Mel Win Khaw
Duke Institute for Brain Sciences
308 Research Drive
Durham, NC 27710
Institutional Affiliation: Duke University
NBER Working Papers and Publications
|August 2018||Cognitive Imprecision and Small-Stakes Risk Aversion|
with , : w24978
Observed choices between risky lotteries are difficult to reconcile with expected utility maximization, both because subjects appear to be too risk averse with regard to small gambles for this to be explained by diminishing marginal utility of wealth, as stressed by Rabin (2000), and because subjects' responses involve a random element. We propose a unified explanation for both anomalies, similar to the explanation given for related phenomena in the case of perceptual judgments: they result from judgments based on imprecise (and noisy) mental representations of the decision situation. In this model, risk aversion results from a sort of perceptual bias—but one that represents an optimal decision rule, given the limitations of the mental representation of the situation. We propose a quantita...
|March 2017||Risk Aversion as a Perceptual Bias|
with , : w23294
The theory of expected utility maximization (EUM) explains risk aversion as due to diminishing marginal utility of wealth. However, observed choices between risky lotteries are difficult to reconcile with EUM: for example, in the laboratory, subjects' responses on individual trials involve a random element, and cannot be predicted purely from the terms offered; and subjects often appear to be too risk averse with regard to small gambles (while still accepting sufficiently favorable large gambles) to be consistent with any utility-of-wealth function. We propose a unified explanation for both anomalies, similar to the explanation given for related phenomena in the case of perceptual judgments: they result from judgments based on imprecise (and noisy) mental representation of the decision sit...
|December 2016||Discrete Adjustment to a Changing Environment: Experimental Evidence|
with , : w22978
We conduct a laboratory experiment to shed light on the cognitive limitations that may affect the way decision makers respond to changes in their economic environment. The subjects solve a tracking problem: they estimate the probability of a binary event, which changes stochastically. The subjects observe draws and indicate their draw-by-draw estimate. Our subjects depart from the optimal Bayesian benchmark in systematic ways, but these deviations are not simply the result of some boundedly rational, but deterministic rule. Rather, there is a random element in the subjects' response to any given history of evidence. Moreover, subjects adjust their forecast in discrete jumps rather than after each new ring draw, even though there are no explicit adjustment costs. They adjust by both large a...
Published: Mel Win Khaw & Luminita Stevens & Michael Woodford, 2017. "Discrete Adjustment to a Changing Environment: Experimental Evidence," Journal of Monetary Economics, . citation courtesy of