Thomas Icard
Professor of Philosophy and, by courtesy, of Computer Science
Web page: http://web.stanford.edu/people/icard
Academic Appointments
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Professor, Philosophy
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Professor (By courtesy), Computer Science
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Faculty Affiliate, Institute for Human-Centered Artificial Intelligence (HAI)
Honors & Awards
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Dean's Award for Achievements in Teaching, School of Humanities and Sciences (2019-2020)
Program Affiliations
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Symbolic Systems Program
2024-25 Courses
- Animal Cognition
OSPKYOTO 77 (Aut) - Computability and Logic
PHIL 152, PHIL 252 (Spr) - Logic Spring Seminar
PHIL 359 (Spr) - Seminar on Logic & Formal Philosophy
CS 353, PHIL 391 (Win) -
Independent Studies (17)
- Advanced Reading and Research
CS 499 (Aut, Win, Spr) - Advanced Reading and Research
CS 499P (Aut, Win, Spr) - Curricular Practical Training
CS 390A (Aut, Win, Spr) - Curricular Practical Training
CS 390B (Aut, Win, Spr) - Independent Project
CS 399 (Aut, Win, Spr) - Independent Study
SYMSYS 196 (Aut, Win, Spr) - Independent Study
SYMSYS 296 (Aut, Win, Spr) - Independent Work
CS 199 (Aut, Win, Spr) - Individual Work for Graduate Students
PHIL 240 (Aut, Win, Spr) - Individual Work, Undergraduate
PHIL 197 (Aut, Win, Spr) - Master's Degree Project
SYMSYS 290 (Aut, Win, Spr) - Senior Honors Thesis
MATH 197 (Win, Spr) - Senior Honors Tutorial
SYMSYS 190 (Aut, Win, Spr) - Senior Project
CS 191 (Aut, Win, Spr) - Teaching in Symbolic Systems
SYMSYS 297 (Aut) - Tutorial, Senior Year
PHIL 196 (Aut, Win, Spr) - Writing Intensive Senior Research Project
CS 191W (Aut, Win, Spr)
- Advanced Reading and Research
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Prior Year Courses
2023-24 Courses
- Citizenship in the 21st Century
COLLEGE 102 (Win) - Mathematical Logic
PHIL 150, PHIL 250 (Aut) - Metalogic
PHIL 151, PHIL 251 (Win) - Seminar on Logic & Formal Philosophy
CS 353, PHIL 391 (Aut, Win)
2022-23 Courses
- Computability and Logic
PHIL 152, PHIL 252 (Spr) - Metalogic
PHIL 151, PHIL 251 (Win) - Minds and Machines
CS 24, LINGUIST 35, PHIL 99, PSYCH 35, SYMSYS 1, SYMSYS 200 (Aut) - Topics in Philosophy of Action: Decision Theory and Planning Agency
PHIL 387 (Spr)
2021-22 Courses
- Levels of Analysis in Cognitive Science
PHIL 366, PSYCH 296 (Aut) - Mathematical Logic
PHIL 150, PHIL 250 (Aut) - Metalogic
PHIL 151, PHIL 251 (Win) - Randomness: Computational and Philosophical Approaches
CS 57N, PHIL 3N (Win)
- Citizenship in the 21st Century
Stanford Advisees
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Puyin Li, Junyi Tao -
Doctoral Dissertation Reader (AC)
Aileen Luo, Mathew McGuthry, Soham Shiva, Imran Thobani -
Orals Chair
Lenny Truong -
Postdoctoral Faculty Sponsor
Gaia Belardinelli -
Doctoral Dissertation Advisor (AC)
David Gottlieb, Jacqueline Harding, Alexander Pereira -
Master's Program Advisor
Matias Benitez, Advit Deepak, Linda Liu, Stephanie Tamayo, Ben Viggiano, Katherine Worden
All Publications
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A Simple Logic of Concepts
JOURNAL OF PHILOSOPHICAL LOGIC
2022
View details for DOI 10.1007/s10992-022-09685-1
View details for Web of Science ID 000886820600001
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An interaction effect of norm violations on causal judgment.
Cognition
2022; 228: 105183
Abstract
Existing research has shown that norm violations influence causal judg- ments, and a number of different models have been developed to explain these effects. One such model, the necessity/sufficiency model, predicts an interac- tion pattern in people's judgments. Specifically, it predicts that when people are judging the degree to which a particular factor is a cause, there should be an interaction between (a) the degree to which that factor violates a norm and (b) the degree to which another factor in the situation violates norms. A study of moral norms (N=1000) and norms of proper functioning (N=3000) revealed robust evidence for the predicted interaction effect. The implications of these patterns for existing theories of causal judgments is discussed.
View details for DOI 10.1016/j.cognition.2022.105183
View details for PubMedID 35830782
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Causal Distillation for Language Models
ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2022: 4288-4295
View details for Web of Science ID 000859869504033
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Inference From Explanation
JOURNAL OF EXPERIMENTAL PSYCHOLOGY-GENERAL
2021
Abstract
What do we communicate with causal explanations? Upon being told, "E because C", a person might learn that C and E both occurred, and perhaps that there is a causal relationship between C and E. In fact, causal explanations systematically disclose much more than this basic information. Here, we offer a communication-theoretic account of explanation that makes specific predictions about the kinds of inferences people draw from others' explanations. We test these predictions in a case study involving the role of norms and causal structure. In Experiment 1, we demonstrate that people infer the normality of a cause from an explanation when they know the underlying causal structure. In Experiment 2, we show that people infer the causal structure from an explanation if they know the normality of the cited cause. We find these patterns both for scenarios that manipulate the statistical and prescriptive normality of events. Finally, we consider how the communicative function of explanations, as highlighted in this series of experiments, may help to elucidate the distinctive roles that normality and causal structure play in causal judgment, paving the way toward a more comprehensive account of causal explanation. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
View details for DOI 10.1037/xge0001151
View details for Web of Science ID 000733088000001
View details for PubMedID 34928680
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Expectations Affect Physical Causation Judgments
JOURNAL OF EXPERIMENTAL PSYCHOLOGY-GENERAL
2020; 149 (3): 599–607
Abstract
When several causes contributed to an outcome, people often single out one as "the" cause. What explains this selection? Previous work has argued that people select abnormal events as causes, though recent work has shown that sometimes normal events are preferred over abnormal ones. Existing studies have relied on vignettes that commonly feature agents committing immoral acts. An important challenge to the thesis that norms permeate causal reasoning is that people's responses may merely reflect pragmatic or social reasoning rather than arising from causal cognition per se. We tested this hypothesis by asking whether the previously observed patterns of causal selection emerge in tasks that recruit participants' causal reasoning about physical systems. Strikingly, we found that the same patterns observed in vignette studies with intentional agents arise in visual animations of physical interactions. Our results demonstrate how deeply normative expectations affect causal cognition. (PsycINFO Database Record (c) 2020 APA, all rights reserved).
View details for DOI 10.1037/xge0000670
View details for Web of Science ID 000512302600015
View details for PubMedID 31512904
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Why Be Random?
Mind
2020
View details for DOI 10.1093/mind/fzz065
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On Open-Universe Causal Reasoning
JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2020: 1233-1243
View details for Web of Science ID 000722423500114
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Probabilistic Reasoning Across the Causal Hierarchy
ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2020: 10170-10177
View details for Web of Science ID 000668126802074
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Calibrating Generative Models: The Probabilistic Chomsky-Schützenberger Hierarchy
Journal of Mathematical Psychology
2020; 95
View details for DOI 10.1016/j.jmp.2019.102308
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Bayes, Bounds, and Rational Analysis
PHILOSOPHY OF SCIENCE
2018; 85 (1): 79–101
View details for DOI 10.1086/694837
View details for Web of Science ID 000419596500004
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Inferring probability comparisons
MATHEMATICAL SOCIAL SCIENCES
2018; 91: 62–70
View details for DOI 10.1016/j.mathsocsci.2017.08.003
View details for Web of Science ID 000424960600009
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Normality and actual causal strength.
Cognition
2017; 161: 80-93
Abstract
Existing research suggests that people's judgments of actual causation can be influenced by the degree to which they regard certain events as normal. We develop an explanation for this phenomenon that draws on standard tools from the literature on graphical causal models and, in particular, on the idea of probabilistic sampling. Using these tools, we propose a new measure of actual causal strength. This measure accurately captures three effects of normality on causal judgment that have been observed in existing studies. More importantly, the measure predicts a new effect ("abnormal deflation"). Two studies show that people's judgments do, in fact, show this new effect. Taken together, the patterns of people's causal judgments thereby provide support for the proposed explanation.
View details for DOI 10.1016/j.cognition.2017.01.010
View details for PubMedID 28157584
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Indicative Conditionals and Dynamic Epistemic Logic
ELECTRONIC PROCEEDINGS IN THEORETICAL COMPUTER SCIENCE
2017: 337–51
View details for DOI 10.4204/EPTCS.251.24
View details for Web of Science ID 000439339700025
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Pragmatic Considerations on Comparative Probability
PHILOSOPHY OF SCIENCE
2016; 83 (3): 348-370
View details for Web of Science ID 000378293100003
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A note on cancellation axioms for comparative probability
THEORY AND DECISION
2016; 80 (1): 159-166
View details for DOI 10.1007/s11238-015-9491-2
View details for Web of Science ID 000369017600007
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Iterating semantic automata
LINGUISTICS AND PHILOSOPHY
2013; 36 (2): 151-173
View details for DOI 10.1007/s10988-013-9132-6
View details for Web of Science ID 000323661000002
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Inclusion and Exclusion in Natural Language
STUDIA LOGICA
2012; 100 (4): 705-725
View details for DOI 10.1007/s11225-012-9425-8
View details for Web of Science ID 000309056000004
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Provability and Interpretability Logics with Restricted Realizations
NOTRE DAME JOURNAL OF FORMAL LOGIC
2012; 53 (2): 133-154
View details for DOI 10.1215/00294527-1715653
View details for Web of Science ID 000305371100001
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A Topological Study of the Closed Fragment of GLP
JOURNAL OF LOGIC AND COMPUTATION
2011; 21 (4): 683-696
View details for DOI 10.1093/logcom/exp043
View details for Web of Science ID 000293303900008