We have known for a long time that people’s risky choices depart systematically from expected utility theory,and also from related models like prospect theory. But it is still common to use expected utility theory orprospect theory to estimate parameters like risk aversion from sets of risky choices. We have also known fora long time that when parameters are estimated, a systematic departure between the model and the datacauses biased parameter estimates. Here we show how the bias in parameter estimation interacts with the setof choices presented to participants. We find that estimates of risk aversion vary greatly between choice setseven though no real differences in risk aversion exist. We find parameters do not generalise at all betweenchoice sets, even when the sets are random draws from a master choice set.

In a paper published in Management Science in 2015, Stewart, Reimers, and Harris (SRH) demonstrated that shapes of utility and probability weighting functions could be manipulated by adjusting the distributions of outcomes and probabilities on offer as predicted by the theory of decision by sampling. So marked were these effects that, at face value, they profoundly challenge standard interpretations of preference theoretic models in which such functions are supposed to reflect stable properties of individual risk preferences. Motivated by this challenge, we report an extensive replication exercise based on a series of experiments conducted as a quasi-adversarial collaboration across different labs and involving researchers from both economics and psychology. We replicate the SRH effect across multiple experiments involving changes in many design features; importantly, however, we find that the effect is also present in designs modified so that decision by sampling predicts no effect. Although those results depend on model-based inferences, an alternative analysis using a model-free comparison approach finds no evidence of patterns akin to the SRH effect. On the basis of simulation exercises, we demonstrate that the SRH effect may be a consequence of misspecification biases arising in parameter recovery exercises that fit imperfectly specified choice models to experimental data. Overall, our analysis casts the SRH effect in an entirely new light.

People have a stronger preference for options encountered earlier or later in a sequence than for options in the middle of the sequence. To account for these primacy and recency effects, Mantonakis et al. (2009) sketched a sequential updating mechanism, the pairwise-competition model. We propose a formal instantiation of the model and, using computer simulations, examine how the sizes of the predicted primacy and recency effects are affected by (a) variability in the quality of the options; (b) the number of options presented (sequence length); (c) the level of choice inertia (i.e., the tendency to stick with the current favorite); and (d) whether choice inertia dynamically increases over the sequence. We find that recency effects are reduced and primacy effects are increased with variability in quality as compared to without, and that this holds regardless of sequence length. A sizeable primacy effect occurs only with relatively short sequences or rather high levels of choice inertia. Dynamic inertia increases primacy effects and reduces recency effects, and the impact increases with higher inertia levels. We relate these results to empirical findings and derive novel predictions from the model.