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Causality, Responsibility, and Blame: A Structural-Model Approach

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1 Author(s)
Halpern, J.Y. ; Dept. of Comput. Sci., Cornell Univ., Ithaca, NY

This talk provides an overview of work that I have done with Hana Chockler, Orna Kupferman, and Judea Pearl (H. Chockler and J. Y. Halpern, 2004), (H. Chockler, et al., 2003), (J. Y. Halpern and J. Pearl, 2005), on defining notions such as causality, explanation, responsibility, and blame. I first review the Halpern-Pearl definition of causality, what it means that A is a cause of B, and show how it handles well some standard problems of causality. This definition of causality (like most in the literature) views causality as an all-or-nothing concept. Either A is a cause of B or it is not. I show how it can be extended to take into account the degree of responsibility of A for B. For example, if someone wins an election 11-0, each person is less responsible for his victory than if he had won 6-5. Finally, I show how this notion of degree of responsibility can be used to provide insight into model checking notions such as coverage

Published in:

Quantitative Evaluation of Systems, 2006. QEST 2006. Third International Conference on

Date of Conference:

11-14 Sept. 2006