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Counterfactual, Casual and Explanatory Reasoning

Stanford linguist Daniel Lassiter to explore theoretical shifts in philosophy of language
Counterfactual, Casual and Explanatory Reasoning

Daniel Lassiter

Oct 13, 2017
from 03:00 PM to 04:30 PM

Andrews Conference Room 2203, Social Sciences and Humanities Building

Abstract: Counterfactual/subjunctive conditionals ("If I weren't in Davis, I would be at home") are an important topic in linguistics, many areas of philosophy, and both cognitive and social psychology. For almost 50 years, the dominant theoretical framework in philosophy of language and formal semantics has been built around either a 'similarity' concept or inferencing from contextually provided 'premise sets'. Recently there has been a move toward building theories of counterfactuals around causal models, with many of the key formal and empirical contributions coming from computer scientists and psychologists. In this talk, Daniel Lassiter will review some of the motivations for this shift and sketch some of his recent research on the topic, focusing on the goal of a unified compositional semantics for indicative and counterfactual conditionals, interactions with the language of probability, and a new approach to the longstanding problem of modeling complex interventions ("If it were either raining or the sprinkler and hose were both on, ..."). Time permitting, he may also discuss some new experimental work testing the quantitative predictions of the complex‐intervention model.

Daniel Lassiter is an assistant professor of linguistics at Stanford University. His research combines formal tools and experimental methods from linguistics and other areas of cognitive science to work toward a unified theory of language understanding as a cognitive  phenomenon. He has worked on a variety of topics such as the semantics of modals and degree expressions, the pragmatics of vagueness and presupposition, inductive vs. deductive reasoning, and models of various pragmatic phenomena which treat language understanding as a problem of Bayesian inference. He has argued in various domains that combining logical and probabilistic models not only achieves a desirable theoretical unification but also improved empirical coverage and new theoretical  insights.  

This Linguistics Colloquium is sponsored by the Department of Linguistics and the UC Davis Language Sciences Group.