Home>Publications>Econometrica>A New Representation of Preferences over "Certain x Uncertain" Consumption Pairs: The "Ordinal Certainty Equivalent" Hypothesis
For problems involving choices over "certain x uncertain" consumption pairs, it is almost universally assumed that the decision maker's preferences can be represented by an expected TPC (two-period cardinal) utility function. In this paper, we present an alternative representation of preferences, referred to as the "ordinal certainty equivalent" hypothesis, which we argue (i) is at least as intuitive as the expected utility hypothesis, (ii) includes the corresponding TPC representation as a special case with the set of cases not expressible in the latter format being both large and important, and (iii) is based on a more sensible hypothesis concerning the connection between "risk" and "time" preferences.
MLA
Selden, Larry. “A New Representation of Preferences over "Certain x Uncertain" Consumption Pairs: The "Ordinal Certainty Equivalent" Hypothesis.” Econometrica, vol. 46, .no 5, Econometric Society, 1978, pp. 1045-1060, https://www.jstor.org/stable/1911435
Chicago
Selden, Larry. “A New Representation of Preferences over "Certain x Uncertain" Consumption Pairs: The "Ordinal Certainty Equivalent" Hypothesis.” Econometrica, 46, .no 5, (Econometric Society: 1978), 1045-1060. https://www.jstor.org/stable/1911435
APA
Selden, L. (1978). A New Representation of Preferences over "Certain x Uncertain" Consumption Pairs: The "Ordinal Certainty Equivalent" Hypothesis. Econometrica, 46(5), 1045-1060. https://www.jstor.org/stable/1911435
We are deeply saddened by the passing of Kate Ho, the John L. Weinberg Professor of Economics and Business Policy at Princeton University and a Fellow of the Econometric Society. Kate was a brilliant IO economist and scholar whose impact on the profession will resonate for many years to come.
By clicking the "Accept" button or continuing to browse our site, you agree to first-party and session-only cookies being stored on your device. Cookies are used to optimize your experience and anonymously analyze website performance and traffic.