computational rationality and bounded optimality

Our work on computational rationality and bounded optimality provides a way to model and explain human behavior by leveraging its adaptive nature. Computational rationality is based on the idea that behaviors are generated by cognitive mechanisms that are adapted to the structure of not only the environment, but also the mind and brain itself. We call the framework computational rationality to emphasize the incorporation of computational mechanism into the definition of rational action. This idea has been around in some form in psychology for many decades, but advances in our understanding of the processing constraints, advances in computational algorithms for adaptive control, and advances in raw computing power make it possible now to more fully explore its implications.

Computational rationality can be formulated precisely as an application of bounded optimality to the challenges of psychological theory. Bounded optimality was given an elegant formal definition by Stuart Russell and Devika Subramanian in 1995. The approach has strong ties to reinforcement learning, and our most recent computational modeling developments adopt the reinforcement learning approach to derive adaptive behavior.

This work is a collaboration with researchers at Birmingham (Andrew Howes), Michigan (Satinder Singh), and NASA Ames (Alonso Vera).

For more on this work, read the papers below, and visit Andrew Howes' website at Birmingham.

key overview publications

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Howes, A., Warren, P. A., Farmer, G., El-Deredy, W., and Lewis, R. L. (2016). Why contextual preference reversals maximize expected value. Psychological Review, 123(4):368-391. [ PDF ]

Lewis, R. L., Howes, A., and Singh, S. (2014). Computational rationality: Linking mechanism and behavior through utility maximization. Topics in Cognitive Science, 6(2):279-311. [ PDF ]

Lewis, R. L., Shvartsman, M., and Singh, S. (2013). The adaptive nature of eye-movements in linguistic tasks: How payoff and architecture shape speed-accuracy tradeoffs. Topics in Cognitive Science, 5(3):583-610. [ PDF ]

Bratman, J., Shvartsman, M., Lewis, R. L., and Singh, S. (2010). A new approach to exploring language emergence as boundedly optimal control in the face of environmental and cognitive constraints. In Salvucci, D. and Gunzelmann, G., editors, Proceedings of the 10th International Conference on Cognitive Modeling. To appear. [ PDF ]

Singh, S., Lewis, R. L., Barto, A. G., and Sorg, J. (2010). Instrinsically motivated reinforcement learning: An evolutionary perspective. IEEE Transactions on Autonomous Mental Development. [ PDF ]

Howes, A., Lewis, R. L., and Vera, A. H. (2009). Rational adaptation under task and processing constraints: Implications for testing theories of cognition and action. Psychological Review, 116(4):717-751. [ PDF ]

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other relevant publications

Howes, A., Chen, X., Acharya, A., and Lewis, R. L. (2018). Interaction as an emergent property of a partially observable markov decision process. In Oulasvirta, A., Kristensson, P. O., Bi, X., and Howes, A., editors, Computational Interaction, pages 287-310. Oxford University Press. [ PDF ]

Acharya, A., Chen, X., Myers, C. W., Lewis, R. L., and Howes, A. (2017). Human Visual Search as a Deep Reinforcement Learning Solution to a POMDP. In Proceedings of the Annual Conference of the Cognitive Science Society. [ DOI ]

Howes, A., Duggan, G. B., Kalidindi, K., Tseng, Y.-C., and Lewis, R. L. (2015). Predicting short-term remembering as boundedly optimal strategy choice. Cognitive Science, pages 1-32. [ PDF ]

Howes, A., Lewis, R. L., and Singh, S. (2014). Utility maximization and bounds on human information processing. Topics in Cognitive Science, 6(2):198-203. [ PDF ]

Shvartsman, M., Lewis, R. L., and Singh, S. (2014). Computationally rational saccadic control: An explanation of spillover effects based on sampling from noisy perception and memory. In Demberg, V. and O'Donnell, T. J., editors, Proceedings of the 5th Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2014), Baltimore, MD. Association for Computational Linguistics. Best Student Paper Award. [ PDF ]

Feary, M., Billman, D., Chen, X., Howes, A., Lewis, R. L., Sherry, L., and Singh, S. (2013). Linking context to evaluation in the design of safety critical interfaces. In Proceedings of Human-Computer Interaction International. [ PDF ]

Lewis, R. L. (2010). The strong minimalist thesis and bounded optimality. Unpublished manuscript. [ PDF ]

Sorg, J., Singh, S., and Lewis, R. L. (2010). Internal rewards mitigate agent boundedness. In International Conference on Machine Learning, Haifa, Israel. [ PDF ]

Singh, S., Lewis, R. L., and Barto, A. G. (2009). Where do rewards come from? In Proceedings of the Annual Conference of the Cognitive Science Society, pages 2601-2606, Amsterdam. [ PDF ]

Smith, M. R., Lewis, R. L., Howes, A., Chu, A., Green, C., and Vera, A. (2008). More than 8,192 ways to skin a cat: Modeling behavior in multidimensional strategy spaces. In Love, B. C., McRae, K., and Sloutsky, V. M., editors, Proceedings of the 30th Annual Conference of the Cognitive Science Society, pages 1441-1446, Austin, TX. [ PDF ]

Chu, A., Lewis, R. L., and Howes, A. (2007). Evaluating the performance of optimizing constraint satisfaction techniques for cognitive constraint modeling. In Lewis, R., Polk, T., and .Laird, J., editors, The Proceedings of the 8th International Conference on Cognitive Modeling, pages 26-31, Ann Arbor, Michigan. Psychology Press/Taylor & Francis. [ PDF ]

Howes, A., Lewis, R. L., and Vera, A. (2007). Bounding rational analysis: Constraints on asymptotic performance. In Gray, W. D., editor, Integrated Models of Cognitive Systems. Oxford University Press, New York. [ PDF ]

Eng, K., Lewis, R. L., Tollinger, I., Chu, A., and Howes, A. (2006). Generating automated predictions of behavior strategically adapted to specific performance objectives. In Proceedings of the Computer-Human Interaction Conference, CHI 2006. Best paper nomination: top 5 per cent of submissions. [ PDF ]

Howes, A., Lewis, R. L., Vera, A., and Richardson, J. (2005). Information-requirements grammar: A theory of the structure of competence for interaction. In Proceedings of the Cognitive Science Society, Stresa, Italy. [ PDF ]

Tollinger, I., Lewis, R. L., McCurdy, M., Tollinger, P., Vera, A., Howes, A., and Pelton, L. (2005). Supporting efficient development of cognitive models at multiple skill levels: Exploring recent advances in constraint-based modeling. In Proceedings of the Computer-Human Interaction Conference, Portland, Ore. Best paper nomination: top 5 per cent of submissions. [ PDF ]

Vera, A. H., Howes, A., Lewis, R. L., Tollinger, I., Eng, K., and Richardson, J. (2005a). A constraint-based approach to understanding the composition of skill. In Proceedings of the Human-Computer Interaction 2005 Symposium,, Las Vegas.

Vera, A. H., Tollinger, I., Eng, K., Lewis, R. L., and Howes, A. (2005b). Architectural building blocks as the locus of adaptive behavior selection. In Proceedings of the Cognitive Science Society, Stresa, Italy. [ PDF ]

Howes, A., Vera, A., Lewis, R. L., and McCurdy, M. (2004). Cognitive constraint modeling: A formal approach to supporting reasoning about behavior. In Proceedings of the Cognitive Science Society, Chicago. [ PDF ]

Lewis, R. L., Vera, A., and Howes, A. (2004). A constraint-based approach to understanding the composition of skill. In Proceedings of the International Conference on Cognitive Modeling. [ PDF ]

Vera, A., Howes, A., McCurdy, M., and Lewis, R. L. (2004). A constraint-satisfaction approach to predicting skilled interactive cognition. In Proceedings of the Computer-Human Interaction Conferece CHI-2004, Vienna, Austria. [ PDF ]

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