Accepted Special Sessions

Special Session 01: IT Supported Collaborative Decision-making in Management and Control: Methods, Tools, Systems, and Applications

Prof. F. G. Filip, The Romanian Academy, Bucharest, Romania, ffilip@acad.ro (https://acad.ro/cv/FilipF/FGF-CV-en.pdf)

Prof. Constantin Bala Zamfirescu, “Lucian Blaga”, University, Sibiu, Romania, zbc@acm.org (https://web.ulbsibiu.ro/constantin.zamfirescu/)

Prof. Cristian Ciurea, Academy of Economic Studies-ASE, Bucharest Romania, cristian.ciurea@ie.ase.ro (https://orcid.org/0000-0002-7327-0007)

In the context of new business models applied to the management of the present-day public and private organizations, the multi-participant collaborative decision-making activities have gained ever more traction. The pandemic has caused adaptation of the working style to a new situation by using appropriate supporting methods and technologies. Recent trends for combining AI1 (Artificial Intelligence) with AI2 (Augmenting Intelligence of humans) within hybrid units can be noticed. Data collection, consensus building, solution selection, network and crowd working-based decisions also involving collaboratives robots in decision-making activities have been supported by modern Information and Communication Technologies (I&CT). Also, the preoccupation for human wellbeing, safety, and cultural diversity of the people involved is ever more noticeable in modern complex management and control settings.

The session is meant to include papers that contain recent results obtained by research teams from academia and industry concerning, but not limited to the following topics:
  • Network and crowd working methods and corresponding platforms.
  • Cobots, digital clones, and humans with augmented intelligence and their collaboration in hybrid units.
  • Computer supported collaboration engineering.
  • Modern I&CT enablers for collaborative activities, such as: a) AI-based tools including service-oriented cognitive systems, b) multi-agent cooperative schemes, c) data science and analytics, d) cloud, sky, and mobile computing, e) social networks, f) digital twins and co-simulation, and so on.
  • Multi-person Decision Support Systems (DSS) and platforms.
  • Special cases of DSS, such as: recommender systems, systems designed to support real-time decision-making in emergency and risky situations and so on.
  • Practical recent applications including collaborative learning, management of healthcare centres, and cultural institutions and events.
  • Green and trustworthy computing, ethical aspects, and digital humanism

Special Session 02: Network Analysis in Decision Making

Prof. Fuad Aleskerov, HSE University, Russia, fa201204@gmail.com

Today many problems influencing a wide set of people, countries, industries cannot be explained without analyzing bilateral and multilateral connections among the groups of actors.
Our section accepts papers dealing with theoretical and practical problems in which to make the right decision is necessary to study network relations among objects.
In particular, we are going to consider an analysis of networks using classic and new centrality indices, stability of networks, networks under deep uncertainty, dynamic analysis of networks such as the spread of diseases, particular problems dealing with networks such as migration network, food security using network analysis, etc.

Special Session 03: Reimagining Crisis Management and Decision-Making with Technology

Prof. Nitin Upadhyay, Indian Institute of Management Jammu, India, nitin@iimj.ac.in

The COVID-19 pandemic has exposed the vulnerability of global systems to unexpected and unprecedented crises. This has highlighted the need for effective crisis management and decision-making strategies to mitigate the impact of future crises. Technology has played a vital role in facilitating communication, collaboration, and data-driven decision-making during the current pandemic. However, there is a need to explore and develop new technologies and tools that can improve crisis management and decision-making in times of uncertainty.
The conference session welcome papers that explore the potential of technology in supporting effective communication, collaboration, and data-driven decision-making during crises. Additionally, submissions are encouraged that investigate the ethical considerations and potential risks associated with technology-enabled crisis management strategies. One of the key areas of interest for this call for papers is the role of artificial intelligence (AI) and machine learning (ML) in crisis management and decision-making. AI and ML technologies have the potential to analyze large datasets, identify patterns and trends, and provide insights that can inform decision-making. This technology can support the automation of routine tasks and allow decision-makers to focus on higher-level strategic planning.
Furthermore, the use of visualization and simulation tools can enhance situational awareness and improve decision-making during crises. Visualization tools can allow decision-makers to view real-time data, such as COVID-19 infection rates or natural disaster patterns, in a way that is easily understood. Similarly, simulation tools can allow decision-makers to model different scenarios and evaluate the potential outcomes of various decisions. Another area of interest is the potential of emerging technologies, such as blockchain and cloud computing, to enable resilient crisis response. Blockchain technology can be used to securely and transparently manage supply chains, track the distribution of critical resources, and verify the authenticity of information. Similarly, cloud computing can support remote collaboration, data sharing, and backup and recovery during crises.
The impact of social media, crowdsourcing, and citizen science on crisis communication and decision-making is another area of interest for this call for papers. Social media platforms have become a crucial source of information during crises, providing real-time updates and facilitating communication between affected individuals and organizations. Crowdsourcing and citizen science can also support crisis response efforts by enabling citizens to collect and analyze data and share insights with decision-makers.
Moreover, the potential of human-machine collaboration in crisis response and decision-making is another area of interest. This involves combining the unique strengths of humans and machines to enhance crisis management strategies. For instance, machines can automate routine tasks, while humans can provide critical thinking and decision-making skills.
Finally, the conference session welcome papers that explore the impact of crises on technological innovation and the potential of technology to address future crises. Crises can serve as catalysts for innovation, leading to the development of new technologies and approaches to crisis management. It is crucial to evaluate these innovations and determine their potential for future crises.
The aim of this call for papers is to invite scholars, researchers, practitioners, and policymakers to contribute to a multidisciplinary discussion on the role of technology in reimagining crisis management and decision-making. The conference session encourage theoretical, conceptual, empirical, and case study papers that offer new insights into the role of technology in reimagining crisis management and decision-making.
Topics of the session include, but is not limited to, the following:
  • The role of artificial intelligence, machine learning, and data analytics in crisis management and decision-making
  • The use of visualization and simulation tools to enhance situational awareness and improve decision-making
  • The potential of blockchain, cloud computing, and other emerging technologies to enable resilient crisis response
  • The impact of social media, crowdsourcing, and citizen science on crisis communication and decision-making
  • The ethical considerations in using technology to support crisis management and decision-making
  • The role of collaboration and collective intelligence in enhancing crisis response and decision-making
  • The potential of human-machine collaboration in crisis response and decision-making
  • The use of virtual and augmented reality in crisis management and decision-making
  • The impact of crises on technological innovation and the potential of technology to address future crises.
  • Technology-driven crises management and decision-making in various industries (For instance, Banking and Financial Services, Tourism, Healthcare, Manufacturing, Hospitality, Education, etc.)

Special Session 04: The 10th Intelligent Decision Making and Extenics based Application

Prof. Xingsen Li, Guangdong University of Technology, China, lixingsen@126.com

Prof. Tao Wang, Beijing Institute of Technology, China, wangtao1020@126.com

Prof. Long Tang, Nanjing University of Information Science and Technology, China, tanglong@gdut.edu.cn

With the rapid development of information technology, knowledge acquisition through data mining becomes one of the most important directions of scientific decision-making. Extenics is a new inter-discipline of mathematics, information, philosophy, and engineering including Extension theory, extension innovation methods and extension engineering. It builds the theory and methods of solving contradictory problems using formalized models to explore the possibility of extension and transformation of things and solve problems intelligently. The intelligent methods aim to provide targeted decision-making on the transformation of the practice which is facing the challenges of data explosion. Artificial intelligence and intelligent systems beyond big data offer efficient mechanisms that can significantly improve the quality of decision-making. Through ITQM, participants can further discuss the state-of-art technology in the Intelligent Decision Making and Extenics based application as well as the problems or issues occurred during their research. The topics and areas include, but not limited to:
  • Extenics based Information methods and technology
  • Intelligent knowledge management based on Extenics
  • Intelligent Information management and Problem Solving on Extenics
  • Knowledge Mining on E-business
  • Intelligent Systems and its Applications based on Extenics
  • Web Marketing and CRM taking Extenics as methodology
  • Intelligent Data Analysis and Financial Management
  • Intelligent technology and Tourism Management
  • Innovation theory and Extenics based Methods
  • Extenics based Decision Making
  • Extension data mining and its Applications
  • Web Intelligence and Innovation on big data
  • Knowledge based Systems and decision-making theory on Extenics
  • Extenics based design technology and applications
  • Intelligent Logistics Management and Web of Things taking Extenics as methodology

Special Session 05: Soft Computing and Optimization for Decision-Making

Prof. Bogdana Stanojević, Mathematical Institute of the Serbian Academy of Sciences and Arts, Serbia, bgdnpop@mi.sanu.ac.rs

Prof. Boris Delibašić, Faculty of Organizational Sciences, University of Belgrade, Serbia, boris.delibasic@fon.bg.ac.rs

Prof. Sandro Radovanović, Faculty of Organizational Sciences, University of Belgrade, Serbia, sandro.radovanovic@fon.bg.ac.rs

We cordially invite papers addressing new methods and applications in the areas of soft computing and optimization methods. Despite the prevalent use of fully automated machine learning methods, expert-based methods and their combination with machine learning methods still play an important role in decision-making as they try to bind two worlds in decision-making, the expert based and the data based.
With soft computing methods and tools one can exploit domain knowledge about the problem at hand, model uncertainty, and imprecision in measurements robustly and with low computation cost. By doing this one enhances the context of the decision-making process and offers comprehensiveness (greater interpretability and explainability) of the decision being made.
This session aims to present state-of-the-art research on soft computing and optimization, and its application to either expert- or algorithmic-based decision making. Having this in mind, we solicit submissions in all aspects of soft computing and optimization for decision-making, from mathematical foundations of soft computing and optimization for decision-making, novel approaches and methods, and its applications for business purposes or for proposing solutions for social problems.
Topics of interest in this session are presented in the following non-exhaustive list:
  • soft computing;
  • fuzzy logic, fuzzy sets and systems and their extensions;
  • fuzzy decision-making and decision support systems;
  • fuzzy optimization;
  • optimization methods and applications for decision-making;
  • multi-criteria decision-making;
  • soft computing and optimization in data mining and machine learning;
  • soft computing and optimization for social good;
  • multi-objective optimization;
  • mathematical theory for modelling uncertainty;
  • decision making under uncertainty;
  • approximate reasoning, and others.

Special Session 06: Soft Computing Techniques in Real-world Application

Dr. Simona Dzitac, University of Oradea, Romania, simona@dzitac.ro

Dr. Attila Simo, Politehnica University Timisoara, Romania, attila.simo@upt.ro

Prof. Domnica Dzitac, New York University Abu Dhabi, Abu Dhabi, domnica.dzitac@nyu.edu

Simulation of human intelligence into mathematical models has been the focus of recent research, with the goal of finding solutions to large, complex, and ever-changing problems in the real world. The term "soft computing" refers to the combination of various intelligent paradigms that deal with real-world practical scenarios in a manner that is analogous to how humans deal with these scenarios. The primary purpose of soft computing is to achieve tractability, robustness, and low solution cost by utilizing the tolerance for imprecision and uncertainty in order to do so.
The objective of this special session is to bring together a variety of different types of research on topics and issues connected to soft computing. This session will serve as a forum for unifying discussion, which will foster comparisons, extensions, and new applications. In light of this, the purpose of the session is to convene scientists and engineers actively engaged in research and development in order to maximize the potential of this rapidly expanding field.
The following are some of the goals that will be accomplished during this session:
  • To provide a conceptual understanding of the fundamental components of soft computing, including Neural Computing, Fuzzy Logic, Evolutionary Computing, Probabilistic Reasoning, Neural Computing, and Machine Learning;
  • To bring together, through the presentation of papers, discussions, and talks, researchers from all over the world working in the field of soft computing;
  • To investigate the use of soft computing techniques in the context of dealing with real-world applications;
  • To disseminate more recent ideas that have been developed through the combination of soft computing components such as Neuro-Fuzzy systems and Neural Networks;
  • To encourage an integrated view of soft computing techniques and tools with other fields of study such as education, governance, and health care, etc.
The topics of interest include, but are not limited to:
  1. 1. Applications
    • Data/Web Mining;
    • Big data analytics;
    • Data Visualization
    • Decision Support Systems
    • Fault Diagnosis
    • Human-Machine Interface
    • Industrial Electronics / Consumer Electronics
    • Multi-objective Optimization
    • Process Optimization
    • Hybridization of intelligent models/algorithms
  2. 2. Algorithms
    • Evolutionary Computing
    • Swarm Intelligence
    • Memetic Computing
    • Fuzzy Computing
    • Hybrid Methods

Special Session 07: Neuromanagement and Neuromarketing

Prof. Felisa M. Córdova, University San Sebastián, Chile, felisa.cordova@uss.cl

Dr. Hernán Díaz, University Finis Terrae, Chile, hernan.diaz@usach.cl

Dr. Fernando Cifuentes, University Finis Terrae, Chile, fernando.cifuentes@usach.cl

Never before has it been so important to understand human behavior and decision making. The global pandemic and the social impact it has had, changed our values and attitudes as well as the way we see, perceive and interact with the world around us. As a consequence, much knowledge and business assumptions have been challenged, have become obsolete or at least are in need of modifications.
In this context, advances made in both neurobiology and decision-making process, as well as efforts to apply engineering to neurocognitive processes, have led to the development of Neuromanagement, considering the importance to understand human behaviour and its drivers. From the discovery of the neurobiological basis that rule the process of learning and memory, and the development of neuroscience, the amount of knowledge that is being accumulated and comprehended progressively, makes necessary to consider brain functioning and knowledge about how it works by delivering a great and useful value.
Today it is being studied how the human being decides what he wants to do, with the help of emerging technologies involving brain stimulation and analysis, using tools such as electroencephalogram EEG, eye tracking, physiological sensors GSR, pupilometer sensors and other tools to measure human behaviour in the decision making process.
Companies are in the challenging position to translate those scientific and technologic findings into concrete actions which can be implemented in their context. They are recruiting academics and professionals from the areas of health, psychology and engineering, with their media coverage being high. In this way, marketing has been assimilating concepts of cognitive psychology and neuroscience, and incorporating technological tools into its practices. Its applications range from brand design and advertising, political propaganda, through expert advice to society on health issues or the measurement of the affective reactions of customers.
With this session we will highlight how common cognitive biases influence the decision-making process and have an impact on the behaviour of consumers. We will also show how these biases can be studied and understood from a multidisciplinary scope.
It is in our interest to present research work that associates the neurocognitive research of human behavior with empirical findings of neuro management and neuro marketing in the process of decision-making. We have made many applications regarding neuro marketing, which range from having a set of people who will decide what they buy in the supermarket, and see what they base their decisions on, through work teams where the people in each one, have different cognitive profiles to establish what is happening in the solutions they give to creative problems, using either disruptive groups, assimilators or ushers; or different combined types and groups of the specific profile.
Knowing that the focus of neuro marketing has been the stimulus-response paradigm, the actual approach of neurocognitive engineering is looking for medium-and long-term responses to the change in human behaviour as a decision-maker. This means deepening the learning processes as central processes and procedures of human communication, education and culture.
In Academia, the neuroscientific approach has created a reliable toolset to make attitudes and values, attention, perception and cognitive processes visible and measurable in the context of laboratory experiments, but also more recently, in natural interactions. Tools like eyetracking, EEG, biometric sensors and emotion detection, amongst others, allow to uncover otherwise hidden factors in decision making processes and give valuable insights in how to concretely adjust and improve the interaction with processes, products, prices, marketing activities and sales channels.
The main following topics of interest are:
  • New Methodologies, Models, Techniques and Practices in Neuro management and Neuro marketing.
  • Theoretical and practical solutions to capture human physiological activity during decision-making processes and purchase decisions.
  • Systems and Information Technology applications in decision-making processes.
  • Simulation process involved in decision making.
  • Software and systems based on Neuro management and Neuro marketing.
  • Qualitative and quantitative methods used in new applications.
  • Process of learning about customer purchasing preferences.
  • Artificial Intelligence in Neuro management and Neuro marketing.
  • Trends in Neuro management and Neuro marketing.

Special Session 08: Financial and economic sustainability; Modeling for decision-making

Prof. A. M. Karminsky, Higher School of Economics, MGIMO-U, Moscow, Russia, karminsky@mail.ru

Prof. Massimiliano Caporin, University of Padova,Italy, massimiliano.caporin@unipd.it

Prof. M.I. Stolbov, MGIMO-U, Moscow, Russia, stolbov_mi@mail.ru

Financial decisions are the whole show in up-to-date economic processes. To be more effective they require data and analytical & model support. The main aim of this special session will be concentrated at Big Data collection as well as using them for empirical model development mostly connected with sustainable development and ESG approaches.
We propose to consider proposals for such questions as:
  • Construction Big Data information systems in Banking and Financial markets.
  • Creation of Risk Management models for financial decisions and financial and economic sustainability.
  • Formation of rating system in Business and Finance.
  • Credit ratings as creditworthiness measure.
  • ESG ratings and there modeling.
  • Efficiency models to compeer bank and financial companies' usefulness.
  • Evolution of ecosystems.
  • Banking and financial innovations at the FinTech platforms.
  • Financial digitalization of the financial sector and banks.
  • Financial stability assessment and prudential regulation, including ESG sustainability governance.
  • Systemic Risks Assessment including ESG systemic risks.
  • COVID pandemic and systemic risks.

Special Session 09: Decision-Making Behavior and Collaborative Management in Human-Machine Systems

Prof. Yong Shi, Chinese Academy of Sciences, China, yshi@ucas.ac.cn

Prof. Yanzhong Dang, Dalian University of Technology, China yzhdang@dlut.edu.cn

Prof. Xianneng Li, Dalian University of Technology, China, xianneng@dlut.edu.cn

Asso. Prof. Zhaoguang Xu, Dalian University of Technology, China, zhgxu@dlut.edu.cn

The rapid development of artificial intelligence and its deep integration with the national economy have spawned a new form of hybrid intelligence that integrates human wisdom and machine intelligence, forming complex human-machine systems. Unlike traditional management systems that use human or machine as a single decision-making entity, fundamental changes have taken place in human-machine systems in a hybrid intelligence form, which has had a significant impact on traditional management models and decision-making methods.
This special session attempts to discuss the internal mechanism of human-machine behavior from a multidisciplinary perspective, explore new modes of human-machine collaborative management from the system level, and discuss human-machine hybrid decision-making from a dual driven dimension of data and knowledge. The research outcomes would be essentially useful for forming the decision-making behavior and collaborative management theory and methodology of human-machine systems.
This session will cover but not limited to the following topics:
  • Advanced methods of human intelligence and/or machine intelligence
  • Internal mechanisms of human intelligence and/or machine intelligence
  • Collaborative management of human-machine systems
  • Integration methods of human wisdom and machine intelligence
  • Applications of human-machine systems
  • Other human-machine systems related topics

Special Session 10: Green Economy and decarbonisation processes supported by Information Technology and Quantitative Methods

Prof. Luiz F. Autran M. Gomes, Ibmec University Center, luiz.gomes@professores.ibmec.edu.br

Dr. Yarly Q. de Lima, Petrobras, yarly@petrobras.com.br

This session will cover research and industry applications of Information Technology and quantitative decision models to the promotion of renewable energies. Articles presenting models for decision analysis, Artificial Intelligence, Big Data, approaches to ubiquotous computing as well as other tools will be most welcome as long as they relate to problems of renewable electricity and heat, wind, solar, biofuels, and other emerging trends. The emphasis of applications must cover technical, economical, social, and institutional aspects of the Green Economy and decarbonisation processes.

Special Session 11: Empowering Digital Twins with Blockchain: Opportunities and Challenges

Assistant prof. Seyed Mojtaba Hosseini Bamakan, Department of IT Management and Data sciences, Yazd University, Iran smhosseini@yazd.ac.ir

Prof. Qiang Qu, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China, qiang@siat.ac.cn

Prof. Qingshan Jiang, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China, qs.jiang@siat.ac.cn

Industry 4.0 has led to the emergence of distributed technologies, including Digital Twins, which are becoming increasingly popular in various domains. Digital Twins are virtual replicas of physical assets, processes, or systems that can help businesses optimize their operations, enhance efficiency, and reduce costs. However, managing and sharing data securely and efficiently is a significant challenge for digital twin platforms. In this special session, we aim to explore the potential of blockchain technology in empowering digital twins by providing traceable and trustworthy data sharing mechanisms for distributed digital twin platforms. We will also discuss the role of blockchain-based distributed federated learning in digital twins, which enables the collaborative training of machine learning models on decentralized data sources.
The integration of blockchain technology with Digital Twins has the potential to unlock new possibilities in secure and decentralized data sharing and collaboration. Blockchain technology provides a tamper-proof and transparent platform for managing and sharing data, while ensuring data privacy and security. Blockchain technology can also be used to enable smart contracts and decentralized autonomous organizations (DAOs), which can facilitate automated and decentralized decision-making processes.
This session will cover but not limited to the following topics:
  • Web 3 Technologies and Digital Twins
  • Blockchain-based Digital Twin platforms
  • Decentralized Data Management and Sharing in Digital Twins
  • Traceability and Trustworthiness of Data in Digital Twins
  • Blockchain-based Distributed Federated Learning in Digital Twins
  • Smart Contracts for Digital Twin Management
  • Blockchain-based Digital Twin Applications in Industry
  • Interoperability of Blockchain-based Digital Twins
  • Governance and Security of Blockchain-based Digital Twins
  • Blockchain-based Digital Twin architectures and applications
  • Smart contracts and DAOs for Digital Twins
  • Challenges and opportunities in the integration of Digital Twins and blockchain technology
  • Future research directions in this field

Special Session 12: Heterogenous Data based Intelligent Data Analysis

Prof. Ying Liu, University of Chinese Academy of Sciences, China, yingliu@ucas.ac.cn

Prof. Wen-Qin Wang, University of Electronic Science and Technology of China, China, wqwang@uestc.edu.cn

Associate Prof. Hao Shi, Beijing Institute of Technology, China, shihao@bit.edu.cn

Associate Prof. Di Zhao, Institute of Computing Technology, Chinese Academy of Sciences, China zhaodi@ict.ac.cn

Due to the rapid development of hardware and software, a variety of data are generated, collected and analyzed in various real-world applications, including optical images, video, audio, radar signal, infrared, remote sensing images, etc. Furthermore, some applications even utilize multiple types of data from multiple sources, so called heterogenous data, for a single task. In fact, as an emerging technique, heterogenous data analysis is in strong demand in more and more applications, such as remote sensing, security monitoring, robot, etc. where single source or single type of data is not sufficient to solve the problems in the applications. Although data analysis methods for each type of data have been well studied in the past decades, methods for addressing and analyzing heterogenous data are still at the beginning stage.
Therefore, this special session aims at exploring novel methods, algorithms, techniques, or systems that are able to use heterogenous data for intelligent data analysis. We welcome papers on all aspects of heterogenous data analysis, including but not limited to:
  • Data fusion
  • Data alignment
  • Feature selection
  • Feature fusion
  • Methods for heterogenous data analysis
  • Deep learning based models for heterogenous data analysis
  • Real-time data analysis system
  • Novel applications of heterogenous data analysis
Program Committee
  • Xiaofei Zhou, Institute of Information Engineering, Chinese Academy of Sciences
  • Steve Chiu, Idaho State University, USA
  • Yang Gao, SenseTime Co., China
  • Jun Xu, Renmin University of China
  • BingQiang Wang, Sun Yat-sen University, China
  • Ying Liu, University of Chinese Academy of Sciences
  • Wen-Qin Wang, University of Electronic Science and Technology, China,
  • Hao Shi, Beijing Institute of Technology, China
  • Di Zhao, Institute of Computing Technology, Chinese Academy of Sciences

Special Session 13: Intelligent Decision Making and Consensus

Prof. Francisco Javier Cabrerizo, University of Granada, Spain, cabrerizo@decsai.ugr.es

Prof. Juan Antonio Morente-Molinera, University of Granada, Spain, jamoren@decsai.ugr.es

Prof. Ignacio Javier Pérez, University of Granada, Spain, ijperez@decsai.ugr.es

Prof. Enrique Herrera-Viedma, University of Granada, Spain, viedma@decsai.ugr.es

Intelligent decision-making processes are developed by automatic decision-making systems that support individual or organizational decision-making processes using different information technologies (as web and social networks) and artificial intelligence tools (as computational intelligence tools). The intelligent decision-making processes involve the use of preference modelling and consensus processes. The preference modelling deals with the representation and modelled of the preferences provided by the experts in the problems. The fuzzy logic is a computational intelligence tool that provides an adequate framework to deal with the uncertainty presented in the user opinions. The fuzzy preference modelling has been satisfactory applied in intelligent decision-making. On the other hand, consensus is an important area of research in intelligent decision-making. Consensus is defined as a state of mutual agreement among members of a group where all opinions have been heard and addressed to the satisfaction of the group. A consensus reaching process is a dynamic and iterative process composed by several rounds where the experts express, discuss and modify their preferences.
The objective of the proposed session is to highlight the ongoing research on intelligent decision-making, fuzzy preference modelling and consensus processes under uncertainty. Focusing on theoretical issues and applications on various domains, such as global green digital economy, ideas on how to solve consensus processes in intelligent decision-making under fuzzy preference modelling, both in research and development and industrial applications, are welcome. Papers describing advanced prototypes, systems, tools and techniques and general survey papers indicating future directions are also encouraged. Topics appropriate for this special session include, but are not limited to:
  • Fuzzy preference modelling in intelligent decision-making.
  • Intelligent decision-making system applications.
  • Consensus in fuzzy multi-agent decision-making.
  • New models of fuzzy preference modelling.
  • Intelligent decision-making systems for big data.
  • Intelligent decision-making in web.
  • Intelligent decision-making in global green digital economy.
  • Aggregation of preferences.
  • Intelligent decision-making in dynamic environments.

Accepted Workshops

Workshop 01: Quantitative Finance and Risk Management

Prof. Wei Chen, Capital University of Economics and Business, China. ( chenwei@cueb.edu.cn)

Prof. Zhensong Chen, Capital University of Economics and Business, China. ( chenzhensong@cueb.edu.cn)

Prof. Yinhong Yao, Capital University of Economics and Business, China. ( yaoyinhong@cueb.edu.cn)

Prof. Yanxin Liu, Capital University of Economics and Business, China. ( lyxinnn@cueb.edu.cn)

Prof. Xueyong Liu, Capital University of Economics and Business, China. ( liuxueyong@cueb.edu.cn)

With the development of multi-source heterogeneous big data, artificial intelligent methods are being more and more important in various fields within finance. Recently, stock market forecasting, algorithmic trading, risk assessment, portfolio allocation, asset pricing and derivatives market are among the areas where machine learning and deep learning researchers focus on developing models that can provide real-time working solutions for the financial industry. The main purpose of this workshop is to provide researchers and practitioners an opportunity to share the most recent advances in the area of quantitative finance and risk management, to promote in-depth research on frontier theoretical and practical issues in quantitative finance and risk management, to generate new results in this relatively under-researched area, and determine directions for further research. The workshop is interested in topics related to all aspects of quantitative finance and risk management, which includes, but are not limited to, the following:
  • Quantitative investment strategy and technology
  • Quantitative risk management
  • Portfolio optimization and strategy
  • Machine learning based model for financial forecasting
  • Financial complex network
  • Financial distress prediction and bankruptcy prediction

Workshop 02: Risk scenario-based decision making: Methods and Applications

Associate Prof. Weilan Suo, Institutes of Science and Development, Chinese Academy of Sciences, China. ( suoweilan@casisd.cn)

Prof. Jing Zhang, Reserve Security and Emergency Materials Support Center, National Food and Strategic Reserves Administration, Beijing 100834, China. ( olivia_928@msn.com)

Prof. Gang Li, Northeastern University at Qinhuangdao, China. ( ligang@neuq.edu.cn)

Assistant Prof. Mingxi Liu, Institutes of Science and Development, Chinese Academy of Sciences, China. ( liumingxi@casisd.cn)

Prof. Xiaolei Sun, Institutes of Science and Development, Chinese Academy of Sciences, China. ( xlsun@casisd.cn)

Preventing and resolving critical risks has been an important national strategic plan. To response the requirements of the national strategic plan, scientists are exploring new methods, models, and tools to address the issues arising from the various risk scenarios in different fields. Facing the major public emergency, how to identify the multisource risks and clarify the risk evolution mechanism? Facing the urgent public requirements for emergency supplies, how to assess the resilience of emergency support system to respond the multisource risks? Facing the increasing safety threat of critical infrastructures, how to prevent multi-source risks and improve critical infrastructure resilience? Facing the increasing credit demand of real economy, how to conduct credit decision and credit risk management? Facing the increasingly prosperous cryptocurrency market brought about by the rapid development of financial technology, how to look ahead and judge the risks of the volatile digital currency market?
In the process of preventing and resolving critical risks, some general issues on risk management and decision-making have attracted much attention. For example, risk scenarios analysis is conducted from the perspectives of scenario elements, effect mechanism, and potential consequence. For another example, risk characterization is represented in the form of multi-source, interdependency, and dynamicity, which are modeled by some quantification tools. Recently, some intelligence technologies, such as knowledge graph, machine learning (MI), artificial intelligence (AI), and text mining, are used to provide an efficient support for risk data collection and processing, visualize the judgment reference for the risk identification and diagnosis, improve the accuracy of credit decision, and solve the problems on warning and management for financial risks. On the other hand, some application issues derived from the process of preventing and resolving critical risks have been studied, such as scenario-driven resilience assessment models for risk response of emergency support, scenario-based models for critical infrastructure risks, and ML/AI models for credit risks and Fintech risks. The main objective of this workshop is to facilitate the exchange of ideas and knowledge among practitioners, scholars, teachers, and others interested in applications of decision-analysis methods in various risk scenarios.
Topics of interest include, but are not limited to, the following:
  • Risk and emergency decision making
  • Risk scenario analysis and modeling
  • Risk-based intelligence decision analysis
  • Risk data collection and processing
  • Risk identification and diagnosis
  • Mechanism analysis for risk evolution
  • Resilience assessment models for risk response of emergency support
  • Scenario-based models for critical infrastructure risks
  • ML/AI models in finance risk management
  • Credit decisions
  • Credit rating and credit scoring
  • Bayesian risk decision
  • Cryptocurrency market risks
  • Fintech risk management

Workshop 03: The 8th workshop on Big Data and Management Science & Outlier Detection in Finance and Economics

Prof. Aihua Li, Central University of Finance and Economics, China. ( aihuali@cufe.edu.cn)

Prof. Zhidong Liu, Central University of Finance and Economics, China. ( liu_phd@163.com)

Prof. Xiaodong Lin, Rutgers University, USA. ( xiaodonglin@gmail.com)

Prof. Fan Meng, Peking University, China. ( mengfan@pku.edu.cn)

With the development of information technology, more and more data are stored in many fields and different industries. Big data is not only a definition for data but also for the technology and idea to deal with big data. For decision makers, there are still drowned in data but lack of knowledge. Management Science is a subject to solve the problem in management with qualitative and quantitative method. New idea and method in big data put new energy for management science. Thus, there are new methods and technology in the field big data and management science in recent years.

Outlier detection in economic and financial markets is a problem faced by managers. In the era of big data, data from various sources are collected to analyze outlier detection, and data fusion methods are often used. Therefore, outlier detection based on data fusion is widely concerned. This workshop focuses on how to detect abnormal pattern with data fusion especially in finance and economics fields. In addition, theoretical system and methods of outlier detection would be needed to be proposed. This topic includes outlier detection theory, method and application for financial and economic data based on domain knowledge, data fusion, and empirical analysis.

The topics and areas include, but not limited to:
  • Data fusion method and application in financial outlier detection problem;
  • Data fusion method and application in economics outlier detection problem;
  • Outlier detection based on classification method;
  • Outlier detection based on clustering method;
  • Outlier detection based on domain knowledge;
  • Data preprocessing method for data streams;
  • Domain knowledge and risk management in finance;
  • Data mining and knowledge discovery in finance;
  • Outlier detection method with data fusion in other field;
  • Quantitative management and decision making in finance;
  • Method, model and application in big data and management science;

Workshop 04: The 10th Workshop on Optimization-based Data Mining

Prof. Yingjie Tian, Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science, China. ( tyj@ucas.ac.cn)

Prof. Zhiquan Qi, Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science, China. ( qizhiquan@ucas.ac.cn)

Dr. Saiji Fu, School of Economics and Management, Beijing University of Posts and Telecommunications, China. ( fusaiji@bupt.edu.cn)

Prof. Yong Shi, Chinese Academy of Sciences Research Center on Fictitious Economy and Data Science, China. College of Information Science and Technology, University of Nebraska at Omaha, USA. ( yshi@ucas.ac.cn)

The fields of data mining and mathematical programming are increasingly intertwined. Optimization problems lie at the heart of most data mining approaches. For last several years, the researchers have extensively applied quadratic programming into classification, known as V. Vapnik’s Support Vector Machine, as well as various applications. However, using optimization techniques to deal with data separation and data analysis goes back to more than many years ago. According to O. L. Mangasarian, his group has formulated linear programming as a large margin classifier in 1960’s. In 1970’s, A. Charnes and W.W. Cooper initiated Data Envelopment Analysis where a fractional programming is used to evaluate decision making units, which is economic representative data in a given training dataset. From 1980’s to 1990’s, F. Glover proposed a number of linear programming models to solve discriminant problems with a small sample size of data. Then, since 1998, the organizer and his colleagues extended such a research idea into classification via multiple criteria linear programming (MCLP) and multiple criteria quadratic programming (MQLP), which differs from statistics, decision tree induction, and neural networks. So far, there are more than 100 scholars around the world have been actively working on the field of using optimization techniques to handle data mining and web intelligence problems. This workshop intends to promote the research interests in the connection of optimization, data mining and web intelligence as well as real-life applications.

Program Committee


Workshop 05: The 8th Workshop on Scientific Data Analysis and Decision Making

Associate Prof. Dengsheng Wu, Institutes of Science and Development, Chinese Academy of Sciences, China. ( wds@casipm.ac.cn)

Prof. Jianping Li, School of Economics and Management, University of Chinese Academy of Sciences, China. ( ljp@ucas.ac.cn)

As E-science has emerged as a persistent and increasingly large part of the research enterprise, scientists are exploring new roles, services, staffing, and resources to address the issues arising from this new mode of research. Scientists use computer modeling and simulation programs to test and produce new theories and experimental techniques, often generating and accumulating vast amounts of data. Ideally, that data could be shared with other scientists, for re-use and re-analysis, ultimately speeding up the process of scientific discovery. The collection and utilization of scientific data are the two primary features that characterize e-Science. The scientific data are generated by different aspects and departments in the management activities of research institutions and are decentralized-managed and separated-stored, which generates the difficult to share and manage the scientific data. Furthermore, the global sharing of data has promoted interdisciplinary teamwork on complex problems and has enabled other researchers to use data for different purposes. Recently, the scientific data analysis for ecological animal husbandry has attracted much attention. Some empirical research, such as the big data management platform for ecological animal husbandry in Qinghai Province China, has generated significant benefits. The main objective of this workshop is to provide researchers and practitioners an opportunity to share the most recent advances in the area of data science and decision analysis for e-science, especial the ecological animal husbandry. The workshop aims to create a communication platform for researchers to share recent and significant developments in the general area.

Topics of interest include, but are not limited to, the following:
  • Metadata standard for scientific data
  • Scientific data quality analyzing
  • Scientific data integration and sharing
  • ETL process for scientific data
  • Disambiguation of scientific data
  • Scientific data visualization
  • Decision analysis modeling from scientific data
  • Network analysis from scientific data
  • Data integration for ecological animal husbandry
  • Analysis modeling for ecological animal husbandry
  • Bibliometrics analysis from scientific data
  • Scientometrics analysis from scientific data

Workshop 06: Business Intelligence

Prof. Chonghui Guo, Institute of Systems Engineering, Dalian University of Technology, Dalian, China. ( dlutguo@dlut.edu.cn)

Associate Prof. Kun Guo, Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, China; Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, China. ( guokun@ucas.ac.cn)

Prof. Yong Shi, Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, China; Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, China. ( yshi@ucas.ac.cn)

With the development of big data and artificial intelligence technology, the digital transformation of enterprises has become an inevitable trend. Business intelligence plays an important role in discovering the knowledge behind the data and providing the management decision support for enterprises. Especially in the era of big data, how to use the new artificial intelligence technology to improve the quantitative management ability of enterprises has become the key point to the digital transformation of enterprises and the improvement of management decision-making ability.

The session is meant to include papers that contain recent results obtained by research teams from academia and industry concerning but not limited to the following topics:
  • New Algorithm of Business intelligence
  • Business intelligence based on big data
  • Business intelligence for enterprise management
  • Business intelligence for financial management
  • Business intelligence in complexity system

Chonghui Guo is a professor of the Institute of Systems Engineering, Dalian University of Technology, Dalian, China. He is the Head of Center for Big Data and Intelligent Decision Making, Dalian University of Technology. He received a B.S. degree in mathematics from Liaoning University in 1995, an M.S. degree in operational research and control theory in 1999, and a Ph.D. degree in management science and engineering from Dalian University of Technology in 2002. He was a postdoctoral research fellow in the Department of Computer Science at Tsinghua University, Beijing, China. His studies concentrate on data mining and knowledge discovery. He has published over 160 peer-reviewed papers in academic journals and conferences, besides 5 text-books and 2 monographs. He has been the principal investigator on over 20 research projects from the government and the industry.

Kun Guo is an associate professor in Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences and Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences. She received her PhD in management science and engineering from UCAS in 2011. Her research covers financial market, big data application in economy analysis, fictitious economy and complex system. She has published over 30 peer-reviewed journal articles on Journal of Forecasting, Industrial Management & Data Systems, Applied Economics, Finance Research Letters, Knowledge-Based Systems, etc. She is also secretary general of Business Intelligence Society, Chinese Academy of Management.

Yong Shi is the Director of Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences and Deputy Director of National Engineering Laboratory of Big Data Analysis System. He is State Council Counsellor, Academician of Academy of Sciences for Developing Countries and President of International Academy of Information Technology and Quantitative Management (IAITQM). His major research areas include data mining, big data analysis and management decision-making.

Workshop 07: Machine Learning and Intelligent Awareness

Prof. Xiaofei Zhou, Chinese Academy of Sciences, China. ( zhouxiaofei@iie.ac.cn)

Prof. Jia Wu, Macquarie University, Australia. ( jia.wu@mq.edu.au)

With the arrival of the era of big data, machine learning methods are being more and more important in the fields of artificial intelligence and related applications, such as natural language, multimedia, social networks, etc. Intelligent sensing and analysis based on big data is the core task of artificial intelligence in the future. This workshop aims to provide an international forum to discuss data intelligent mining methods, technologies and systems for computational science, artificial intelligence, knowledge engineering and management decision discipline fields.

The topics covered will include, but not limited to:
  • Machine Learning and data mining technique: classification, clustering, regression etc.
  • Neural network and deep learning
  • Graph data and networks
  • Knowledge extraction and expression
  • Text analysis and nature language process
  • Knowledge graph
  • Link and Graph Mining
  • Social agents for intelligent awareness
  • Recommendation Systems
  • Visualization technologies and analytics
  • Multimedia and Multi-structured Data Analysis
  • Intelligent knowledge in management decision
  • Biometric identification
  • AI Security
  • Algorithms and Systems for Social Media Analysis
  • Mobility and Social Network Data
  • Information filter
  • Object recognition
  • Distributed and Parallel Algorithms
  • Big Data Search Architectures, Scalability and Efficiency
  • Social Data Acquisition, Integration, Cleaning, and Best Practices
  • Cloud/Grid/Stream Data Mining

Workshop 08: Modelling and optimization of complex systems with application to decision making

Prof. Gang Kou, School of Business Administration, Southwestern University of Finance and Economics, China ( kougang@swufe.edu.cn)

Prof. Hui Xiao, School of Statistics, Southwestern University of Finance and Economics, China. ( msxh@swufe.edu.cn)

With the rapid development of technology, both the modern engineering systems and operation management systems are becoming more and more complex. The increasing complexity brings new challenges on modelling and analysing these systems. These challenges include but not limited to the complexity of system structure, the interaction among internal structures, the dynamic nature of system behaviours, and the uncertain external impacts. In the past decades, a variety of analytical models are proposed to model the complex systems. Besides, simulation is also frequently used to model the system dynamic behaviours and to evaluate the system performance. In the era of artificial intelligence, machine learning techniques provide new approaches for modelling and analysing complex systems.

This workshop aims to present state-of-the-art research on modelling and optimization of complex systems and its application to decision making. Having this in mind, we solicit submissions in all aspects of approaches from analytical methods and simulation approaches to machine learning techniques.

Topics of interest in this workshop include but not limited to:
  • Reliability modelling and optimization of complex systems
  • Maintenance decision for complex systems
  • Quality control of complex products
  • Multi-attribute decision making for complex systems
  • Simulation modelling and optimization
  • Multi-objective optimization
  • Mathematical theory for modelling uncertainty
  • Decision making under uncertainty
  • Group decision making
  • Complex supply chain systems
  • Defense and attack of complex systems
  • Data driven approaches to complex systems
  • Machine learning in complex systems

Workshop 09: Intelligent Knowledge Management

Prof. Jifa Gu, Academy of Mathematics and Systems Science of Chinese Academy of Sciences, China. ( jfgu@amss.ac.cn)

Prof. Lingling Zhang, School of Economics and Management, University of Chinese Academy of Sciences, China. ( zhangll@ucas.ac.cn)

Knowledge or hidden patterns discovered by data mining from large databases has great novelty, which is often unavailable from experts’ experience. Its unique irreplaceability and complementarity has brought new opportunities for decision-making and it has become important means of expanding knowledge bases to derive business intelligence in the information era. The challenging problem, however, is whether the results of data mining can be really regarded as “knowledge”. To address this issue, the theory of knowledge management should be applied.

Intelligent Knowledge Management is the management of how rough knowledge and human knowledge can be combined and upgraded into intelligent knowledge. Intelligent Knowledge Management aims to bridge the gap between these two fields. In addition, Knowledge Graph can express knowledge through visualization method and explore and analyze the relationship between knowledge, reveal the dynamic development law of knowledge field, and then realize knowledge sharing and reusing, which can provide rich semantic knowledge network and auxiliary information source for knowledge management and knowledge recommendation. This study not only promotes more significant research beyond data mining, but also enhances the quantitative analysis of knowledge management on hidden patterns from data mining.

In order to promote intelligent knowledge management research, we want to organize a special workshop dedicated to the topic of “Intelligent Knowledge Management” under the ninth International Conference on Information Technology and Quantitative Management (ITQM 2023).

The main purpose of this workshop is to provide researchers and practitioners an opportunity to share the most recent advances in the area of data mining, expert mining, knowledge graph, recommendation system and intelligent knowledge management, to generate new methods to determine directions for further research. Papers should present modeling approaches/perspectives to intelligent knowledge. The workshop is interested in topics related to all aspects of expert mining and intelligent knowledge. Topics of interest include, but are not limited to, the following:
  • Intelligent Knowledge Management
  • Wuli-Shili-Renli System Methodology (WSR)
  • Knowledge Synthesis
  • Expert Mining
  • Knowledge Extraction
  • Knowledge Graph
  • Equipment Health Management (EHM)
  • Problem-driven Knowledge Management
  • Knowledge Graph Embedding (KGE)
  • Recommendation Systems
  • Link Prediction
  • Large-scale Engineering Projects Management
  • Data and Knowledge Management Related to the Large-scale Scientific Facilities
  • Knowledge Presentation and Visualization
  • Extension
  • Knowledge Sharing
  • Knowledge Spillover
  • Knowledge Evaluation
  • KDD Process and Human Interaction
  • Intelligent Systems and Agents
  • Knowledge Management Support Systems

Workshop 10: Credit Risk Evaluation and Management

Prof. Jing Gu, Sichuan University, China. ( gj0901@scu.edu.cn)

Credit risk, with the characteristics of potential, cumulative and destructive, is the main risk undertaken by the financial institution, investor and counterparty. Credit risk management, one of the core issues of financial management, is the biggest subject in the financial industry must be considered in any business model. It is necessary to evaluate credit risk, so as to effectively manage and control it. In today's complex global social-economic environment, credit risk evaluation and management has become one of the core issues of academic circles at home and abroad.

In order to promote the development of credit risk evaluation and management, we organize a special workshop dedicated to the topic of “credit risk evaluation and management” under The 10th International Conference on Information Technology and Quantitative Management (ITQM 2023) (http://www.itqm-meeting.org/2023/). The main purpose of this workshop is to provide researchers and practitioners an opportunity to share the most recent advances in the area of credit risk evaluation and management, and determine directions for further research. Papers should present modeling approaches/perspectives to credit risk evaluation and management.
Potential research topics include, but are not limited to the following:
  • Credit risk evaluation
  • Credit risk forecasting with big data
  • The influencing factors of credit risk
  • The contagion effect of credit risk
  • Credit risk management
  • Associated credit risk evaluation and management
  • Credit risk research from the perspective of network

Workshop 11: The 13th International Workshop on Computational Methods in Energy Economics

Prof. Lean Yu, School of Business, Sichuan University, China. ( yulean@amss.ac.cn)

Prof. Kaijian He, College of Tourism, Hunan Normal University, China. ( kaijian.he@my.cityu.edu.hk)

As is known to all, energy economics is a subfield of economics that focuses on energy relationships as the foundation of all other relationships. The field can arise from a number of disciplines, including economic theory, financial economics, computational economics, statistics, econometrics, operational research and strategic modeling. A wide interpretation of the subject includes, for example, issues related to forecasting, financing, pricing, investment, development, conservation, policy, regulation, security, risk management, insurance, portfolio theory, taxation, fiscal regimes, accounting and the environments. In these listed issues there are a large number of computational problems to be solved for the energy systems, particular for energy risk measurement and management. This will be the eleventh workshop for such a subject that provides a premier and open forum for the dissemination of innovative computational methods as well as original research results in energy economics and energy risk management.

In order to provide an academic exchange platform, the First International Workshop on Computational Methods in Energy Economics (CMEE 2007) was held in Beijing on May 27-30, 2007. Subsequently, the Second, Third, Fourth, Fifth, Sixth, Seventh, Eighth, Ninth, Tenth, Eleventh International Workshop on Computational Methods in Energy Economics (CMEE 2008, CMEE 2009, CMEE 2010, CMEE 2011, CMEE 2012, CMEE 2013, CMEE 2015, CMEE 2016, and CMEE 2017, and CMEE2021) were held in Nanjing, Sanya, Huangshan, Kunming, Harbin, rio De Janeiro (Brazil), Asan (Korea), New Dehli (India), and Chengdu on June 27-30, 2008, April 24-26, 2009, May 28-31, 2010, April 15-19, 2011, June 24-26, 2012, May 16-18, 2013, July 21-24, 2015, August 16-18, 2016, December 8-10, 2017 and July 9-11, 2021. To promote the idea-exchange and discussion of this field, the Eleventh International Workshop on Computational Methods in Energy Economics (CMEE 2023) will be held in Beijing, China, December 9-11, 2023. The organizers solicit all interested academic researchers and industrial practitioners to submit their recent research results to this workshop within the scope of the following topics.

The workshop will provide an open forum for research papers concerned with the computational problems on energy economics and energy risk management, including economic and econometric modeling, computation, and analysis issues in energy systems.

The workshop will focus on, but not limited to, the following topics:
  • Forecasting models for energy prices (oil, coal, gas, electricity)
  • Pricing models in energy markets (mean reversion, jump diffusion)
  • Investment analysis models in energy projects (portfolio theory)
  • Econometric modeling for energy demands
  • Energy and environment policy modeling
  • Modeling strategic behavior for energy security
  • Hybrid energy-economy models for energy policy simulation
  • Statistical analysis of energy cost, energy consumption and economic growth
  • Energy risk management (risk measurement, hedging strategy and instruments)

Workshop 12: Data Essentialization and Financial Innovation

Prof. Jianping Li, University of Chinese Academy of Sciences, China, ( ljp@ucas.ac.cn)

Prof. Rongda Chen, Jiaxing University, China, ( rongdachen@zufe.edu.cn)

Prof. Jin Li, Xi'an Jiaotong University, China, ( jinlimis@xjtu.edu.cn)

Dr. Jun Hao, University of Chinese Academy of Sciences, China. ( haojun@ucas.ac.cn)

With the rapid development of emerging information technology and digital economy, data has become an important element in the national governance system. It is of strategic significance to promote data security governance in a coordinated manner. At the same time, data elements are increasingly becoming an important driving force for high-quality development of the financial industry. Recently, data essentialization and trading, data security and risk assessment, data asset pricing, data governance policy evaluation and financial innovation are among the areas where management science and financial engineering researchers focus on. The main purpose of this workshop is to provide researchers and practitioners an opportunity to share the most recent advances in the area of data essentialization and financial innovation. This workshop aims to promote in-depth research on theoretical and practical issues towards data governance and financial innovation, to generate new insights in this relatively under-researched area, and to propose meaningful future research directions. The workshop is interested in topics related to all aspects of data essentialization and financial innovation, which includes, but are not limited to, the following:

The workshop will focus on, but not limited to, the following topics:
  • Data elements and digital finance
  • Data elements and fintech innovation
  • Marketization of data elements and digital transformation of finance
  • Data elements and industrial development
  • Data pricing and accommodation trading mechanism
  • Data security and intelligent monitoring model
  • Data circulation and risk management
  • Intelligent evaluation of data governance policies
  • System complexity of data governance
  • Theories and methods of data element governance
  • Tracking of cutting-edge technologies in data element market

Workshop 13: FinTech and Quantitative Financial Management

Prof. Ping Li, Beihang University, China, ( liping124@buaa.edu.cn)

Prof. Ling Zhang, Guangdong University of Finance, China, ( zhangl99@gduf.edu.cn)

Associate Prof. Yun Shi, East China Normal University, China, ( yshi@fem.ecnu.edu.cn)

Assistant Prof. Yang Deng, Huazhong University of Sci. & Tech., China. ( dengyang@hust.edu.cn)

The continuous innovation of artificial intelligence, blockchain, cloud computing, big data and other cutting-edge technologies expand the width and depth of FinTech development and quantitative finance. With technology-enabled and financial innovation becoming the basic features of FinTech, finance and technology are highly integrated and developed. To innovate financial services with technology and accelerate the deep integration of finance and technology has become a global consensus. The products, services and business models in the financial industry are also becoming increasingly complex and diverse, which brings new opportunities and challenges to the micro-financial enterprise operations and the macro-financial supervision.

In this context, in order to promote the research related to quantitative financial management, we organize a special workshop dedicated to the topic of “FinTech and Quantitative Financial Management” under the 10th International Conference on Information Technology and Quantitative Management (ITQM 2023) (http://www.itqm-meeting.org/2023/). The main purpose of this workshop is to provide researchers and practitioners with an opportunity to share the most recent advances in the area of FinTech and Quantitative Financial Management and determine directions for further research, including economic and econometric modelling, computation, and empirical analysis issues in financial management.

The workshop will focus on, but not limited to, the following topics:
  • Forecasting models and methodologies for financial asset pricing
  • Financial risk measurement and management
  • FinTech and investor behavior
  • FinTech and financial ReguTech
  • Fintech and economic development
  • Risk contagion across financial institutions and financial markets
  • Digital economics and digital finance
  • New technologies and methods in FinTech for corporate governance and portfolio theory, such as blockchain, machine learning, artificial intelligence, etc.
  • Fintech in diverse scenarios, such as inclusive finance, micro and small enterprise financing, supply-chain finance, financial services based on digital platforms, etc.

Workshop 14: Applications of Deep Learning and Reinforcement Learning

Associate Prof. Haifeng Li, Central University of Finance and Economics, China, ( mydlhf@cufe.edu.cn)

Deep learning and reinforcement learning have been widely focused by researchers. In the recent few years, they supply enough power to change the society, such as AlphaZero, AlphaFold, ChatGPT, and they will continue to deeply improve the manner of consideration and action in the future. This workshop will focus on the topic of “Applications of Deep Learning and Reinforcement Learning”, and provide participants with a comprehensive understanding of the applications of deep learning and reinforcement learning. The target audience for the workshop includes researchers, practitioners, and students who are interested in learning about the latest developments in the field of deep learning and reinforcement learning and their applications in various industries. We anticipate that the workshop will attract a diverse range of participants from various industries, including healthcare, finance, robotics, and natural language processing.

Potential research topics include, but are not limited to the following:
  • Deep learning and reinforcement learning fundamentals
  • Applications of deep learning and reinforcement learning in healthcare
  • Applications of deep learning and reinforcement learning in finance
  • Applications of deep learning and reinforcement learning in robotics
  • Applications of deep learning and reinforcement learning in natural language processing

Workshop 15: Operational Research and Artificial Intelligence

Assistant Prof. Ruizhi Zhou, Institute of Operations Research and Information Engineering, Beijing University of Technology, Beijing, China, ( zhourz@bjut.edu.cn)

Assistant Prof. Pei Quan, College of Economics and Management, Beijing University of Technology, Beijing, China, ( quanpei@bjut.edu.cn)

Prof. Lingfeng Niu, Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, China, ( niulf@ucas.ac.cn)

New real world applications of artificial intelligence and machine learning have shown that popular methods may appear to be too simple and restrictive. Mining more complex, larger and generally speaking “more difficult” data sets pose new challenges for researchers and ask for novel and more complex approaches. We organize this workshop where we want to promote research and discussion on more complex and advanced methods for the particularly demanding operational research and machine learning problems. Although we welcome submissions concerning methods based on different principles, we also expected to see new research in it about the use of optimization techniques. The new challenges emerging in artificial intelligence are definitely more complex than traditional ones and they could result in more difficult non-convex optimization formulations. We would like to focus interest of artificial intelligence community on various challenging issues which come up while using complex methods to deal with the difficult machine learning problems.

Potential research topics include, but are not limited to the following:
  • Optimization methods for machine learning
  • Combined classifiers for complex learning problems
  • Discussion on using artificial intelligence methods to solve problems in operational research
  • Application of optimization in modeling and algorithm design of artificial intelligence
  • New methods for constructing and evaluating on-line recommendation
  • Mining spatial data and images
  • Complex data representation learning
  • Artificial intelligence driven combinational optimization

Workshop 16: Computing power, data, models, and applications of the digital economy era

Associate Prof. Biao Li, Southwestern University of Finance and Economics, China, ( biaoli@swufe.edu.cn)

Assistant Prof. Jia Chen, Southwestern University of Finance and Economics, China, ( chenjia@swufe.edu.cn)

Assistant Prof. Tie Li, University of Electronic Science and Technology of China, China, ( Lteb2002@uestc.edu.cn)

As the digital economy era arrives, computing power, data, models, and applications have become important factors driving digital economic development. In this era, computing power can help us quickly process large amounts of data, gain insights into business opportunities, and make optimal decisions. Data collection and analysis have also become simpler and more efficient, allowing us to extract more valuable information.At the same time, the application of models is becoming more and more extensive. The development of technologies such as artificial intelligence, deep learning, and machine learning enables many companies and organizations to automate data analysis, find potential business opportunities, and predict market trends. This not only improves business competitiveness but also reduces human and time waste. In summary, in the digital economy era, computing power, data, models, and applications are key elements driving the development of the digital economy. Through their continuous innovation and application, we can better grasp business opportunities, improve efficiency and reduce waste, and promote the stable development of the digital economy. The main purpose of this workshop is to provide researchers and practitioners an opportunity to share the most recent advances in the area of digital economy era, to promote in-depth research on frontier theoretical and practical issues , to generate new results in this relatively under-researched area, and determine directions for further research. The workshop is interested in topics related to all aspects of digital economy era, which includes, but are not limited to, the following:

  • Data mining and intelligent analysis
  • Artificial intelligence and machine learning
  • Cybersecurity and privacy protection
  • Blockchain technology
  • Digital marketing and user experience
  • Financial distress prediction and bankruptcy prediction