Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-17T16:37:46.608Z Has data issue: false hasContentIssue false
  • English
  • Français

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 15213olivola@cmu.eduhttps://sites.google.com/site/chrisolivola/
Nick Chater
Affiliation:
Behavioural Science Group, Warwick Business School, University of Warwick, Coventry CV4 7AL, United KingdomNick.Chater@wbs.ac.ukhttp://www.wbs.ac.uk/about/person/nick-chater/
Get access Rights & Permissions [Opens in a new window]

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.

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

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

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