techniques
AI based network analysis
Big data based causal inference
Visualization and scientometric analysis
Econometrics and demographic techniques
Machine learning and fuzzy theory
Complexity and simplicity
Reducing complexity to simplicity
Equilibrium or studying attractors
Non-linear dynamics
Self-organizing dynamics
Survival not optimality
Co-emergence of structure, beliefs and patterns of behavior
| Original language | English |
|---|---|
| Journal | Discrete Dynamics in Nature and Society |
| Publication status | Published - 1 Jan 2022 |