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Behavioral economics and artificial intelligence (AI) have been two rapidly growing fields of research over the past few years. While behavioral economics aims to combine concepts from psychology, sociology, and neuroscience with classical economic thoughts to understand human decision-making processes in the complex economic environment, AI on the other hand, focuses on creating intelligent machines that can mimic human cognitive abilities such as learning, problem-solving, decision-making, and language understanding. The intersection of these two fields has led to thrilling research theories and practical applications. This study provides a bibliometric analysis of the literature on AI and behavioral economics to gain insight into research trends in this field. We conducted this bibliometric analysis using the Web of Science database on articles published between 2012 and 2022 that were related to AI and behavioral economics. VOSviewer and Bibliometrix R package were utilized to identify influential authors, journals, institutions, and countries in the field. Network analysis was also performed to identify the main research themes and their interrelationships. The analysis revealed that the number of publications on AI and behavioral economics has been increasing steadily over the past decade. We found that most studies focused on customer and consumer behavior, including topics such as decision-making under uncertainty, neuroeconomics, and behavioral game theory, combined mainly with machine learning and deep learning techniques. We also identified several emerging themes, including the use of AI in nudging and prospect theory in behavioral finance, as well as undeveloped themes such as AI-driven behavioral macroeconomics. The findings suggests that there is a need for more interdisciplinary collaboration between researchers in behavioral economics and AI. We also suggest that future research on AI and behavioral economics further consider the ethical implications of using AI and behavioral insights in decision-making. This study can serve as a valuable resource for researchers interested in AI and behavioral economics.INDEX TERMS Artificial intelligence; behavioral economics; behavioral finance; consumer behavior; investor behavior; decision making; neuroeconomics; machine learning; bibliometric analysis I. INTRODUCTIONB EHAVIORAL economics is a subfield of economics that examines the decision-making processes of individuals and groups and how they affect economic outcomes. It is an interdisciplinary field that combines elements of psychology, sociology, neuroscience, and economics to provide a more complete understanding of human behavior in the marketplace [1]. It differs from classical economics, which assumes that individuals are rational and always make decisions that maximize their utility. In contrast, behavioral economics
Behavioral economics and artificial intelligence (AI) have been two rapidly growing fields of research over the past few years. While behavioral economics aims to combine concepts from psychology, sociology, and neuroscience with classical economic thoughts to understand human decision-making processes in the complex economic environment, AI on the other hand, focuses on creating intelligent machines that can mimic human cognitive abilities such as learning, problem-solving, decision-making, and language understanding. The intersection of these two fields has led to thrilling research theories and practical applications. This study provides a bibliometric analysis of the literature on AI and behavioral economics to gain insight into research trends in this field. We conducted this bibliometric analysis using the Web of Science database on articles published between 2012 and 2022 that were related to AI and behavioral economics. VOSviewer and Bibliometrix R package were utilized to identify influential authors, journals, institutions, and countries in the field. Network analysis was also performed to identify the main research themes and their interrelationships. The analysis revealed that the number of publications on AI and behavioral economics has been increasing steadily over the past decade. We found that most studies focused on customer and consumer behavior, including topics such as decision-making under uncertainty, neuroeconomics, and behavioral game theory, combined mainly with machine learning and deep learning techniques. We also identified several emerging themes, including the use of AI in nudging and prospect theory in behavioral finance, as well as undeveloped themes such as AI-driven behavioral macroeconomics. The findings suggests that there is a need for more interdisciplinary collaboration between researchers in behavioral economics and AI. We also suggest that future research on AI and behavioral economics further consider the ethical implications of using AI and behavioral insights in decision-making. This study can serve as a valuable resource for researchers interested in AI and behavioral economics.INDEX TERMS Artificial intelligence; behavioral economics; behavioral finance; consumer behavior; investor behavior; decision making; neuroeconomics; machine learning; bibliometric analysis I. INTRODUCTIONB EHAVIORAL economics is a subfield of economics that examines the decision-making processes of individuals and groups and how they affect economic outcomes. It is an interdisciplinary field that combines elements of psychology, sociology, neuroscience, and economics to provide a more complete understanding of human behavior in the marketplace [1]. It differs from classical economics, which assumes that individuals are rational and always make decisions that maximize their utility. In contrast, behavioral economics
Background The present review aimed to systematically identify and classify barriers and facilitators of conducting research with a team science approach. Methods PubMed, EMBASE, PsycINFO, Scopus, Web of Science, Emerald, and ProQuest databases were searched for primary research studies conducted using quantitative, qualitative, or mixed methods. Studies examining barriers and facilitators of research with a team science approach were included in search. Two independent reviewers screened the texts, extracted and coded the data. Quality assessment was performed for all 35 included articles. The identified barriers and facilitators were categorized within Human, Organization, and Technology model. Results A total of 35 studies from 9,381 articles met the inclusion criteria, from which 42 barriers and 148 facilitators were identified. Human barriers were characteristics of the researchers, teaming skills, and time. We consider Human facilitators across nine sub-themes as follows: characteristics of the researchers, roles, goals, communication, trust, conflict, disciplinary distances, academic rank, and collaboration experience. The barriers related to organization were institutional policies, team science integration, and funding. Organizational facilitators were as follows: team science skills training, institutional policies, and evaluation. Facilitators in the field of technology included virtual readiness and data management, and the technology barriers were complexity of techniques and privacy issues. Conclusions We identified major barriers and facilitators for conducting research with team science approach. The findings have important connotations for ongoing and future implementation of this intervention strategy in research. The analysis of this review provides evidence to inform policy-makers, funding providers, researchers, and students on the existing barriers and facilitators of team science research. Trial registration This review was prospectively registered on PROSPERO database (PROSPERO 2021 CRD42021278704).
Global challenges are complex and must be tackled in a holistic manner. Understanding and addressing them requires collaboration across disciplines, often uniting the humanities and social and natural sciences, to ask better questions and identify practical and revolutionary solutions. Universities can be excellent vehicles for transformational change as they educate the next generation of civically-motivated thinkers to create meaningful action and impact. Too often systemic, artificial barriers exist within these institutions that prevent meaningful transdisciplinary collaboration from succeeding. We recommend that universities identify grand challenges and foster a culture of cross-department collaboration with appropriate internal and external resources to enable broader impacts. Together, funders and institutional policymakers play a critical strategic role in fostering civic scientists and transdisciplinary researchers to solve multifaceted global problems.
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