Measuring Decreasing and Increasing Impatience

Many studies show that time preference data from experiments and surveys are related to field behavior. Time preference measures in these studies typically depend simultaneously on utility curvature, the level of impatience, and the change in the level of impatience. Thus, these studies do not allow one to establish which of these three components drive(s) the field behavior of interest. Of these components, the change in the level of impatience is theoretically thought to be the main driver of time inconsistencies and self-control problems. To test this theoretical presumption, one has to measure the change in the level of impatience independently from utilities and levels of impatience. This paper introduces a measure of the degree of decreasing impatience, the DI-index. It measures the change of impatience independently from the level of impatience and independently from utility. It can also be used to test various discounting models. An experiment finds no correlation between the degree of decreasing impatience and self-reported self-control problems in daily life, suggesting that changing impatience is not the sole driver of self-control problems

Eliciting Discount Functions when Baseline Consumption changes over Time

Many empirical studies on intertemporal choice report preference reversals in the sense that a preference between a small reward to be received soon and a larger reward to be received later reverses as both rewards are equally delayed. Such preference reversals are commonly interpreted as contradicting constant discounting. We show that this interpretation is correct only if baseline consumption to which the outcomes are added, remains constant over time. The difficulty with measuring discounting when baseline consumption changes over time, is that delaying an outcome has two simultaneous effects: (1) due to the change in baseline consumption, it changes the increase in utility from receiving the outcome, and (2) it changes the discount factor applied to this increase in utility. In order to draw conclusions about discounting one needs to disentangle these two effects which seems impossible at first sight (Noor, 2009). Yet, in this paper we propose a way to disentangle the two effects

Weighted Temporal Utility

__Abstract__ We propose a novel utility representation for preferences over risky timed outcomes. The weighted temporal utility model generalizes many well known utility functions for intertemporal decision making under risk. A decision maker with a weighted temporal utility function can have time consistent yet non-stationary preferences or stationary yet time inconsistent preferences. Thus, our model can explain the empirical evidence in Halevy (2012) which is at odds with standard models of intertemporal choice that assume non-linear time perception to be the sole driver of non-stationary and time-inconsistent behavior. We also propose a non-parametric approach to elicit a weighted temporal utility function

Discount Functions for Fitting Individual Data

The commonly used hyperbolic and quasi-hyperbolic discount functions imply decreasing impatience, which is the prevailing empirical phenomenon in intertemporal choice, in particular for aggregate behavior. At the individual level there is much variation, however, and there will always be some individuals who exhibit increasing impatience. Hence, to fit data at the individual level, new discount functions are needed. This paper introduces such functions, with constant absolute (CADI) or constant relative (CRDI) decreasing impatience. These functions can accommodate any degree of decreasing or increasing impatience, which makes them sufficiently flexible for analyses at the individual level. The CADI and CRDI discount functions are the analogs of the well known CARA and CRRA utility functions for decision under risk

Improving one’s choices by putting oneself in others’ shoes – An experimental analysis

This paper investigates how letting people predict others’ choices under risk affects subsequent own choices. We find an improvement of strong rationality (risk neutrality) for losses in own choices, but no such improvement for gains. There is no improvement of weak rationality (avoiding preference reversals). Overall, risk aversion in own choices increases. Conversely, for the effects of own choices on predicting for others, the risk aversion predicted in others’ choices is reduced if preceded by own choices, for both gains and losses. Remarkably, we find a new probability matching paradox at the group level. Relative to preceding studies on the effects of predicting others’ choices, we added real incentives, pure framing effects, and simplicity of stimuli. Our stimuli were maximally targeted towards our research questions