When normative and descriptive diverge: how to bridge the difference

Abstract

Revealed preferences are not consistent. Many anomalies have been found in different contexts. This finding leads to a divergence between normative and descriptive analyses. There are several ways of facing this problem. In this paper we argue in favour of debiasing observed choices in such a way that the "true" preferences are discovered. Our procedure is based on quantitative corrections derived from assuming the descriptive validity of prospect theory and the normative validity of expected utility theory. Those corrective formulas were first applied by Bleichrodt et al. (Manag Sci 47:1498-1514. We explain here how such formulas can be used to avoid inefficient allocation of health care resources. This approach shares the philosophy of libertarian paternalism (LP). However, it reduces some of the potential problems of LP: the definition of error (and the need to nudge people) is more clear and objective. In this sense, it reduces the chances that the regulator tries to nudge people toward behaviour based on her preferences rather than those of the people she is nudging.

Rabin's paradox for health outcomes

“…Fortunately, by modelling preferences (over health outcomes) within a reference‐dependent framework such as prospect theory (Tversky & Kahneman, ), RP can be easily resolved. The increasing attention for such reference‐dependent frameworks in health economics (Abellan‐Perpiñan, Bleichrodt, & Pinto‐Prades, ; Attema et al, ; Lipman, Brouwer, & Attema, ; Pinto‐Prades & Abellan‐Perpiñan, ) seeking more accurate descriptive theories is supported by our findings. Although these reference‐dependent theories may be general enough to capture the strong risk aversion demonstrated by a small part of our sample, further investigation to understand how we decide about health under risk is clearly still needed.…”

A QALY loss is a QALY loss is a QALY loss: a note on independence of loss aversion from health states

“…However, as our exploratory analyses of within-subject heterogeneity demonstrated, individuals’ loss aversion and utility curvature may depend on the health state used during elicitation. This heterogeneity at the individual level may be problematic for approaches using averages, like median-optimized parameters (e.g., [ 27 ]). When aiming to address PT biases for QALYs [ 28 ], such as loss aversion, at the individual level, our data would suggest that assuming such median loss aversion parameters may misrepresent individuals’ actual preferences and trade-offs.…”

The Corrective Approach: Policy Implications of Recent Developments in QALY Measurement Based on Prospect Theory

“…A frequently applied approach is to pre-emptively assume a certain degree of loss aversion, utility curvature, and probability weighting in all respondents. 35 In this type of work, average parameters elicited in earlier work (eg, loss aversion coefficients of 2.25) are applied to each individual. Nevertheless, typically large differences in loss aversion, utility curvature, and probability weighting are observed between individuals; that is, not everyone is equally loss averse or weighs probabilities the same way.…”