| Project/Area Number |
18K12739
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 07010:Economic theory-related
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| Research Institution | Nagoya University of Commerce & Business (2020-2023)
Tohoku University (2018-2019) |
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Principal Investigator |
QIN DAN 名古屋商科大学, 経営学部, 准教授 (10756092)
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| Project Period (FY) |
2018-04-01 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2020: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2019: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | bounded rationality / reference-dependency / reference-dependent / dual-self / multi-self / reference alternative / structural property / choice overload / revealed preference / revealed reference |
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| Outline of Final Research Achievements |
There are two major parts of the research project. The first part presents a new way to understand how people make choices by considering two different types of preferences: simple, gut-feeling decisions and more complex, thoughtful decisions. By relaxing some traditional assumptions about rational decision-making, the model can explain a variety of unexpected behaviors people exhibit.
The second part examines how people's decisions are influenced by their reference points, such as what they already own or what they consider a loss. It looks at three specific models: the endowment effect, loss aversion, and reference-dependent shortlisting. By exploring the unique and overlapping behaviors of these models, the research provides deeper insights into how reference points shape our choices.
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| Academic Significance and Societal Importance of the Research Achievements |
Our models help organize and make sense of different ways people choose between options and offers a valuable tool for understanding and predicting real-life decision-making and help create better policies and strategies to encourage positive behaviors.
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