2026-07-22T13:49:42Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:23132 2025-10-02T12:34:47Z ddc:004 open_access

Pessimism Traps and Algorithmic Interventions Blum, Avrim Diana, Emily Ravichandran, Kavya Tolbert, Alexander Pessimism trap opinion dynamics algorithmic interventions subsidy decision-making 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Avrim Blum and Emily Diana and Kavya Ravichandran and Alexander Tolbert 2025 Is Part Of LIPIcs, Volume 329, 6th Symposium on Foundations of Responsible Computing (FORC 2025) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.FORC.2025.5 urn:nbn:de:0030-drops-231321 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FORC.2025.5 eng https://creativecommons.org/licenses/by/4.0/legalcode