| Project/Area Number |
17K18569
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| Research Category |
Grant-in-Aid for Challenging Research (Exploratory)
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| Allocation Type | Multi-year Fund |
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| Research Field |
Economics, Business Administration, and related fields
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| Research Institution | Waseda University |
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Principal Investigator |
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| Project Period (FY) |
2017-06-30 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥6,240,000 (Direct Cost: ¥4,800,000、Indirect Cost: ¥1,440,000)
Fiscal Year 2019: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2017: ¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
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| Keywords | 帰納的ゲーム理論 / ゲーム論理 / 動的認識論理 / 経験 / 資産取引実験 / コミュニケーション / 事例ベース推論 / 事例ベース意思決定理論 / ゲーム理論 / 帰納的学習 / 確率論理学 |
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| Outline of Final Research Achievements |
I constructed a decision theory on inductive approach and examine empirical and experimental data of trades and consumptions. In the construction of a theory, I introduce unawareness structure into dynamic game logic and distinguish two different information: new information and information which an agent already holds but is not aware of the relevance. The theory gives a foundation of a behavioral model to explain their trades.
In the experimental and empirical analyses, I examined the trade data in experimental asset markets and the consumption data of experience goods. In asset market trading, I observed that the confidence level of the traders' forecasts activates asset trading. Although the empirical goods consumption data did not provide data on confidence in the satisfaction predictions that consumption brings about, we observed consumption behavior with high price sensitivity.
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| Academic Significance and Societal Importance of the Research Achievements |
標準的ゲーム理論において想定される直面する状況を完全に把握し、その元で演繹的な推論を行う完全合理的主体の想定を緩め、限定合理的で経験を通じた帰納的推論を行う主体を想定した意思決定理論を構築した。経済理論がこれまで演繹的なアプローチに終始していた中、帰納的推論の観点から説明可能なこれまでとは異なる方法論に基づいた理論を構築している。このことは、人間の持つ帰納的能力の意思決定における役割を分析するための有用な枠組みになると考えている。また、帰納的推論は実験・実証データとの相性が良く、理論の科学的検証をより精緻に行うことにもつながる。このため、近年のデータサイエンスの発展の一助となる。
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