Using Response Times to Infer Others’ Private Information: An Application to Information Cascades
Management Science · 2022 · Volume 68 · Issue 4
Abstract
The standard assumption in social learning environments is that agents learn from others through choice outcomes. We argue that in many settings, agents can also infer information from others’ response times (RT), which can increase efficiency. To investigate this, we conduct a standard information cascade experiment and find that RTs do contain information that is not revealed by choice outcomes alone. When RTs are observable, subjects extract this private information and are more likely to break from incorrect cascades. Our results suggest that in environments where RTs are publicly available, the information structure may be richer than previously thought. This paper was accepted by Yan Chen, decision analysis.
Keywords
information cascade, herding, response time, reaction time, drift diffusion model, sequential sampling model, information aggregation, social learning
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