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Bounded Optimality and Rational Metareasoning
Samuel J Gershman · Falk Lieder · Tom Griffiths · Noah Goodman

Fri Dec 11 05:30 AM -- 03:30 PM (PST) @ 512 bf
Event URL: https://sites.google.com/site/boundedoptimalityworkshop/ »

Formal definitions of rationality are instrumental for understanding and designing intelligent systems. By specifying the optimal way to reason under the constraint of limited information, Bayesian rationality has enabled tremendous advances in machine learning and artificial intelligence together with deep insights into human cognition and brain function. Bounded optimality (Horvitz, 1989; Russell, & Wefald, 1991a) extends Bayesian rationality by taking into account two additional constraints: limited time and finite computational resources. Bounded optimality is a practical framework for designing the best AI system possible given the constraints of its limited-performance hardware (Russell & Subramanian, 1995), and provides a way to capture the time and resource-constraints on human cognition. To adaptively allocate their finite computational bounded agents may have to perform rational metareasoning (Russel, & Wefald, 1991b) which corresponds to topics like cognitive control and metacognition studied in cognitive neuroscience and psychology.

Current research in cognitive science is leveraging bounded optimality and rational metareasoning to understand how the human mind can achieve so much with so little computation (Gershman, Horvitz, & Tenenbaum, in press; Vul, Griffiths, Goodman, & Tenenbaum, 2014), to develop and constrain process models of cognition (Griffiths, Lieder, & Goodman, 2015; Lewis, Howes, & Singh, 2014), to reevaluate the evidence for human irrationality, and to rethink heuristics and biases (Lieder, Griffiths, & Goodman, 2013; Lieder, Plunkett, et al. 2015). Rational metareasoning and bounded optimality also have interesting connections to neuroscience including the top-down control of neural information processing (e.g., Shenhav, Botvinick, & Cohen, 2013) and neural coding (Gershman, & Wilson, 2007).

This workshop brings together computer scientists working on bounded optimality and metareasoning with psychologists and neuroscientists reverse-engineering the computational principles that make the human brain incredibly resource-efficient. The goal of this workshop is to synthesize these different perspectives on bounded optimality, to promote interdisciplinary interactions and cross-fertilization, and to identify directions for future research.


1. Horvitz, E. (1987). Reasoning about Beliefs and Actions under Computational Resource Constraints, Third Workshop on Uncertainty in Artificial Intelligence, Seattle, Washington. Association for Uncertainty and Artificial Intelligence. pp. 429-444.
2. Russell, S. J., & Wefald, E. H. (1991a). Do the Right Thing: Studies in Limited Rationality. Cambridge, MA: MIT Press.
3. Russell, S. J., & Wefald, E. H. (1991a). Principles of Metareasoning. Artificial Intelligence, 49, 361–395.
4. Russell, S. J., & Subramanian, D. (1995). Provably bounded-optimal agents. Journal of Artificial Intelligence Research, 2, 575-609.
5. Gershman, S.J., Horvitz, E.J., & Tenenbaum, J.B. (2015). Computational rationality: A converging paradigm for intelligence in brains, minds and machines. Science, 349, 273-278.
6. Vul, E., Goodman, N., Griffiths, T. L., & Tenenbaum, J. B. (2014). One and done? Optimal decisions from very few samples. Cognitive science, 38(4), 599-637.
7. Griffiths, T.L., Lieder, F., & Goodman, N.D. (2015). Rational use of cognitive resources: Levels of analysis between the computational and the algorithmic. Topics in Cognitive Science, 7(2), 217-229.
8. Lieder, F., Plunkett, D., Hamrick, J.B., Russell, S.J, Hay, N.J., & Griffiths, T.L. (2014). Algorithm Selection by Rational Metareasoning as a Model of Human Strategy Selection. Advances in Neural Information Processing Systems 27, pp. 2870-2878.
9. Lewis, R. L., Howes, A., & Singh, S. (2014). Computational Rationality: Linking Mechanism and Behavior Through Bounded Utility Maximization. Topics in cognitive science, 6(2), 279-311.
10. Lieder, F., Griffiths, T. L., & Goodman, N. D. (2013). Burn-in, bias, and the rationality of anchoring. Advances in Neural Information Processing Systems 26, 2690-2798.
11. Shenhav, A., Botvinick, M. M., & Cohen, J. D. (2013). The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron, 79(2), 217-240.
12. Gershman, S., & Wilson, R. (2010). The neural costs of optimal control. Advances in neural information processing systems 23, 712-720.

Author Information

Samuel J Gershman (Harvard University)
Falk Lieder (UC Berkeley)
Tom Griffiths (UC Berkeley)
Noah Goodman (Stanford University)

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