On the functional form of temporal discounting: An optimized adaptive test
By Daniel Cavagnaro (California State University at Fullerton)
Abstract: The tendency to discount the value of future rewards has become one of the best-studied constructs in the behavioral sciences. Although hyperbolic discounting remains the dominant quantitative characterization of this phenomenon, a variety of models have been proposed and consensus around the one that most accurately describes behavior has been elusive. To help bring some clarity to this issue, we propose an Adaptive Design Optimization (ADO) method for fitting and comparing models of temporal discounting. We test the method in simulation and compare its performance to several non-adaptive benchmarks, and then conduct an ADO experiment with human subjects, aimed at discriminating among six popular models of temporal discounting. Rather than supporting a single underlying model, our results show that each model is inadequate in some way to describe the full range of behavior exhibited across subjects. The precision of results provided by ADO further identify critical properties of models, which are mandatory to describe temporal discounting broadly.