Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-10T05:05:11.053Z Has data issue: false hasContentIssue false
  • English
  • Français

Multiple conceptions of resource rationality

Published online by Cambridge University Press:  11 March 2020

Wei Ji Ma Open the ORCID record for Wei Ji Ma [Opens in a new window]  and
Michael Woodford
Wei Ji Ma
Affiliation:
New York University, New York, NY10003http://www.cns.nyu.edu/malab
Michael Woodford
Affiliation:
Department of Economics, Columbia University, New York, NY10027mw2230@columbia.eduhttp://blogs.cuit.columbia.edu/mw2230/
Get access Rights & Permissions [Opens in a new window]

Abstract

Resource rationality holds great promise as a unifying principle across theories in neuroscience, cognitive science, and economics. The target article clearly lays out this potential for unification. However, resource-rational models are more diverse and less easily unified than might appear from the target article. Here, we explore some of that diversity.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 43 , 2020 , e15
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

Barlow, H. B. (1961) Possible principles underlying the transformation of sensory messages. Sensory Communication 1:217–34.Google Scholar
Bays, P. M. (2014) Noise in neural populations accounts for errors in working memory. Journal of Neuroscience 34(10):3632–45.CrossRefGoogle ScholarPubMed
Blume, L. E. & Easley, D. (1984) Rational expectations equilibrium: An alternative approach. Journal of Economic Theory 34(1):116–29.CrossRefGoogle Scholar
Ganguli, D. & Simoncelli, E. P. (2014) Efficient sensory encoding and Bayesian inference with heterogeneous neural populations. Neural Computation 26(10):2103–34. doi:10.1162/NECO_a_00638.CrossRefGoogle ScholarPubMed
Laughlin, S. (1981) A simple coding procedure enhances a neuron's information capacity. Zeitschrift für Naturforschung C 36(9–10):910–12.CrossRefGoogle ScholarPubMed
Li, V., Castañon, S. H., Solomon, J. A., Vandormael, H. & Summerfield, C. (2017) Robust averaging protects decisions from noise in neural computations. PLoS Computational Biology 13(8):e1005723.CrossRefGoogle ScholarPubMed
Lieder, F. (2018) Beyond bounded rationality: Reverse-engineering and enhancing human intelligence (Doctoral dissertation). University of California, Berkeley.Google Scholar
Lieder, F., Griffiths, T. L., Huys, Q. J. M. & Goodman, N. D. (2018d) The anchoring bias reflects rational use of cognitive resources. Psychonomic Bulletin & Review 25(1):322–49. doi:10.3758/s13423-017-1286-8.CrossRefGoogle Scholar
Lieder, F., Hsu, M. & Griffiths, T. L. (2014) The high availability of extreme events serves resource-rational decision-making. in Proceedings of the Annual Meeting of the Cognitive Science Society. Cognitive Science Society.Google Scholar
Mackowiak, B. & Wiederholt, M. (2009) Optimal sticky prices under rational inattention. American Economic Review 99(3):769–803.CrossRefGoogle Scholar
Młynarski, W. F. & Hermundstad, A. M. (2018) Adaptive coding for dynamic sensory inference. Elife 7:e32055.CrossRefGoogle ScholarPubMed
Netzer, N. (2009) Evolution of time preferences and attitudes toward risk. American Economic Review 99(3):937–55.CrossRefGoogle Scholar
Olshausen, B. A. & Field, D. J. (1996) Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381(6583):607–09. doi:10.1038/381607a0.CrossRefGoogle ScholarPubMed
Park, I. M. & Pillow, J. W. (2017) Bayesian efficient coding. bioRxiv. Preprint. doi:10.1101/178418.Google Scholar
Robson, A. & Whitehead, L. A. (2016) Rapidly adaptive hedonic utility. Working paper. Simon Fraser University.Google Scholar
Robson, A. J. (2001) The biological basis of economic behavior. Journal of Economic Literature 39(1):11–33.CrossRefGoogle Scholar
Shaw, M. L. & Shaw, P. (1977) Optimal allocation of cognitive resources to spatial locations. Journal of Experimental Psychology: Human Perception and Performance 3(2):201.Google ScholarPubMed
Sims, C. A. (2003) Implications of rational inattention. Journal of Monetary Economics 50(3):665–90. doi:10.1016/S0304-3932(03)00029-1.CrossRefGoogle Scholar
Sims, C. R., Jacobs, R. A. & Knill, D. C. (2012) An ideal observer analysis of visual working memory. Psychological Review 119(4):807–30. doi:10.1037/a0029856.CrossRefGoogle ScholarPubMed
Smith, V. L. (2009) Rationality in economics. Cambridge Books.Google Scholar
Steiner, J. & Stewart, C. (2016) Perceiving prospects properly. American Economic Review 106(7):1601–31.Google Scholar
Stigler, G. J. (1961) The economics of information. Journal of Political Economy 69(3):213–25.CrossRefGoogle Scholar
Todorov, E. & Jordan, M. I. (2002) Optimal feedback control as a theory of motor coordination. Nature Neuroscience 5(11):1226.CrossRefGoogle ScholarPubMed
van den Berg, R. & Ma, W. J. (2018) A resource-rational theory of set size effects in human visual working memory. ELife 7:e34963.CrossRefGoogle ScholarPubMed
Vul, E., Goodman, N. D., Griffiths, T. L. & Tenenbaum, J. B. (2014) One and done? Optimal decisions from very few samples. Cognitive Science 38(4):599–637. doi:10.1111/cogs.12101.CrossRefGoogle ScholarPubMed
Wei, X.-X. & Stocker, A. A. (2015) A Bayesian observer model constrained by efficient coding can explain “anti-Bayesian” percepts. Nature Neuroscience 18(10):1509–17. doi:10.1038/nn.4105.CrossRefGoogle ScholarPubMed
Woodford, M. (2012) Prospect theory as efficient perceptual distortion. American Economic Review 102(3):41–46.CrossRefGoogle Scholar
Zaslavsky, N., Kemp, C., Regier, T. & Tishby, N. (2018) Efficient compression in color naming and its evolution. Proceedings of the National Academy of Sciences 115(31):7937–42. doi:10.1073/pnas.1800521115.CrossRefGoogle ScholarPubMed