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 study this both theoretically and empirically.

Emily Diana is an Assistant Professor in the Operations Research group at CMU’s Tepper School of Business. She received her Ph. D. in Statistics and Data Science from the Wharton School of the University of Pennsylvania, where she was advised by Michael Kearns and Aaron Roth. Her research focuses on the intersection of ethical algorithm design and socially responsible machine learning, and she is a co-principal investigator of the AI & Philosophy Lab along with Alexander Williams Tolbert. She is a recipient of the 2024 FORC Best Paper Award and the 2022 Wharton School's J. Parker Memorial Bursk Prize for Excellence in Research, and she has been recognized as both a Rising Star in EECS by MIT and a Future Leader in Data Science by the University of Michigan.