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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/63015


    Title: An Abductive-Reasoning Guide for Finance Practitioners
    Authors: Tsaih, Rua-Huan;Lin, Hsiou-Wei William;Ke, Wen-Chyan
    蔡瑞煌;林修葳;柯文乾
    Contributors: 資管系
    Keywords: Abductive reasoning;Rule extraction;Neural networks;Linear/nonlinear programming
    Date: 2013.06
    Issue Date: 2013-12-26 17:12:44 (UTC+8)
    Abstract: This article proposes a process through which a finance practitioner’s knowledge interacts with artificial intelligence (AI) models. AI models are widely applied, but how these models learn or whether they learn the right things is not easily unveiled. Extant studies especially regarding neural networks have attempted to extract reliable rules/features from AI models. However, if these models make mistakes, then the decision maker may establish paradoxical beliefs. Therefore, extracted rules/features should be justified via the prior thoughts, and vice versa. That is, with these extracted rules/features, a practitioner may need either to update his or her belief or to disregard the AI models. This study sets up a finance demonstraion for the proposed process. The proposed guide demonstrates an abductive-reasoning effect.
    Relation: Computational Economics, 43(4), pp.411-443
    Data Type: article
    DOI link: http://dx.doi.org/10.1007/s10614-013-9390-y
    DOI: 10.1007/s10614-013-9390-y
    Appears in Collections:[Department of MIS] Periodical Articles

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