Hostname: page-component-cd9895bd7-gvvz8 Total loading time: 0 Render date: 2024-12-25T18:05:08.630Z Has data issue: false hasContentIssue false
  • English
  • Français

Cognitively bounded rational analyses and the crucial role of theories of subjective utility

Published online by Cambridge University Press:  11 March 2020

Richard L. Lewis Open the ORCID record for Richard L. Lewis [Opens in a new window]  and
Andrew Howes Open the ORCID record for Andrew Howes [Opens in a new window]
Richard L. Lewis
Affiliation:
Department of Psychology and Weinberg Institute for Cognitive Science, University of Michigan Ann Arbor, [email protected]://www-personal.umich.edu/~rickl/
Andrew Howes
Affiliation:
School of Computer Science, University of Birmingham, BirminghamB15 2TT, United Kingdom. [email protected]://www.cs.bham.ac.uk/~howesa/
Get access Rights & Permissions [Opens in a new window]

Abstract

We agree that combining rational analysis with cognitive bounds, what we previously introduced as Cognitively Bounded Rational Analysis, is a promising and under-used methodology in psychology. We further situate the framework in the literature, and highlight the important issue of a theory of subjective utility, which is not addressed sufficiently clearly in the framework or related previous work.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 43 , 2020 , e14
Check for updates
Copyright
Copyright © Cambridge University Press 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Howes, A., Lewis, R. L. & Vera, A. H. (2009) Rational adaptation under task and processing constraints: Implications for testing theories of cognition and action. Psychological Review 116(4):717–51. doi:10.1037/a0017187CrossRefGoogle ScholarPubMed
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. doi:10.1111/tops.12086.CrossRefGoogle ScholarPubMed
Newell, A. (1990) Unified theories of cognition. Harvard University Press.Google Scholar
Russell, S. J. & Subramanian, D. (1995) Provably bounded-optimal agents. Journal of Artificial Intelligence Research 2(1):575–609. doi: 10.1613/jair.133.CrossRefGoogle Scholar
Shenhav, A., Musslick, S., Lieder, F., Kool, W., Griffiths, T. L., Cohen, J. D. & Botvinick, M. M. (2017) Toward a rational and mechanistic account of mental effort. Annual Review of Neuroscience 40:99–124. doi:10.1146/annurev-neuro-072116-031526.CrossRefGoogle Scholar
Singh, S., Lewis, R. L., Barto, A. G. & Sorg, J. (2010) Intrinsically motivated reinforcement learning: An evolutionary perspective. IEEE Transactions on Autonomous Mental Development 2(2):70–82.CrossRefGoogle Scholar
Sorg, J., Singh, S. & Lewis, R. L. (2010) Internal rewards mitigate agent boundedness. In: Proceedings of the International Conference on Machine Learning (ICML), Haifa, Israel.Google Scholar