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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.
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This content will become publicly available on June 3, 2026
Pessimism Traps and Algorithmic Interventions
In this paper, we relate the philosophical literature on pessimism traps to information cascades, a formal model derived from the economics and mathematics literature. A pessimism trap is a social pattern in which individuals in a community, in situations of uncertainty, copy the sub-optimal actions of others, despite their individual beliefs. This maps nicely onto the concept of an information cascade, which involves a sequence of agents making a decision between two alternatives, with a private signal of the superior alternative and a public history of others' actions. Key results from the economics literature show that information cascades occur with probability one in many contexts, and depending on the strength of the signal, populations can fall into the incorrect cascade very easily and quickly. Once formed, in the absence of external perturbation, a cascade cannot be broken - therefore, we derive an intervention that can be used to nudge a population from an incorrect to a correct cascade and, importantly, maintain the cascade once the subsidy is discontinued. We extend this to the case of multiple communities, each of which might have a different optimal action, and a government providing subsidies that cannot discriminate between communities and does not know which action is optimal for each. We study this both theoretically and empirically.
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- PAR ID:
- 10645946
- Editor(s):
- Bun, Mark
- Publisher / Repository:
- Schloss Dagstuhl – Leibniz-Zentrum für Informatik
- Date Published:
- Volume:
- 329
- ISSN:
- 1868-8969
- Page Range / eLocation ID:
- 5:1-5:19
- Subject(s) / Keyword(s):
- Pessimism trap opinion dynamics algorithmic interventions subsidy decision-making Applied computing → Economics
- Format(s):
- Medium: X Size: 19 pages; 808428 bytes Other: application/pdf
- Size(s):
- 19 pages 808428 bytes
- Location:
- 6th Symposium on Foundations of Responsible Computing (FORC 2025)
- Sponsoring Org:
- National Science Foundation
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