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Numerical magnitude evaluation as a foundation for decision making

Published online by Cambridge University Press:  27 July 2017

Christopher Y. Olivola  and
Nick Chater
Christopher Y. Olivola
Affiliation:
Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213 olivola@cmu.edu https://sites.google.com/site/chrisolivola/
Nick Chater
Affiliation:
Behavioural Science Group, Warwick Business School, University of Warwick, Coventry CV4 7AL, United Kingdom Nick.Chater@wbs.ac.uk http://www.wbs.ac.uk/about/person/nick-chater/
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Abstract

The evaluation of magnitudes serves as a foundation not only for numerical and mathematical cognition, but also for decision making. Recent theoretical developments and empirical studies have linked numerical magnitude evaluation to a wide variety of core phenomena in decision making and challenge the idea that preferences are driven by an innate, universal, and stable sense of number or value.

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Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 40 , 2017 , e183
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Copyright © Cambridge University Press 2017 

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References

Frederick, S., Loewenstein, G. & O'Donoghue, T. (2002) Time discounting and time preference: A critical review. Journal of Economic Literature 40(2):351–401.Google Scholar
Kahneman, D. & Tversky, A. (1979) Prospect theory: An analysis of decision under risk. Econometrica 47(2):263–92.Google Scholar
Kim, B. K. & Zauberman, G. (2009) Perception of anticipatory time in temporal discounting. Journal of Neuroscience, Psychology, and Economics 2(2):91–101.Google Scholar
Kornienko, T. (2013) Nature's measuring tape: A cognitive basis for adaptive utility. Working paper, University of Edinburgh.Google Scholar
Olivola, C. Y. (2015) The cognitive psychology of sensitivity to human fatalities: Implications for life-saving policies. Policy Insights from the Behavioral and Brain Sciences 2(1):141–46.Google Scholar
Olivola, C. Y. & Chater, N. (2017) Decision by sampling: Connecting preferences to real-world regularities. In: Big data in cognitive science, ed. Jones, M. N.. pp. 294–319. Psychology Press, Taylor and Francis.Google Scholar
Olivola, C. Y., Rheinberger, C. M. & Hammitt, J. K. (2017) Sensitivity to fatalities from frequent small-scale deadly events: A decision by sampling account. Working paper, Carnegie Mellon University.Google Scholar
Olivola, C. Y. & Sagara, N. (2009) Distributions of observed death tolls govern sensitivity to human fatalities. Proceedings of the National Academy of Sciences of the United States of America 106(52):22151–56.CrossRefGoogle ScholarPubMed
Olivola, C. Y. & Wang, S. W. (2016) Patience auctions: The impact of time vs. money bidding on elicited discount rates. Experimental Economics 19(4):864–85.Google Scholar
Prelec, D. (1998) The probability weighting function. Econometrica 66(3):497–527.CrossRefGoogle Scholar
Read, D. (2004) Intertemporal choice. In: Blackwell handbook of judgment and decision making, ed. Koehler, D. J. & Harvey, N., pp. 424–43. Blackwell.Google Scholar
Schley, D. R. & Peters, E. (2014) Assessing “economic value”: Symbolic-number mappings predict risky and riskless valuations. Psychological Science 25(3):753–61.Google Scholar
Slovic, P. (2007) “If I look at the mass I will never act”: Psychic numbing and genocide. Judgment and Decision Making 2(2):79–95.CrossRefGoogle Scholar
Stewart, N. (2009) Decision by sampling: The role of the decision environment in risky choice. Quarterly Journal of Experimental Psychology 62(6):1041–62.Google Scholar
Stewart, N., Chater, N. & Brown, G. D. A. (2006) Decision by sampling. Cognitive Psychology 53(1):1–26.Google Scholar
Stewart, N. & Simpson, K. (2008) A decision-by-sampling account of decision under risk. In: The probabilistic mind: Prospects for Bayesian cognitive science, ed. Chater, N. & Oaksford, M., pp. 261–76. Oxford University Press.Google Scholar
Tversky, A. & Kahneman, D. (1992) Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty 5(4):297–323.Google Scholar
Ungemach, C., Stewart, N. & Reimers, S. (2011) How incidental values from the environment affect decisions about money, risk, and delay. Psychological Science 22(2):253–60.Google Scholar
Walasek, L. & Stewart, N. (2015) How to make loss aversion disappear and reverse: Tests of the decision by sampling origin of loss aversion. Journal of Experimental Psychology: General 144(1):7–11.Google Scholar
Wu, G. & Gonzalez, R. (1996) Curvature of the probability weighting function. Management Science 42(12):1676–90.Google Scholar
Zauberman, G., Kim, B. K., Malkoc, S. A. & Bettman, J. R. (2009) Discounting time and time discounting: Subjective time perception and intertemporal preferences. Journal of Marketing Research 46(4):543–56.Google Scholar