Causal Responsibility and Counterfactuals
Open accessAbstract
How do people attribute responsibility in situations where the contributions of multiple agents combine to produce a joint outcome? The prevalence of over‐determination in such cases makes this a difficult problem for counterfactual theories of causal responsibility. In this article, we explore a general framework for assigning responsibility in multiple agent contexts. We draw on the structural model account of actual causation (e.g., Halpern & Pearl, 2005) and its extension to responsibility judgments (Chockler & Halpern, 2004). We review the main theoretical and empirical issues that arise from this literature and propose a novel model of intuitive judgments of responsibility. This model is a function of both pivotality (whether an agent made a difference to the outcome) and criticality (how important the agent is perceived to be for the outcome, before any actions are taken). The model explains empirical results from previous studies and is supported by a new experiment that manipulates both pivotality and criticality. We also discuss possible extensions of this model to deal with a broader range of causal situations. Overall, our approach emphasizes the close interrelations between causality, counterfactuals, and responsibility attributions.
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Human reasoning is scaffolded by rich causal models of the world, and humans can intervene on these models to perform rapid and flexible counterfactual simulations.
“there is increasing evidence from cognitive science that human reasoning is scaffolded by rich causal models of the world (Pearl, 1988;Lake et al., 2017), and that humans can intervene on these models to perform rapid and flexible counterfactual simulations (Lagnado et al., 2013;Gerstenberg et al., 2017Gerstenberg et al., , 2021)).”
From Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks (2024) · cites this paper as Lagnado et al., 2013
Section 1.2, “Counterfactual models of causation” (page unavailable), explains that an imagined intervention on a causal model supports counterfactual reasoning. The General Discussion (§4.3, page unavailable) also considers modifying models and counterfactual worlds. These passages support the intervention-and-simulation idea, but they do not establish that humans perform such simulations rapidly. (LLM, conf=0.86)
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Cited by
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Wu, Z., Qiu, L., Ross, A., Akyürek, E., Chen, B., Wang, B., Kim, N., Andreas, J., & Kim, Y. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2024.naacl-long.102 · Cited by 58 · see citing works
Blaming automated vehicles in difficult situations
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Self-citationCausal Responsibility and Robust Causation
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Self-citation
- Authors
- Lagnado, D. A., Gerstenberg, T., & Zultan, R.
- Affiliations
- University College London (GB)
- Ben-Gurion University of the Negev (IL)
- Corresponding author
- Lagnado, David A.
- Venue
- Cognitive Science
- Publisher
- Wiley
- Published
- 2013-07-15
- Volume
- 37
- Issue
- 6
- Pages
- 1036-1073
- ISSN
- 0364-0213 (ISSN-L)
- 1551-6709
- PMC
- PMC4296348
- OpenAlex
- W2047447098
- Cited by
- 191
- References
- 3
- Citations by year
- 2026: 11 · 2025: 14 · 2024: 11 · 2023: 20 · 2022: 17 · 2021: 23 · 2020: 12 · 2019: 33 · 2018: 14 · 2017: 12 · 2016: 6 · 2015: 8 · 2014: 8 · 2013: 1 · 2012: 1
- FWCI
- 4.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cognitive Science (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- AXA Research Fund
- Economic and Social Research Council (ES/H041796/1, -062-33-0004)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Psychology of Moral and Emotional Judgment
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Keywords
- causal responsibility, counterfactuals, actual causation, multiple agents, overdetermination, pivotality, criticality, responsibility judgments, joint outcomes
- MeSH terms
- Humans, Judgment, Models, Psychological, Social Behavior, Social Perception, Causality
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