About Me

Hanti Lin

Assistant Professor
Department of Philosophy
University of California at Davis


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 two or 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 so-called faithfulness condition or the likeTo 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 in which convergence to the truth is valued epistemically. See the next section for more details. 

You can find my CV here

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.

Sketch of the Main Project: Learning-Theoretic Epistemology

If I am right, the epistemological tradition I just mentioned is best reconstructed as a commitment to the following core guidelines:

(i) Inferential procedures and learning methods for tackling one empirical problem or another should be evaluated and justified in terms of a certain distinguished group of concepts, the most important of which include (but are not limited to) convergence to the truth and its various modes

(ii) An inferential procedure is always evaluated with respect to how good it is for tackling one empirical problem or another. For there is no such thing as a universally best inferential procedure, best for tackling all possible empirical problems.

(iii) Convergence to the truth has many different modesThey are epistemic ideals for inquirers or inferential procedures to achieve where possible; some are higher epistemic ideals than some others are. When tackling an empirical problem, we should look for the modes of convergence to the truth can be achieved, and try to achieve the best mode we can have

(iv) The above does not imply that we should forget other epistemic ideals. Feel free to pursue, in addition to convergence to the truth, any other epistemic ideals you value (such as diachronic coherence, explanatory unification, or safe beliefs in your chosen worlds). The epistemological tradition in question only says this: 

For any empirical problem P, a learning method or inferential procedure counts as one of the best for tackling P only if it meets this condition: achieving the best achievable mode of convergence with respect to P

This condition is only claimed to be necessary, rather than sufficient. And there is a good reason to take it as at least necessary: Why would you want to rely on a certain inferential procedure right now if it is not expected to return you the truth even when it is given an infinite data set?

Many people have contributed ideas to some of those guidelines. For example: 

- philosophers H. Reichenbach and H. Putnam
- formal learning theorists E. M. Gold, and D. Angluin
- computational learning theorists L. Valiant
- statistician R. Fisher, and even Bayesian statisticians P. Diaconis and D. Freedman

I call the above epistemological tradition learning-theoretic epistemology, for it is most recognizable in the many branches of learning theory as studied in theoretical computer science. Despite the fact that many people have contributed to this epistemological tradition, I want to do something that very few have done: articulate the guidelines (i)-(iv) clearly, take all of them seriously, and keep distance from additional theses that are entirely optional but potentially misleading. In particular, I argue that learning-theoretic epistemology is compatible with Bayesianism, neutral between externalism and internalism, and even can be welcome by coherentists despite its reliabilist flavor. By taking all the guidelines (i)-(iv) seriously, I have been able to justify certain kinds of inductive inferences that have long resisted justification---or so I argue in the following manuscripts:

1. Modes of Convergence to the Truth: Steps toward a Better Epistemology of Induction

This paper aims to justify enumerative induction at its full strength---a task that very few formal epistemologists (if any) have attempted before. Slides 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.

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

For more details, 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.


Lin, H. (forthcoming) “Belief Revision Theory”, in Pettigrew, R. and Weisberg, J. (ed.) The Open Handbook of Formal Epistemology.

Hájek, A. and Lin, H. (2017) “A Tale of Two Epistemologies?”, Res Philosophica, 94(2): 207- 232.

Lin, H. (2017) “Enumerative Induction and Semi-Uniform Convergence to the Truth”, in Baltag, A., Seligman, J. and Yamada, T. (eds.) Logic, Rationality, and Interaction: Proceedings of the 6th International Workshop, LORI 2017, Lecture Notes in Computer Science, Springer, 362-376.

Kevin, K. T., Genin, K. and Lin, H. (2016) “Realism, Rhetoric, and Reliability”, Synthese, 193(4): 1191-1223.

Lin, H. (2016) “Bridging the Logic-Based and Probability-Based Approaches to Artificial Intel- ligence”, in Hung, T.-W. (ed.) Rationality: Constraints and Contexts, Amsterdam: Elsevier.

Lin, H. (2016) “The Meaning of Epistemic Modality and the Absence of Truth”, in Yang, C-M., Deng, D.-M., and Lin, H. (eds.) Structural Analysis of Non-Classical Logics, Berlin: Springer-Verlag.

Lin, H. (2014) "On the Regress Problem of Deciding How to Decide", Synthese 191: 661- 670.