Proposal: Research on Human-Finance Interaction (HFI)
Introduction:
We propose conducting research on Human-Finance Interaction (HFI) to address the limitations of human decision-making in the context of financial markets. This research aims to compensate for the inherent flaws in human judgment, particularly irrational and emotional biases that can lead to suboptimal financial decisions. By understanding and incorporating the predictable aspects of human behavior, we seek to develop a logical thinking framework that can enhance financial, investment, and economic rationality. Furthermore, this research can contribute to improving mental health by investigating the reasons behind online conflicts and their impact on individuals.
Objectives:
The primary objectives of the HFI research project are as follows:
- Develop a logical thinking framework to mitigate the influence of irrational and emotional biases on financial decision-making.
- Investigate the impact of human behavior as an animal on financial markets and economic outcomes.
- Identify and analyze the reasons for online conflicts and their effects on individual mental health.
Methodology:
The research will be conducted through a combination of empirical studies, data analysis, and theoretical frameworks. The following approaches will be employed to achieve the stated objectives:
Empirical Studies:
- Conduct surveys and interviews to gather data on human decision-making processes, emotional biases, and the impact of animal behavior on financial outcomes.
- Analyze real-world financial data to identify patterns and correlations between human behavior and market trends.
Data Analysis:
- Utilize advanced statistical and machine learning techniques to analyze large datasets and identify behavioral patterns and biases.
- Develop predictive models to forecast market trends based on human behavior indicators.
Theoretical Frameworks:
- Develop a logical thinking framework that integrates behavioral economics, psychology, and finance to enhance financial decision-making.
- Conduct theoretical research on the impact of online conflicts and their influence on mental health, drawing from microeconomic principles.
Expected Outcomes:
The proposed research is expected to yield the following outcomes:
Financial Decision-Making:
- Publication of 12 papers at international conferences, highlighting the research findings on human behavior and its impact on financial decision-making.
- Publication of 2 research papers in reputable academic journals, further contributing to the existing body of knowledge.
- Development of 2 publications aimed at disseminating the logical thinking framework to a broader audience, including policymakers, financial institutions, and individuals.
Impact:
The research on HFI is expected to have significant impact in the following areas:
Financial Industry:
- Enhanced financial decision-making processes through the adoption of the logical thinking framework.
- Reduction of emotional biases and irrational judgments in investment and financial strategies.
- Improved risk management practices based on a deeper understanding of human behavior.
Individuals and Society:
- Improved individual financial well-being by empowering individuals with rational decision-making tools.
- Enhanced mental health by raising awareness of the negative consequences of online conflicts and encouraging more rational behavior in digital interactions.
- Creation of a more logical and rational society, fostering Japanese 2.0 armed with logic and improved economic rationality.
Adding Math Fields
Partial Differential Equations (PDE):
We will employ PDEs to model and analyze the dynamics of human behavior in financial markets. By formulating PDE-based optimization problems, we can capture the influence of emotions, biases, and irrationality on financial decision-making. We will develop numerical methods and algorithms to solve these PDEs and gain insights into the impact of human behavior on market trends and outcomes.
Directed Graphs:
Utilizing directed graphs, we will represent complex networks of financial interactions and decision-making processes. By analyzing the flow of information, sentiments, and financial resources within the network, we can gain a deeper understanding of the factors influencing human behavior. We will develop graph-theoretic algorithms to optimize decision-making strategies and resource allocation, aiming to improve financial outcomes based on the directed graph structure.
Game Theory:
Applying game-theoretic models, we will study the interactions between humans and the financial market. We will analyze strategic behavior, risk aversion, cooperation, and information asymmetry to capture the dynamics of decision-making. By developing game-theoretic optimization frameworks, we aim to identify equilibrium strategies that enhance decision-making in financial contexts, considering the interplay between human behavior and market dynamics.
Pure Logic:
We will utilize pure logic to formalize and reason about human decision-making in finance. By developing logical frameworks, we can mitigate irrational biases, emotional judgments, and cognitive limitations. We will explore the foundations of logical reasoning and evidence-based analysis to enhance financial decision-making processes, fostering more rational and informed choices.
Set Theory:
Applying set theory, we will define and analyze sets of possible financial outcomes and decision variables. By utilizing set-theoretic techniques, we can model and optimize financial constraints and objectives. This will allow us to analyze feasible regions, decision boundaries, and optimize financial decision-making, considering the inherent biases and complexities of human behavior.
Category Theory:
Incorporating category theory, we will investigate the relationships between human behavior and financial outcomes. By studying categorical structures and transformations, we aim to gain insights into the interconnections between different aspects of finance and human decision-making. Developing categorical frameworks will provide a holistic understanding of the impact of human behavior on financial systems, enabling us to optimize decision-making processes.
Optimization:
We will leverage optimization techniques, such as linear programming, nonlinear programming, and metaheuristic approaches, to enhance financial decision-making. By developing optimization algorithms, we can optimize financial strategies while considering insights from human behavior models. We will analyze the convergence, complexity, and sensitivity of these algorithms to ensure effective and efficient decision-making processes.
Incorporating modern technologies, such as Deep Learning (DL), Machine Learning (ML), blockchain, and Generative Pre-trained Transformer (GPT), we will utilize large-scale financial datasets to gain insights into human decision-making. DL and ML techniques will enable us to identify patterns, trends, and potential biases in human behavior. Blockchain technology will ensure transparency, security, and traceability in financial transactions, mitigating risks associated with biases and fraudulent activities. GPT models will assist individuals in making rational financial choices by providing unbiased and evidence-based information.
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(Research paper submission schedule )
Kiara R&D team will proceed “Human Finance Interaction” research project by submitting the papers to the following international conferences.
2023 roadmap
(0)IIAI AAI about financial engineering and AI - Our paper URL
(1)ICEBI about internet business
(2)IEEE ICA about agent system
(3)AIMC about psychology
(0)14th International Congress on Advanced Applied Informatics
https://iaiai.org/conference/aai2023/
IIAI International Conference on Smart Computing and Artificial Intelligence (IIAI SCAI) focuses on Smart Computing and Artificial Intelligence based applications, technologies and theory. AI and Smart Computing can play a key role in addressing environmental, economic, and social challenges in real life. Developments of Smart Computing and Artificial Intelligence fields are key aspects of solving the social problems. In order to provide solutions in such complex problems, research of AI and smart computing have focused on different AI and smart computing technologies including search, decision theory, Information retrieval, Agent technologies, optimization and predicting and learning methods. The applications of smart computing and AI could include e-commerce tools, decision-making support tools, collaboration tools, e-commerces as well as knowledge discovery and learning tools.
The IIAI International Conference on Smart Computing and Artificial Intelligence (IIAI SCAI) will create an international forum for researchers and participants to exchange new ideas and practical experience in the areas of smart computing and artificial intelligence. The conference provides an opportunity to present and observe the latest research, results, and ideas in these areas.
The conference will cover a broad set of research topics including, but not limited to:
- Applications of AI
- Autonomous Systems
- Agents
- Bayesian networks
- Bioinformatics
- Bio-inspired intelligence
- Cognitive systems
- Constraint satisfaction
- Data mining and knowledge discovery
- Decision theory
- Evolutionary computation
- Games and interactive entertainment
- Human-Computer Interaction
- Information retrieval and extraction
- Knowledge acquisition and ontology
- Knowledge representation
- Machine learning
- Multimedia and arts
- Multimodal interaction
- Natural language processing
- Neural networks
- Ontologies & Semantic Web
- Planning and scheduling
- Probabilistic inference
- Reasoning
- Robotics
- Search
- Text/Web/Data mining
- Task Planning & Execution
(1)2023 7th International Conference on E-Business and Internet (ICEBI 2023)
October 20-22, 2023 in Singapore
ICEBI 2023 will provide an effective platform for professionals, scientists, engineers, educators, students, and researchers worldwide to share and exchange their scientific ideas, views, innovations and experiences in the vast areas of E-Business in Software Engineering, E-Business in Big Data and Internet of Things (IoT) with fellow researchers and participants. The conference features a dynamic program incorporating a range of academic, technical and industry related keynote speakers, oral and poster presentations.
(2)IEEE ICA 2022 : The 6th IEEE International Conference on Agent
https://zhcaonctu.wixsite.com/ieee-ica-2022
We envision a future society where computational agents and humans (physical agents) live, work and snuggle together in harmony. This harmonious collaboration is of paramount importance in the next-generation society. IEEE ICA2022 will call for papers related to, but not limited to, the following topics:
- Decision making techniques
- Multi-agent communication and interaction protocols
- Negotiation and argumentation
- Game theory and auctions
- Cooperation, collaboration and coordination mechanisms
- Self-organization and self-adaptation
- Computational reasoning
- Complex systems and system dynamics
- Collective intelligence
- Systems designs, development and implementation
- Human and multi-agent systems interaction
- Social science, humanities, ethical, legal, and social issues
(3)AIMC 2023 : Annual International Multidisciplinary Conference 2023
https://www.jc.um.edu.mt/aimc/callforpapers
We invite presentations of a multidisciplinary nature which may be related, but not limited to, the following areas of knowledge and research:
Social & Human Sciences
Arts & the Humanities
Education
Business
Technology
Sustainability & the Environment
Medical & Health Sciences
Law & Human Rights
Maths & Physics