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This paper is about two things: (i) Charles Sanders Peirce (1837-1914) -- aniconoclastic philosopher and polymath who is among the greatest of Americanminds. (ii) Abductive inference -- a term coined by C. S. Peirce, which hedefined as "the process of forming explanatory hypotheses. It is the onlylogical operation which introduces any new idea."  Abductive inference and quantitative economics: Abductive inference plays afundamental role in empirical scientific research as a tool for discovery anddata analysis. Heckman and Singer (2017) strongly advocated "Economists shouldabduct." Arnold Zellner (2007) stressed that "much greater emphasis onreductive [abductive] inference in teaching econometrics, statistics, andeconomics would be desirable." But currently, there are no established theoryor practical tools that can allow an empirical analyst to abduct. This paperattempts to fill this gap by introducing new principles and concrete proceduresto the Economics and Statistics community. I termed the proposed approach asAbductive Inference Machine (AIM).  The historical Peirce's experiment: In 1872, Peirce conducted a series ofexperiments to determine the distribution of response times to an auditorystimulus, which is widely regarded as one of the most significant statisticalinvestigations in the history of nineteenth-century American mathematicalresearch (Stigler, 1978). On the 150th anniversary of this historicalexperiment, we look back at the Peircean-style abductive inference through amodern statistical lens. Using Peirce's data, it is shown how empiricalanalysts can abduct in a systematic and automated manner using AIM.

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