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

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  • Subhadeep Mukhopadhyay

    (​United Analytics and Computational Intelligence, Inc.)

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.” 1. 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). 2. 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 in Ann Stat 239–265, 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

  • 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.
    • Handle: RePEc:spr:jqecon:v:21:y:2023:i:1:d:10.1007_s40953-022-00332-9
    DOI: 10.1007/s40953-022-00332-9
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    References listed on IDEAS

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    1. Buansing, T.S. Tuang & Golan, Amos & Ullah, Aman, 2020. "An information-theoretic approach for forecasting interval-valued SP500 daily returns," International Journal of Forecasting, Elsevier, vol. 36(3), pages 800-813.
    2. Lee Tae-Hwy & Ullah Aman & Mao Millie Yi, 2021. "Maximum Entropy Analysis of Consumption-based Capital Asset Pricing Model and Volatility," Journal of Econometric Methods, De Gruyter, vol. 10(1), pages 1-19, January.
    3. 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.
    4. Subhadeep & Mukhopadhyay, 2021. "A Maximum Entropy Copula Model for Mixed Data: Representation, Estimation, and Applications," Papers 2108.09438, arXiv.org, revised Aug 2022.
    5. George Box, 2001. "Statistics for discovery," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(3-4), pages 285-299.
    6. Subhadeep Mukhopadhyay, 2022. "A maximum entropy copula model for mixed data: representation, estimation and applications," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 34(4), pages 1036-1062, October.
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