About Me

Hanti Lin

Associate Professor
Department of Philosophy
University of California at Davis

ika[AT]ucdavis.edu

I am a philosopher of science and formal epistemologist at UC Davis. I did my postdoc at the Australian National University and my PhD at Carnegie Mellon University. I have spent the past three years working on a project that aims to justify certain kinds of inductive inferences and to make some progress in our endeavor to reply to Hume's problem of induction. To set the bar very high, my project targets specifically at inductive inferences that are fundamental to the sciences but have hitherto been quite recalcitrant---resisting any justification in statistics, machine learning theory, or formal epistemology. I have at least two examples in mind: (a) enumerative induction to its full conclusion, e.g., that all ravens are black, not just that all the ravens you will observe are black; (b) causal inference without the (somewhat notorious) faithfulness condition or the like in machine learningTo justify those kinds of inductions, I articulate and defend an epistemological tradition that is very influential in science but often underrecognized and misunderstood in philosophy---the tradition that takes seriously the epistemic ideal of convergence to the truth, following the footsteps of C. S. Peirce, H. Reichenbach and H. Putnam. See the following three papers for more details. You can find my CV here

1. Modes of Convergence to the Truth: Steps toward a Better Epistemology of Induction, forthcoming in The Review of Symbolic Logic.

This paper aims to justify enumerative induction at its full strength---a task that very few formal epistemologists (if any) have attempted before. The slides presented at the 2018 Formal Epistemology Workshop are available here.


With the same justification strategy as in the preceding paper, this paper aims to justify causal inference without assuming what almost all theorists of causal discovery assume: the famous Causal Faithfulness Condition or the like. The slides presented at the 2018 PSA are available here.


This is a paper in statistics and machine learning theory, proving the theorems that are needed in the preceding, philosophical paper. This is joint work with Jiji Zhang.

For more details about the project, visit the project page

My older work focuses on the cognitive and conative roles of accepting sentences or propositions, especially the roles that it can or should play in inquiry, decision-making, or linguistic understanding--even for a Bayesian agent. That constitutes the bulk of my publications so far. I am also interested in philosophy of language and logic, especially the topics about compositional, non-truth-conditional semantics which is in line with expressivism. But for now I have to focus a lot more on the main, epistemological project before I can get back to a semantics paper I have presented several times: "When 'Or' Meets 'Might': Toward Acceptability-Conditional Semantics", which is available upon request.



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