It is difficult to make decisions based on objective data and rigorous logical calculation in a complicated or uncertain decision situation. People are often bounded-rational by a series of constraints, e.g., inadequate knowledge reservation, less experience accumulation, finite imagination, inadequate computation capacity, different risk preferences, or time limitations. People prefer to simplify problems by relying on some common senses and their intuitions, rapidly dealing with the known information, and subsequently forming judgments. Behavioral decision-making theory believes that the psychological behavioral factor is of vital importance in the process of decision-making and will have a strong impact on the decision result. For instance, the reference dependence in the prospect theory shows that the value relative to the reference point could affect people’s psychological feelings, and thus plays a decisive role in the final decision.
Objective data and subjective judgment are two indispensable kinds of information in common decision problems, where objective data describes the fact and subjective judgment represents its meaning. The objective data is necessary to clarify the essence of the decision problem, even for behavioral decision-making that emphasizes the psychological behavioral factor so much. Especially in the information age of data explosion, data-driven decision-making can help people fully understand the nature of the problem, prevent personal bias and intuitive control, and partially automate the decision-making process. Recent research has shown that the development and application of data-driven behavioral decision-making methods are beneficial in terms of increasing efficiency, promoting collaboration, optimizing processes, and so on. However, underlying correlations between data-driven and psychological behavioral factors remain to be discovered, and more innovative ways of developing behavioral decision-making from the data-driven perspective are needed to satisfy the demands of complicated and uncertain decisions.
This research topic aims to address recent theoretical and methodological advances in data-driven behavioral decision-making. We welcome original innovations and applications of data-driven behavioral decision-making methods that contribute to digging underlying correlations between objective data and subjective judgment, exploiting novel data-driven technology assisting behavioral decision-making, exploring new psychological behavioral factors affecting the process and result of decision-making, clarifying the inner mechanism of psychological behavioral factors acting on decision-making at different stages, designing innovative decision-making ways of collaborating data information with psychological cognition, and so on. We also encourage multidisciplinary researches and works that provide pieces of evidence and perspectives from the fields of multi-attribute decision-making (MADM), group decision making (GDM), Decision Making Trial and Evaluation Laboratory (DEMATEL), decision-making under uncertainty, expert system, artificial intelligence, and so on.
The topics of interest include, but are not restricted to the following:
- Data-driven behavioral MADM innovation and application
- Data-driven behavioral GDM innovation and application
- Data-driven behavioral large-scale GDM innovation and application
- Data-driven behavioral Dempster-Shafer theory of evidence and extensions
- Data-driven behavioral Evidential Reasoning theory and extensions
- Data-driven behavioral DEMATEL innovation and application
- Data-driven behavioral decision-making under uncertainty
- Data-driven behavioral fuzzy decision-making and extensions
- Data-driven behavioral strategic decision-making innovation and application
- Data-driven behavioral operational decision-making innovation and application
- Data-driven behavioral marketing decision-making innovation and application
- Data-driven behavioral e-commerce decision-making innovation and application
- Data-driven behavioral decision making in marine management
It is difficult to make decisions based on objective data and rigorous logical calculation in a complicated or uncertain decision situation. People are often bounded-rational by a series of constraints, e.g., inadequate knowledge reservation, less experience accumulation, finite imagination, inadequate computation capacity, different risk preferences, or time limitations. People prefer to simplify problems by relying on some common senses and their intuitions, rapidly dealing with the known information, and subsequently forming judgments. Behavioral decision-making theory believes that the psychological behavioral factor is of vital importance in the process of decision-making and will have a strong impact on the decision result. For instance, the reference dependence in the prospect theory shows that the value relative to the reference point could affect people’s psychological feelings, and thus plays a decisive role in the final decision.
Objective data and subjective judgment are two indispensable kinds of information in common decision problems, where objective data describes the fact and subjective judgment represents its meaning. The objective data is necessary to clarify the essence of the decision problem, even for behavioral decision-making that emphasizes the psychological behavioral factor so much. Especially in the information age of data explosion, data-driven decision-making can help people fully understand the nature of the problem, prevent personal bias and intuitive control, and partially automate the decision-making process. Recent research has shown that the development and application of data-driven behavioral decision-making methods are beneficial in terms of increasing efficiency, promoting collaboration, optimizing processes, and so on. However, underlying correlations between data-driven and psychological behavioral factors remain to be discovered, and more innovative ways of developing behavioral decision-making from the data-driven perspective are needed to satisfy the demands of complicated and uncertain decisions.
This research topic aims to address recent theoretical and methodological advances in data-driven behavioral decision-making. We welcome original innovations and applications of data-driven behavioral decision-making methods that contribute to digging underlying correlations between objective data and subjective judgment, exploiting novel data-driven technology assisting behavioral decision-making, exploring new psychological behavioral factors affecting the process and result of decision-making, clarifying the inner mechanism of psychological behavioral factors acting on decision-making at different stages, designing innovative decision-making ways of collaborating data information with psychological cognition, and so on. We also encourage multidisciplinary researches and works that provide pieces of evidence and perspectives from the fields of multi-attribute decision-making (MADM), group decision making (GDM), Decision Making Trial and Evaluation Laboratory (DEMATEL), decision-making under uncertainty, expert system, artificial intelligence, and so on.
The topics of interest include, but are not restricted to the following:
- Data-driven behavioral MADM innovation and application
- Data-driven behavioral GDM innovation and application
- Data-driven behavioral large-scale GDM innovation and application
- Data-driven behavioral Dempster-Shafer theory of evidence and extensions
- Data-driven behavioral Evidential Reasoning theory and extensions
- Data-driven behavioral DEMATEL innovation and application
- Data-driven behavioral decision-making under uncertainty
- Data-driven behavioral fuzzy decision-making and extensions
- Data-driven behavioral strategic decision-making innovation and application
- Data-driven behavioral operational decision-making innovation and application
- Data-driven behavioral marketing decision-making innovation and application
- Data-driven behavioral e-commerce decision-making innovation and application
- Data-driven behavioral decision making in marine management
