IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2111.08054.html
   My bibliography  Save this paper

Abductive Inference and C. S. Peirce: 150 Years Later

Author

Listed:
  • Deep Mukhopadhyay

Abstract

This paper is about two things: (i) Charles Sanders Peirce (1837-1914) -- an iconoclastic philosopher and polymath who is among the greatest of American minds. (ii) Abductive inference -- a term coined by C. S. Peirce, which he defined as "the process of forming explanatory hypotheses. It is the only logical operation which introduces any new idea." Abductive inference and quantitative economics: Abductive inference plays a fundamental role in empirical scientific research as a tool for discovery and data analysis. Heckman and Singer (2017) strongly advocated "Economists should abduct." Arnold Zellner (2007) stressed that "much greater emphasis on reductive [abductive] inference in teaching econometrics, statistics, and economics would be desirable." But currently, there are no established theory or practical tools that can allow an empirical analyst to abduct. This paper attempts to fill this gap by introducing new principles and concrete procedures to the Economics and Statistics community. I termed the proposed approach as Abductive Inference Machine (AIM). The historical Peirce's experiment: In 1872, Peirce conducted a series of experiments to determine the distribution of response times to an auditory stimulus, which is widely regarded as one of the most significant statistical investigations in the history of nineteenth-century American mathematical research (Stigler, 1978). On the 150th anniversary of this historical experiment, we look back at the Peircean-style abductive inference through a modern statistical lens. Using Peirce's data, it is shown how empirical analysts can abduct in a systematic and automated manner using AIM.

Suggested Citation

  • Deep Mukhopadhyay, 2021. "Abductive Inference and C. S. Peirce: 150 Years Later," Papers 2111.08054, arXiv.org, revised Feb 2023.
    • Handle: RePEc:arx:papers:2111.08054
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2111.08054
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. George Box, 2001. "Statistics for discovery," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(3-4), pages 285-299.
    2. Subhadeep Mukhopadhyay & Emanuel Parzen, 2020. "Nonparametric universal copula modeling," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(1), pages 77-94, January.
    3. James J. Heckman & Burton Singer, 2017. "Abducting Economics," American Economic Review, American Economic Association, vol. 107(5), pages 298-302, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Subhadeep Mukhopadhyay, 2023. "Modelplasticity and abductive decision making," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 46(1), pages 255-276, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Subhadeep & Mukhopadhyay, 2022. "Modelplasticity and Abductive Decision Making," Papers 2203.03040, arXiv.org, revised Mar 2023.
    2. Subhadeep Mukhopadhyay, 2023. "Modelplasticity and abductive decision making," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 46(1), pages 255-276, June.
    3. Subhadeep Mukhopadhyay, 2023. "Abductive Inference and C. S. Peirce: 150 Years Later," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(1), pages 123-149, March.
    4. Louis R. Nemzer & Florence Neymotin, 2020. "Concierge care and patient reviews," Health Economics, John Wiley & Sons, Ltd., vol. 29(8), pages 913-922, August.
    5. Subhadeep & Mukhopadhyay, 2021. "A Maximum Entropy Copula Model for Mixed Data: Representation, Estimation, and Applications," Papers 2108.09438, arXiv.org, revised Aug 2022.
    6. Joep Cornelissen & Mariëtte Kaandorp, 2023. "Towards Stronger Causal Claims in Management Research: Causal Triangulation Instead of Causal Identification," Journal of Management Studies, Wiley Blackwell, vol. 60(4), pages 834-860, June.
    7. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    8. Charles E. Eesley & Yong Suk Lee, 2021. "Do university entrepreneurship programs promote entrepreneurship?," Strategic Management Journal, Wiley Blackwell, vol. 42(4), pages 833-861, April.
    9. Josephson, Anna & Michler, Jeffrey D., 2018. "Viewpoint: Beasts of the field? Ethics in agricultural and applied economics," Food Policy, Elsevier, vol. 79(C), pages 1-11.
    10. Donelson, Dain C. & Kubic, Matthew & Toynbee, Sara, 2024. "The SEC's September spike: Regulatory inconsistency within the fiscal year," Journal of Accounting and Economics, Elsevier, vol. 77(2).
    11. Murmann, Martin & Salmivaara, Virva & Kibler, Ewald, 2023. "How does late-career entrepreneurship relate to innovation?," Research Policy, Elsevier, vol. 52(6).
    12. Chao Hu & Jianping Tao & Donghao Zhang & Damian Adams, 2021. "Price Signal of Tilled Land in Rural China: An Empirically Oriented Transaction Costs Study Based on Contract Theory," Land, MDPI, vol. 10(8), pages 1-20, August.
    13. Evan Starr & Brent Goldfarb, 2020. "Binned scatterplots: A simple tool to make research easier and better," Strategic Management Journal, Wiley Blackwell, vol. 41(12), pages 2261-2274, December.
    14. Justman, Moshe, 2018. "Randomized controlled trials informing public policy: Lessons from project STAR and class size reduction," European Journal of Political Economy, Elsevier, vol. 54(C), pages 167-174.
    15. Sharma Mithun & Sharma Shilpi, 2021. "Critical Evaluation into the practical utility of the Design of Experiments," Engineering Management in Production and Services, Sciendo, vol. 13(3), pages 50-65, September.
    16. Armstrong, Christopher & Kepler, John D. & Samuels, Delphine & Taylor, Daniel, 2022. "Causality redux: The evolution of empirical methods in accounting research and the growth of quasi-experiments," Journal of Accounting and Economics, Elsevier, vol. 74(2).
    17. Mukhopadhyay, Subhadeep & Wang, Kaijun, 2023. "On The Problem of Relevance in Statistical Inference," Econometrics and Statistics, Elsevier, vol. 25(C), pages 93-109.
    18. Campbell, Brett & Drake, Michael & Thornock, Jacob & Twedt, Brady, 2023. "Earnings Virality," Journal of Accounting and Economics, Elsevier, vol. 75(1).

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2111.08054. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.