Speed, Accuracy, and Complexity
SSRN Electronic Journal(2024)
摘要
This paper re-examines the validity of using response time to infer problem
complexity. It revisits a canonical Wald model of optimal stopping, taking
signal-to-noise ratio as a measure of problem complexity. While choice quality
is monotone in problem complexity, expected stopping time is inverse
U-shaped. Indeed decisions are fast in both very simple and very complex
problems: in simple problems it is quick to understand which alternative is
best, while in complex problems it would be too costly – an insight which
extends to general costly information acquisition models. This non-monotonicity
also underlies an ambiguous relationship between response time and ability,
whereby higher ability entails slower decisions in very complex problems, but
faster decisions in simple problems. Finally, this paper proposes a new method
to correctly infer problem complexity based on the finding that choices react
more to changes in incentives in more complex problems.
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