In this paper, we study how temporal discounting determines sequential decision making. We analyze decision time distribution induced by all sequential information acquisition strategies that 1) implements a target information structure, 2) satisfies a constrainton flow informativeness of signal. The main result is that decisive Poisson signal creates the most dispersed decision time distribution (in mean-preserving spread order ), and the pure accumulation of information creates the least dispersed. This implies that for a decision maker with convex discount function, decisive Poisson signal is the optimal learning strategy .
I consider the sequential implementation of a target information structure. I characterize the set of decision time distributions induced by all signal processes that satisfy a per-period learning capacity constraint. I find that all decision time distributions have the same expectation, and the maximal and minimal elements by mean-preserving spread order are deterministic distribution and exponential distribution. The result implies that when time preference is risk loving (e.g. standard or hyperbolic discounting), Poisson signal is optimal since it induces the most risky exponential decision time distribution. When time preference is risk neutral (e.g. constant delay cost), all signal processes are equally optimal .
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In this paper, we study how temporal discounting determines sequential decision making. We analyze decision time distribution
I consider the sequential implementation of a target information structure. I characterize the set of decision time distributions
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sequential information acquisition strategies that 1) implements a target information structure, 2) satisfies a constrainton flow informativeness of signal. The main result is that decisive Poisson signal creates the most dispersed decision time distribution (in
signal processes that satisfy a per-period learning capacity constraint. I find that all decision time distributions have the same expectation, and the maximal and minimal elements by
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), and the pure accumulation of information creates the least dispersed. This implies that for a decision maker with convex discount function, decisive
are deterministic distribution and exponential distribution. The result implies that when time preference is risk loving (e.g. standard or hyperbolic discounting),
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the optimal learning strategy
optimal since it induces the most risky exponential decision time distribution. When time preference is risk neutral (e.g. constant delay cost), all signal processes are equally optimal