I reconsider Bleichrodt, Pinto Prades and Wakker’s 2001 paper about eliciting utility measures from stated preference surveys. That paper pioneers a method that is now widely used in behavioural economics to correct individuals’ ‘biases’ and to recover their ‘true preferences’. However, BPW propose this method as way of dealing with inconsistent responses to stated preference surveys, in contrast to more recent applications which aim to help individuals to avoid supposed mistakes in their private choices. I argue that the concepts of (...) true preference and bias are empirically ungrounded, but that BPW’s approach can be interpreted as not invoking those concepts. By ‘regularising’ preferences revealed in actual choice, this approach constructs measures of individual welfare that are broadly aligned with actual preferences and consistent with normative standards of rationality that are appropriate for public decision-making. Public decision-makers’ normative judgements are made explicit, rather than being disguised as apparently empirical claims about true preferences. (shrink)

Behavioral economics and existentialism both present informative perspectives on human choice. We argue in this article that the dialogue between the two approaches can enrich the current debate about the normative implications of behavioral economics. While behavioral economics suggests that our capacity to choose is constrained by cognitive biases and environmental influences, existentialism emphasizes that we can treat ourselves as free and ‘becoming’ beings in spite of the many constraints we face. Acknowledging these two perspectives in the form of a (...) theoretical synthesis—which we propose to call existentialist behavioral economics—provides us with reasons why we should protect our choices ‘as our own’ and how doing so may be more difficult than we anticipate. It also provides a framework to analyze the threat of identity-shaping social and technological developments, such as preference-altering nudges and artificially intelligent prediction algorithms. (shrink)