Toward an Attentional Model of Time Discounting
This paper proposes a new theory of time discounting. We assume that when decision makers evaluate a sequence of future rewards, they allocate more attention to large rewards and less attention to small rewards. Moreover, when attention is distributed over a longer time horizon, each period receives less attention. The reductions in attention lead to greater discounting of reward values, and the resulting discount factors follow a distribution similar to the multinomial logit function. We characterize such discount factors using two approaches: an axiomatic analysis based on the optimal discounting framework, and a neuro-computational mechanism based on a simple spiking neural network. The theory explains a wide range of anomalies, including the hidden-zero effect, S-shaped value function, intertemporal correlation aversion, concentration bias, and inconsistent planning. Also, it identifies new determinants for several well-documented phenomena, such as the common difference effect and risk aversion over time lotteries.