David Danks

David Danks is L.L. Thurstone Professor of Philosophy & Psychology, and Head of the Department of Philosophy, at Carnegie Mellon University. He is also the Chief Ethicist of CMU’s Block Center for Technology & Society; co-director of CMU’s Center for Informed Democracy and Social Cybersecurity (IDeaS); and an adjunct member of the Heinz College of Information Systems and Public Policy, and the Carnegie Mellon Neuroscience Institute. His research interests are at the intersection of philosophy, cognitive science, and machine learning, using ideas, methods, and frameworks from each to advance our understanding of complex, interdisciplinary problems. Danks has examined the ethical, psychological, and policy issues around AI and robotics in transportation, healthcare, privacy, and security. He has also done significant research in computational cognitive science, culminating in his Unifying the Mind: Cognitive Representations as Graphical Models (2014, The MIT Press). And he has developed multiple novel causal discovery algorithms for complex types of observational and experimental data. Danks is the recipient of a James S. McDonnell Foundation Scholar Award, as well as an Andrew Carnegie Fellowship. He received an A.B. in Philosophy from Princeton University, and a Ph.D. in Philosophy from University of California, San Diego.

Selected publications:

• Danks, D. (2018). Privileged (default) causal cognition: A mathematical analysis. Frontiers in Psychology, 9: 498. doi: 10.3389/fpsyg.2018.00498
• Danks, D. (2017). Singular causation. In M. R. Waldmann (Ed.), Oxford handbook of causal reasoning (pp. 201-215). Oxford: Oxford University Press.
• Wellen, S., & Danks, D. (2016). Adaptively rational learning. Minds & Machines, 26, 87-102.
• Wellen, S., & Danks, D. (2014). Learning with a purpose: The influence of goals. In P. Bello, M Guarini, M. McShane, & B. Scassellati (Eds.), Proceedings of the 36th annual conference of the cognitive science society (pp. 1766-1771). Austin, TX: Cognitive Science Society.
• Wellen, S., & Danks, D. (2012). Learning causal structure through local prediction-error learning. In N. Miyake, D. Peebles, & R. P. Cooper (Eds.), Proceedings of the 34th annual conference of the cognitive science society (pp. 2529-2534). Austin, TX: Cognitive Science Society.
• Danks, D. (2009). The psychology of causal perception and reasoning. In H. Beebee, C. Hitchcock, & P. Menzies (Eds.), Oxford handbook of causation (pp. 447-470). Oxford: Oxford University Press.
• Danks, D. (2007). Causal learning from observations and manipulations. In M. C. Lovett & P. Shah (Eds.), Thinking with data (pp. 359-388). New York: Lawrence Erlbaum Associates.
• Danks, D. (2007). Theory unification and graphical models in human categorization. In A. Gopnik & L. Schulz (Eds.), Causal learning: Psychology, philosophy, and computation (pp. 173-189). Oxford: Oxford University Press.
• Nichols, W, & Danks, D. (2007). Decision making using learned causal structures. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th annual meeting of the cognitive science society (pp. 1343-1348). Austin, TX: Cognitive Science Society.
• Zhu, H., & Danks, D. (2007). Task influences on category learning. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th annual meeting of the cognitive science society (pp. 1677-1682). Austin, TX: Cognitive Science Society.
• Danks, D. (2006). (Not) learning a complex (but learnable) category. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th annual meeting of the cognitive science society (pp. 1186-1191). Mahwah, NJ: Lawrence Erlbaum Associates.
• Danks, D., & Schwartz, S. (2006). Effects of causal strength on learning from biased sequences. In R. Sun & N. Miyake (Eds.), Proceedings of the 28th annual meeting of the cognitive science society (pp. 1180-1185). Mahwah, NJ: Lawrence Erlbaum Associates.
• Danks, D., & Schwartz, S. (2005). Causal learning from biased sequences. In B. G. Bara, L. Barsalou, & M. Bucciarelli (Eds.), Proceedings of the 27th annual meeting of the cognitive science society (pp. 542-547). Mahwah, NJ: Lawrence Erlbaum Associates.
• Danks, D. (2004). Constraint-based human causal learning. In M. Lovett, C. Schunn, C. Lebiere, & P. Munro (Eds.), Proceedings of the 6th international conference on cognitive modeling (ICCM-2004) (pp. 342-343). Mahwah, NJ: Lawrence Erlbaum Associates.
• Danks, D. (2004). Psychological theories of categorization as probabilistic models. Technical report CMU-PHIL-157. July 15, 2004.
• Gopnik, A., Glymour, C., Sobel, D. M., Schulz, L. E., Kushnir, T. & Danks, D. (2004). A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review, 111 (1), 3-32.
• Danks, D. (2003). Equilibria of the Rescorla-Wagner model. Journal of Mathematical Psychology, 47, 109-121.
  • Java applet implementing the equilibrium derivation procedure.
• Danks, D., Griffiths, T. L., & Tenenbaum, J. B. (2003). Dynamical causal learning. In S. Becker, S. Thrun, & K. Obermayer (Eds.), Advances in neural information processing systems 15 (pp. 67-74). Cambridge, MA: MIT Press.
• Kushnir, T., Gopnik, A., Schulz, L., & Danks, D. (2003). Inferring hidden causes. In R. Alterman & D. Kirsh (Eds.), Proceedings of the 25th annual meeting of the cognitive science society (pp. 699-703). Boston: Cognitive Science Society.